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Aziro (formerly MSys Technologies) 2019 Tech Predictions: Smart Storage, Cloud’s Bull Run, Ubiquitous DevOps, and Glass-Box AI

2019 brings us to the second-last leg of this decade. From the last few years, IT professionals have been propagating rhetoric. They state that the technology landscape is seeing a revolutionary change. But, most of the “REVOLUTIONARY” changes, has, over the time lost their gullibility. Thanks to the awe-inspiring technologies like AI, Robotics, and upcoming 5G networks most tech pundits consider this decade to be a game changer in the technology sector.As we make headway into 2019, the internet is bombarded with numerous tech prophecies. Aziro (formerly MSys Technologies) presents to you the 2019 tech predictions based on our Storage, Cloud, DevOps and digital transformation expertise.1. Software Defined Storage (SDS)Definitely, 2019 looks promising for Software Defined Storage. It’ll be driven by changes in Autonomous Storage, Object Storage, Self-Managed DRaaS and NVMes. But, SDS will also be required to push the envelope to acclimatize and evolve. Let’s understand why so.1.1 Autonomous Storage to Garner MomentumBacked by users’ demand, we’ll witness the growth of self-healing storage in 2019. Here, Artificial Intelligence powered by intelligent algorithms will play a pivotal role. Consequently, companies will strive to ensure uninterrupted application performance, round the clock.1.2 Self-Managed Disaster Recovery as a Service (DRaaS) will be ProminentSelf-Managed DRaaS reduces human interference and proactively recovers business-critical data. It then duplicates the data in the Cloud. This brings relief during an unforeseen event. Ultimately, it cuts costs. In 2019, this’ll strike chords with enterprises, globally, and we’ll witness DRaaS gaining prominence.1.3 The Pendulum will Swing Back to Object Storage as a Service (STaaS)Object Storage makes a perfect case for cost-effective storage. Its flat structure creates a scale-out architecture and induces Cloud compatibility. It also assigns unique Metadata and ID for each object within storage. This accelerates the data retrieval and recovery process. Thus, in 2019, we expect companies to embrace Object Storage to support their Big data needs.1.4 NMVes Adoption to Register TractionIn 2019, Software Defined Storage will accelerate the adoption rate of NVMes. It rubs off glitches associated with traditional storage to ensure smooth data migration while adopting NVMes. With SDS, enterprises need not worry about the ‘Rip and Replace’ hardware procedure. We’ll see vendors design storage platforms that append to NVMes protocol. For 2019, NMVes growth will mostly be led by FC-NVME and NVMe-oF.2. Hyperconverged Infrastructure (HCI)In 2019, HCI will remain the trump card to create a multi-layer infrastructure with centralized management. We’ll see more companies utilize HCI to deploy applications quickly. This’ll circle around a policy-based and data-centric architecture.3. Hybridconverged Infrastructure will Mark its FootprintHybridconverged Infrastructure (HCI.2) comes with all the features of its big brother – Hyperconverged Infrastructure (HCI.1). But, one extended functionality makes the latter smarter. Unlike HCI.1, it allows connecting with an external host. This’ll help HCI.2 mark its footprint in 2019.4. VirtualizationIn 2019, Virtualization’s growth will be centered around Software Defined Data Centers and Containers.4.1 ContainersContainer technology is ace in the hole to deliver promises of multi-cloud – cost efficacy, operational simplicity, and team productivity. Per IDC, 76 percent of users’ leverage containers for mission-critical applications.4.1.1 Persistent Storage will be a Key ConcernIn 2019, Containers’ users will envision a cloud-ready persistent storage platform with flash arrays. They’ll expect their storage service providers to implement synchronous mirroring, CDP – continuous data protection and auto-tiering.4.1.2 Kubernetes Explosion is ImminentThe upcoming Kubernetes version is rumored to include a pre-defined configuration template. If true, it’ll enable an easier Kubernetes deployment and use. This year, we are also expecting a higher number of Kubernetes and containers synchronization. This’ll make Kubernetes’ security a burgeoning concern. So, in 2019, we should expect stringent security protocols around Kubernetes deployment. It can be multi-step authentication or encryption at the cluster level.4.1.3 Istio to Ease Kubernetes Deployment HeadacheIstio is an open source service mesh. It addresses the Microservices’ application deployment challenges like failure recovery, load balancing, rate limiting, A/B testing, and canary testing. In 2019, companies might combine Istio and Kubernetes. This can facilitate a smooth Container orchestration, resulting in an effortless application and data migration.4.2 Software Defined Data CentersMore companies will embark on their journey to Multi-Cloud and Hybrid-Cloud. They’ll expect a seamless migration of existing applications to a heterogeneous Cloud environment. As a result, SDDC will undergo a strategic bent to accommodate the new Cloud requirements.In 2019, companies will start cobbling DevOps and SDDC. The pursuit of DevOps in SDDC will be to instigate a revamp of COBIT and ITIL practice. Frankly, without wielding DevOps, cloud-based SDDC will remain in a vacuum.5. DevOpsIn 2019, companies will implement a programmatic DevOps approach to accelerate the development and deployment of software products. Per this survey, DevOps enabled 46x code deployment. It also skyrocketed the deploy lead time by 2556x. This year, AI/ML, Automation, and FaaS will orchestrate changes to DevOps.5.1 DevOps Practice Will Experience a Spur with AI/MLIn 2019, AI/ML centric applications will experience an upsurge. Data science teams will leverage DevOps to unify complex operations across the application lifecycle. They’ll also look to automate the workflow pipeline – to rebuild, retest and redeploy, concurrently.5.2 DevOps will Add Value to Functions as a Service (FaaS)Functions as a Service aims to achieve serverless architecture. It leads to a hassle-free application development without perturbing companies to handle the monolithic REST server. It is like a panacea moment for developers.Hitherto, FaaS hasn’t achieved a full-fledged status. Although FaaS is inherently scalable, selecting wrong user cases will increase the bills. Thus, in 2019, we’ll see companies leveraging DevOps to fathom productive user cases and bring down costs drastically.5.3 Automation will be the Mainstream in DevOpsManual DevOps is time-consuming, less efficient, and error-prone. As a result, in 2019, CI/CD automation will become central in the DevOps practice. Consequently, Infrastructure as a Code to be in the driving seat.6. Cloud’s Bull Run to ContinueIn 2019, organizations will reimagine the use of Cloud. There will be a new class of ‘born-in-cloud’ start-ups, that will extract more value by intelligent Cloud operations. This will be centered around Multi-Cloud, Cloud Interoperability, and High Performance Computing. More companies will look to establish a Cloud Center of Excellence (CoE). Per RightScale survey, 57 percent of enterprises already have a Cloud Center of Excellence.6.1 Companies will Drift from “One-Cloud Approach.”In 2018, companies realized that having a ‘One-Cloud Approach’ encumbers their competitiveness. In 2019, Cloud leadership teams will bask upon the Hybrid-Cloud Architecture. Hybrid-Cloud will be the new normal within Cloud Computing in 2019.6.2 Cloud Interoperability will be a Major ConcernIn 2019, companies will start addressing the issues of interoperability by standardizing Cloud architecture. The use of the Application Programming Interface (APIs) will also accelerate. APIs will be the key to instill the capability of language neutrality, which augments system portability.6.3 High Performance Computing (HPC) will Get its Place on CloudIndustries such as Finance, Deep Learning, Semiconductors or Genomics are facing the brunt of competition. They’ll envision to deliver high-end compute-intensive applications with high performance. To entice such industries, Cloud providers will start imparting HPC capabilities in their platform. We’ll also witness large scale automation in Cloud.7. Artificial IntelligenceFor 2019 AI/ML will come out of the research and development model to be widely implemented in organizations. Customer engagements, infrastructure optimization, and Glass-Box AI, will be in the forefront.7.1 AI to Revive Customer EngagementsBusinesses (startups or enterprise) will leverage AI/ML to enable a rich end-user experience. Per Adobe, enterprises using AI will more than double in 2019. Tech and non-tech companies, alike, will strive to offer personalized services leveraging Natural Language Processing. The focus will remain to create a cognitive customer persona to generate tangible business impacts.7.2 AI for Infrastructure OptimizationIn 2019, there will a spur in the development of AI embedded monitoring tools. This’ll help companies to create a nimble infrastructure to respond to the changing workload. With such AI-driven machines, they’ll aim to cut down the infrastructure latency, infuse robustness in applications, enhance performances, and amplify outputs.7.3 Glass-Box AI will be crucial in Retail, Finance, and HealthcareThis is where Explainable AI will play its role. Glass-Box AI will create key customers’ insights with underlying methods, errors or biases. In this way, retailers don’t necessarily follow every suggestion. They can sort out responses that fit rights in that present scenario. The bottom-line will be to avoid customer altercations and bring out fairness in the process.

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AI automation Autonomous Storage Cloud Interoperability Containers devops DRaaS Embedded AI FaaS Glass-Box AI

What is Observability? Not Just Logs, Metrics and Traces

The complexity and scale of dynamic systems put increasing pressure on IT teams to monitor and address problems within multi-cloud environments. DevOps and SRE teams seek better visibility into increasingly complex computing systems and infrastructure. What is the significance of observable data? Tell me the importance of this technology in your business strategy? In IT and cloud computing, observability means measuring systems and their current states using data generated, like logs, metrics, and traces.The Foundation of ObservabilityAt its core, observability represents a paradigm shift in understanding and managing complex systems. Unlike traditional monitoring, which focuses solely on assessing whether a system is functioning within predefined parameters, observability seeks to illuminate the inner workings of a system by analyzing its external outputs. By harnessing data from metrics, logs, traces, and events, observability goes beyond surface-level indicators to provide a holistic view of system behavior.Observability transcends the limitations of traditional monitoring by offering insights into not just the ‘what’ but also the ‘why’ and ‘how’ of system behavior. It enables engineers to better understand system interactions, dependencies, and emergent behaviors, especially during complex and unpredictable scenarios. This comprehensive view empowers organizations to identify and address issues proactively, optimize system performance, and enhance reliability and resilience.Monitoring vs. Observability: What’s the Difference Between Monitoring and Observability?Is observation a good idea? I guess not. Observability and monitoring are related but different concepts. While monitoring primarily focuses on collecting and displaying data about a system’s health and performance, observability extends this paradigm by incorporating additional layers of depth and context. Monitoring typically involves tracking predefined metrics and KPIs to assess the state of a system in real time. This approach provides valuable insights into the overall health and performance of the system, allowing for proactive identification of issues and trends.Source: MiddlewareUnderstanding System Behavior at a Deeper LevelIn contrast, observability delves deeper into understanding system behavior’s underlying causes and implications. It goes beyond surface-level metrics and encompasses a holistic view of the system’s internal state, including its interactions, dependencies, and emergent behaviors. Observability aims to answer the ‘what’ – i.e., what is happening within the system – and the ‘why’ and ‘how’ – why certain events occur and propagate through the system.Flexible and Adaptive Data Collection ApproachOne of the critical distinctions between observability and monitoring lies in their respective analysis approaches. While monitoring typically relies on predefined metrics and thresholds, observability adopts a more flexible and adaptive approach. It embraces a broader range of data sources, including metrics, logs, traces, and events, to comprehensively view system behavior.Emphasis on Context and CausalityMoreover, observability emphasizes context and causality, seeking to uncover the symptoms of an issue and its underlying mechanisms. By correlating data from disparate sources and tracing the flow of events across distributed systems, observability enables engineers to gain a deeper understanding of system behavior and identify subtle patterns and anomalies that may indicate potential issues or opportunities for optimization.The Key Components of Observability: Telemetry Types and the Core Analysis LoopObservability involves collecting and comparing information sources to better understand a system’s behavior. This is done through the core analysis loop, where analysis and optimization are continuously triggered, allowing teams to monitor problems and optimize system performance efficiently. Observability tools collect data from varying components of the system, including logs and metric data traces, to get a better picture of the software system.Source: IODMetricsMetrics form the bedrock of observability, offering quantitative data about various aspects of a system, such as response times, error rates, and throughput. Performance metrics provide insights into the efficiency and responsiveness of a system, allowing engineers to identify performance bottlenecks and optimize resource allocation. Resource utilization metrics, on the other hand, shed light on the usage patterns of system resources, including CPU, memory, and disk space, enabling proactive capacity planning and resource optimization strategies.LogsLogs are crucial in observability by providing a detailed narrative of system events, including errors, warnings, and informational messages. They offer context to metrics by capturing the events leading up to an issue, providing valuable insights into the ‘how’ and ‘why’ behind system behavior. During troubleshooting, logs serve as a vital tool for root cause analysis, allowing engineers to trace the execution path of a request, identify strange behaviors, and pinpoint the underlying causes of issues.With the advent of centralized logging platforms and log management solutions, organizations can aggregate and analyze logs from across their infrastructure, facilitating efficient log search, correlation, and analysis and enabling proactive detection and resolution of issues.TracesTraces, or distributed traces, provide visibility into the flow of a request as it traverses various components of a distributed system. Tracing allows for reconstructing a transaction’s journey across multiple services and infrastructure layers, from initiation to completion. By capturing timing information and contextual metadata at each step of the request’s path, traces enable engineers to identify bottlenecks, latency issues, and other abnormalities across the entire system. Distributed tracing systems, such as Jaeger and Zipkin, facilitate the collection, aggregation, and visualization of trace data, empowering organizations to gain insights into the end-to-end performance of their distributed systems.The Significance of ObservabilityRapid TroubleshootingObservability equips engineering teams with the tools to identify and resolve issues swiftly. Correlating metrics, logs, and traces offer a holistic view of system health, enabling quicker root cause analysis and issue resolution. Advanced anomaly detection algorithms leverage machine learning techniques to automatically identify abnormal patterns and deviations from expected behavior, enabling teams to promptly prioritize and address critical issues.Improved Decision-MakingInformed decision-making relies on a comprehensive understanding of system behavior. Observability empowers teams to make data-driven decisions by providing real-time insights into performance, user interactions, and overall system health. Advanced analytics capabilities, such as predictive modeling and trend analysis, enable teams to forecast future system behavior based on historical data trends, facilitating proactive decision-making and resource allocation.Furthermore, observability platforms often offer customizable dashboards and reporting tools, allowing stakeholders to visualize critical metrics and KPIs in real time and facilitating informed decision-making at all levels of the organization.Proactive System OptimizationObservability is not just reactive; it’s a proactive approach to system management. By continuously analyzing metrics, logs, and traces, teams can identify potential bottlenecks or performance degradation before they escalate, allowing for preemptive optimization. Advanced anomaly detection algorithms and predictive analytics techniques enable teams to anticipate future resource demands and adjust infrastructure configurations accordingly, optimizing performance and scalability.Additionally, observability platforms often provide automated remediation capabilities, allowing teams to implement proactive optimization strategies, such as auto-scaling and workload rebalancing, in response to detected anomalies or performance degradation.Implementing ObservabilityDiscover the essential steps and strategies for implementing Observability effectively, enabling organizations to gain deeper insights into system performance and behavior.1. InstrumentationInstrumentation involves embedding code within applications to collect the necessary data for observability. This includes strategically integrating libraries for metrics, strategically logging statements, and incorporating tracking mechanisms. Effective instrumentation lays the foundation for robust observability.It also entails defining relevant metrics and key performance indicators (KPIs) that align with business objectives, ensuring that the collected data provides actionable insights into system health and performance.2. Centralized Logging and Monitoring SystemsOrganizations often invest in centralized logging and monitoring systems to harness the power of observability. These platforms aggregate data from various sources, providing a unified interface for analyzing metrics, logs, and traces. Prominent tools like Prometheus, Grafana, ELK stack, and Jaeger have become staples in the observability toolkit.Leveraging these tools allows organizations to consolidate observability data, streamline analysis workflows, and gain actionable insights into system behavior, enabling proactive issue detection and resolution.3. Collaboration and CultureObservability is not just a set of tools; it’s a cultural mindset. Fostering collaboration among development, operations, and other relevant teams is essential. A culture that values transparency, information sharing, and continuous improvement is conducive to implementing and maximizing observability. This involves breaking down silos between teams, encouraging open communication, and promoting cross-functional collaboration on observability initiatives.Additionally, organizations should invest in training and education programs to ensure team members have the necessary skills and knowledge to leverage observability tools and practices in their day-to-day activities effectively.Observability ToolsObservability tools offer real-time visibility into system metrics and logs and facilitate proactive identification and resolution of issues before they impact end-users. By leveraging advanced analytics and machine learning algorithms, observability tools empower organizations to detect anomalies, optimize resource utilization, and enhance system resilience. Additionally, observability tools enable collaboration among development, operations, and other cross-functional teams, fostering a culture of continuous improvement and innovation.1. Metrics Monitoring ToolsMetrics monitoring tools are instrumental in capturing and analyzing performance metrics and critical system health indicators. These tools collect data from different sources, such as servers, databases, and applications, and present it in a centralized dashboard for easy visualization and analysis.Famous tools include Prometheus, Grafana, and Datadog, which offer robust capabilities for tracking and analyzing metrics in real-time.2. Log Management SolutionsLog management solutions are essential for capturing and analyzing log data from applications and infrastructure components. These tools aggregate logs from multiple sources, such as servers, containers, and network devices, and provide powerful search and filtering capabilities for identifying and troubleshooting issues.Elasticsearch, Logstash, and Kibana (ELK stack) are widely used log management solutions that offer scalable and flexible log aggregation, analysis, and visualization capabilities.3. Distributed Tracing PlatformsDistributed tracing platforms enable organizations to trace the flow of requests across distributed systems and microservices architectures. These tools capture timing information and contextual metadata at each step of a request’s journey, allowing teams to identify performance bottlenecks, latency issues, and dependencies between services.Popular distributed tracing platforms include Jaeger, Zipkin, and OpenTelemetry, which provide comprehensive tracing capabilities for understanding the end-to-end performance of distributed applications.4. Synthetic Monitoring ServicesSynthetic monitoring services simulate user interactions with applications and services to detect and address performance issues before they impact end-users proactively. These tools simulate user scenarios, such as page loads, form submissions, and API calls, and monitor critical endpoints’ response times and availability.Synthetic monitoring services like Pingdom, Uptime Robot, and New Relic Synthetics offer comprehensive capabilities for monitoring application performance and uptime from different geographic locations.5. Anomaly Detection SystemsAnomaly detection systems use machine learning algorithms to identify deviations from regular application and infrastructure metrics behavior. These tools analyze historical data patterns and automatically detect anomalies, such as spikes in CPU utilization, unusual network traffic patterns, or abnormal error rates. Anomaly detection systems help organizations proactively identify and address issues, minimize downtime, and optimize system performance.Tools like Amazon CloudWatch Anomaly Detection, Datadog AI-powered anomaly detection, and InfluxDB anomaly detection offer advanced capabilities for detecting and alerting anomalies in real time.Future Trends in ObservabilityAs technology continues to evolve, so does the field of observability. Emerging trends indicate a shift towards more automation, artificial intelligence, and machine learning to derive meaningful insights from vast observability data. Integrating observability into DevOps practices and the evolution of standards and protocols will shape the future landscape of system observability.1. Automation in ObservabilityManual analysis and interpretation become increasingly challenging with the exponential data growth generated by modern software systems. Automation emerges as a critical trend in observability, leveraging machine learning algorithms and artificial intelligence (AI) to automate data collection, analysis, and response. By automating routine tasks and leveraging AI-driven anomaly detection, organizations can streamline operations, detect issues more quickly, and derive actionable insights from vast observability data.2. Integration with DevOps PracticesObservability is increasingly integral to DevOps practices, bridging the gap between development and operations teams. By embedding observability into the software development lifecycle, organizations can ensure that observability considerations are built into applications from the outset. This integration facilitates continuous feedback loops, enabling teams to identify and address issues early in the development process, iterate rapidly, and deliver higher-quality software with improved reliability and performance.3. Evolution of Standards and ProtocolsAs the field of observability matures, there is a growing emphasis on standardization and interoperability. Industry initiatives such as OpenTelemetry aim to establish common standards and protocols for observability, enabling seamless integration across disparate systems and platforms. Organizations can facilitate data exchange and interoperability between observability tools and platforms by adopting standardized data formats, protocols, and APIs. This empowers engineers to gain a unified view of system behavior regardless of the underlying technologies.4. Real-time Monitoring and AlertingWith modern software systems’ increasing complexity and dynamism, real-time monitoring and alerting becomes paramount. Emerging trends in observability focus on enabling real-time visibility into system health and performance, coupled with intelligent alerting mechanisms that notify stakeholders of potential issues or anomalies as they occur. By providing timely and actionable insights, real-time monitoring and alerting help organizations proactively manage and mitigate risks, minimize downtime, and ensure optimal system performance.5. Cloud-native Observability SolutionsAs organizations increasingly embrace cloud-native architectures and microservices-based applications, observability solutions tailored to the cloud-native ecosystem are gaining traction. These solutions offer native integration with cloud platforms and container orchestration systems, providing visibility into distributed and ephemeral workloads. By leveraging cloud-native observability solutions, organizations can gain insights into the performance, scalability, and reliability of their cloud-native applications. This enables them to optimize resource utilization, troubleshoot issues, and ensure a seamless user experience.6. Continuous Evolution and InnovationObservability is a dynamic and evolving field driven by ongoing technological advancements and changing business requirements. As organizations continue to innovate and adopt new technologies, the landscape of observability will continue to evolve. Continuous investment in research and development, coupled with a commitment to embracing emerging trends and best practices, will be essential for organizations seeking to harness the full potential of observability to drive business success in an increasingly digital world.Wrapping Up the Observability JourneyIn the intricate tapestry of modern software systems, observability emerges as a guiding light, illuminating the path to efficient operations and continuous improvement. What is observability? It’s not merely a buzzword or a checklist item; it’s a holistic approach to understanding, managing, and optimizing complex systems. From metrics and logs to traces and beyond, observability empowers engineers to navigate the intricate labyrinth of software development with confidence and agility.As we continue to push the boundaries of technological innovation, observability remains an indispensable ally, helping us unveil the mysteries of system behavior and ensuring the resilience of the digital ecosystems we build and maintain.FAQs1. What is observability in simple terms?Just as simple as it sounds, observation means measuring the internal state of a process using a system’s outputs. A system may be viewed as “observable” only with input data, i.e., sensors.2. What is observability in DevOps?DevOps observability helps teams understand how systems work throughout the entire development phase. This helps improve customer experiences and reduces the time spent identifying defects and preventing them from happening in their systems.3. How can I resolve system issues with Observability data?Observabilities are crucial in SRE and DevOps since they ensure the reliable operation of the system. Observability is vital because it allows the user to get deeper insights about system behavior, allowing proactive monitoring of troubleshooters and improving system performance. For the developer, operational teams, or site reliability engineers, steps are required to detect, analyze, or resolve problems within any software system using observability data.4. What is the role of real-user monitoring (RUM) and synthetic testing?Moreover, organizations can use real-user monitoring in real time to gain real visibility of their user experience. Teams may monitor these experiences with synthetic monitoring or recording a particular event. This functionality extends Telemetry by including data on APIs and third-party services, errors that occur in the browsers, user demographics, and application performance from the user perspective.5. How do I Implement Observability?A combination of various tools, open-source software, and commercial solutions is essential to achieve Observable System Development. Implementing observability requires integrating four key components, each serving a distinct purpose in ensuring a comprehensive view of system behavior and performance. Metrics Monitoring, Log Management, Distributed Tracing, Anomaly Detection.

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What is the Importance of NVMe and NVMe-oF in Modern Storage?

What is NVMe?NVMe is the new protocol which is known as Non Volatile Memory Express. Let’s have a brief idea about volatile and non-volatile memory before moving ahead to the details of the topic. Volatile memory is a type of memory where the data is lost, in-case of power failure. RAM is a good example of volatile memory. In contrast to volatile memory, non-volatile memory is the type of memory which will retain the data in case of a power failure as there is a battery providing the back-up. Flash is non-volatile memory and of two types- nand and nor flash memory.NVM Express (NVMe)NVM Express® (NVMe™) is an optimized, high-performance scalable host controller interface designed to address the needs of Enterprise and Client systems that utilize PCI Express®-based solid-state storage. (https://nvmexpress.org/wp-content/uploads/NVMe_Overview.pdf). Solid-state drives (SSD) are nothing but storage made by two key components- viz. nand flash chips and flash controller. SSDs are faster than traditional hard drives as these drives have no spinning component.Need for PCIE based NVMe SSDsAll hot data should be available on flash. Now-a-days data is like gold mine, faster processing of data can have a great impact on business decisions. For achieving this kind of speed, flash based storage devices and high speed storage protocol like NVMe is required. NVMe has made its mark as a high-performance protocol and is expanding due to an industry wide adoption by storage vendors. PCIe based NVMe SSD drives achieve that speed because NVMe supports 64k commands per queue and 64 queues whereas the SATA devices support 32 and SaaS devices support up to 256 commands per queue. Hence concluding that NVMe leverages all the potential of flash-based SSDs. This technology has emerged for reducing the gap between fast CPU and slow storage. Datacenter, Gaming and entertainment industries will have great performance benefits through NVMe. Peripheral Component Interconnect Express (PCIE) are also evolving in order to support NVMe.Diagram of PCIE NVMe storage IO stack[/capntion]DMA and RDMAA. DMADirect Memory Access provides faster data transfer rate by reducing the CPU cycle of fetching, decoding, and executing the IO. It enables faster processing as the CPU can be utilized in other operations while data transfer is going on. You need a DMA controller for carrying out the operation.Above is the simple example of CPU cycleB. RDMALet’s try to split the term and understand what Remote Direct Memory Access widely means. It is a direct memory access to a remote host’s DMA from a separate computer’s memory without involving the operating system. This increases the throughput low latency networking as this uses zero-copy, which implies that the data transfer is done without the networking stack, data is received or sent directly to the buffers, without being copied between the network stack. In addition, the RDMA bypasses the kernel, data that is transferred from user space. It is used in many markets; some of them are-HPC – High performance computing, BIG data, cloud, FSI (Finance services and insurance). For using RDMA, one needs a network adapter that supports RDMA. It should supports Ethernet or Infiniband as link layer protocol.NVMe over fabrics (NVMe-oF)NVMe over fabrics is a technology, which enables the extension of distance by which PCIE NVMe based hosts and storage drivers can be connected. NVMe-oF standard supports multiple storage networking fabrics for example Ethernet, Infiniband and FC.For Ethernet, RoCE v2 and iWARP are ideal RDMA fabrics. Mellanox is the leading manufacturer of RoCE based network adapter where as Qlogic has FC-NVMe ready adapters. The desired difference in latency is 10 microseconds between a distant NVMe device and a device sitting locally. The NVMe-oF solution provides the NVMe storage on a high-speed storage network to multiple hosts increasing the throughput and with low latency. Most of the areas in NVMe over fabrics are the same as the local NVMe protocol- for instance I/O and administrative commands, NVMe namespaces, registers and properties, power states, reservations, etc.There are some differences in identifier, discovery, queuing and data transfers. Disaggregation of storage from compute, higher utilization of SSDs and leveraging the CPU to its fullest capacity other than transferring the data only, are key benefits for a cloud infrastructure. NVMe over fabric works on a message based system where the NVMe commands or responses are encapsulated into capsules.ConclusionThe NVMe PCIe SSDs and over fabrics will drive future storage industry and it will add value to the business by helping the cloud infrastructure and big data analytics achieve fast access to Data.Lead storage vendors have shown their interest in these areas and some of them have already shipped their products with this specification and some of them are in-design phase of their products.

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How Disk Access Path Has Evolved In The Last Decade

AbstractThis blog article discusses the evolution of disk access path from bygone years to currently trending Non Volatile Memory Express (NVMe). All engineers should be well aware of the steep latency increase from a few nanoseconds for internal cache hits to a few hundred nanoseconds for RAM, and eventually all the way up to a few hundred milliseconds to seconds for mechanical hard disk access. Latency of external disk access was a severe bottleneck that limited eventual performance, until recently.With the advent of solid state memory architectures like NAND/NOR Flash, the access times and power requirement were dramatically cut down. This brought even storage to the well-known Moore’s performance curve. Newer SSD hard disks were made that replaced the storage media from traditional, mechanical, rotational magnetic media to solid state memories but kept the disk access protocol the same for backward compatibility.Soon the realization dawned that with solid state storage media, the bottleneck still existed with these traditional disk access protocols.In this blog article, let us see how computer designs and disk access protocols have evolved over the years to give us today’s high bandwidth, low-latency disk IO path, called NVMe.Evolution of computer design to achieve current high performance, low-latency disk accessLet us rollback a decade earlier and look at how computers were designed. A computer would contain a CPU with two external chipsets, Northbridge and Southbridge. Please see Figure 1 for such a design.A Northbridge chipset, also called a Memory controller Hub (MCH), provides high-speed access to external memory and graphics controller, directly connecting to the CPU. And then the Southbridge, also called an IO hub, would connect all the low speed IO peripherals. It was a given that spinning hard disks are low-performance components and connected to Southbridge.Figure 1: Computer design with Northbridge/Southbridge chipsetsFigure 2: Anatomy of Disk Access – source: SNIABut with each generation of CPU, faster processors appeared resulting in any external data access out of the CPU, to severely affect its performance because of ever-increasing IO delay. Larger caches helped to an extent, but it soon became obvious that spinning CPU to higher speeds every generation will definitely not get the best performance, unless the external disk delay path scales in comparison to the CPU performance. The wide gap between processor performance and disk performance is captured in Fig 2.As the first step towards addressing the high latency for external storage access, memory controller got integrated into the CPU directly; in other words, the Northbridge chipset got subsumed totally within the CPU. So, there was one fewer bridge for IO access to external disks. But still, hard disk access latency did really hurt the overall performance of the CPU. The capacity scale and data persistence with hard disks cannot be achieved just with RAMs, and so they were critical components that cannot be ignored. Figure 3 essentially captures this performance gap.Figure 3: Disk Access Performance Gap – Source: SNIAFigure 4: Typical SAS drive access pathThe computer industry did have another significant evolution in embracing serial protocols again for high speed interfaces. Storage access protocols changed serial (e.g., SAS), and computer buses followed suit (e.g., PCI Express).AHCI protocol standardized ATA disk access, SAS/FC drives took over SCSI, and serial protocol began to dominate. Each of these protocols had higher speeds and other networked storage features, but the drives were still mechanical. All of these storage protocols needed a dedicated Host Bus Controller (HBA) connected to the CPU local bus that translated requests from/to local CPU (over PCI/PCI-X/PCIe) to/from storage protocol (SAS/SATA/FC). As one could see in the Figure 4, a SAS disk drive could be reached only through a dedicated HBA (host bus adapters).Computer local buses, not to be left behind, followed serializing and came out with PCI Express. PCI Express protocol came into its own; although physically they are different from earlier parallel bus PCI/PCI-X based designs, software interfaces remained the same. Southbridge chipsets carried PCI Express, and there was mass adoption to PCI Express with added performance benefits. The high point of PCI Express was its integration directly into the CPU, thus totally avoiding any external bridge chipset for interfacing to hard disks. With PCI Express becoming the de facto high-speed peripheral interface directly out of the CPU, bandwidth and performance of external peripheral components could be scaled to match CPU directly.Another significant technology improvement delivered solid-state disks. Initial designs only tried to create a niche market for solid state drives. Backward compatibility of new SSDs was an absolute requirement making these SSDs carry the same disk access protocols as traditional hard disks, SAS/SATA, for instance. Initial SSD disks were expensive, with capacities limited to really challenge traditional hard disks. But with each generation, capacity and life (durability) were addressed. It became evident that solid-state disks were here to stay. Figure 5 captures a typical SSD with legacy disk access protocols like SAS/SATA. Now, the storage media became solid state and were no longer mechanical; hence, power requirements and latency got dramatically reduced. But inefficiencies that existed in the disk access protocol got exposed.Figure 5: Legacy IO Path with FlashFigure 6: PCIe SSD through IOHLet us pause a moment here to understand the inefficiencies that were mentioned. If the CPU were to perform a disk access, then driver software in the CPU submits requests to the device over PCIe. The requests are carried over PCIe as payloads and reach the HBA, which decodes the payloads and prepares the same request. Only this time, the request is signaled through another serial storage protocol (e.g., SAS). Eventually the request reaches the disk controller, which performs the operation on the storage media and responds. This response, now initiated in a storage protocol, is received by the HBA, which is again converted to PCIe to hand over the response to the CPU. The role of an HBA was seriously questioned in the whole topology.The full potential of the solid-state disks have yet not been realized because of limitations discussed earlier. Industry responded, removing all the intervening protocol conversions, by avoiding the HBA and legacy disk access protocols, but directly interfacing them over PCIe using proprietary protocols. Refer to Figure 6. Fusion IO PCIe SSD drives were one such successful product that changed the performance profile of disks forever.Finally, everyone could sense the unlimited performance available to the CPU, with solid state Flash storage on Moore’s curve, and the disk IO performance in microseconds from traditional milliseconds to seconds range. This was a moment of reckoning, and standardization had to happen for it to be in the mainstream. Thus was born NVMe. NVMe did have competitors initially through SCSI Express and SATA Express provided backward compatibility to existing AHCI based SATA disks. But NVMe did not have to carry any old baggage, its software stack is lean (though the software stack had to be written from scratch), it became abundantly clear that its advantages far outweighed the additional effort involved. And thus, the CPU vs disk performance curve, which was ever diverging, has been tamed for now. But we can rest assured and look forward to several other significant innovations in storage, networking and processor design to try taming the disk access latency beast completely.References:[1] Southbridge computing, https://en.wikipedia.org/wiki/Southbridge_(computing)[2] Northbridge computing,https://en.wikipedia.org/wiki/Northbridge_(computing)[3] Flash-Plan for the disruption, SNIA – Advancing Storage and InformationTechnology[4] A high performance driver ecosystem for NVM Express[5] NVM Express-Delivering Breakthrough PCIe SSD performance and scalabil-ity, Storage Developer Conference, SNIA 2012.[6] Stephen, Why Are PCIe SSDs So Fast?, http://blog.fosketts.net/2013/06/12/pcie-ssds-fast/

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What are the Top Storage Tech Trends for 2018 (infographic)

With technology changing at light speed, there are lots to hope for in terms of innovation for the year 2018. In the Storage industry too, new technology trends will have a significant impact as we move into the next year. Let’s take a look at what the year 2018 holds new for Storage, Virtualization, Networking and Cloud industry.

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Storage Trends 2018

What is Full Stack Observability? Understanding Its Importance in Today’s Digital Landscape

Ensuring application performance, reliability, and security has become paramount in the rapidly evolving digital landscape. As organizations invest in IT budgets to accelerate pandemic-era digital transformation, the need for full stack observability becomes increasingly relevant. This necessity has given rise to the concept of full stack observability. But what is full stack observability? In essence, it refers to the comprehensive monitoring and analysis of every component within an application’s stack—from the frontend user interface to the backend infrastructure and everything in between. This article delves into the critical aspects of full stack observability, its significance, and how it can be a game-changer for organizations.Defining Full Stack ObservabilityFull stack observability is an advanced approach that provides end-to-end visibility into the performance and health of an entire application stack. It encompasses monitoring all layers, including the application, database, server, network, and underlying infrastructure. By leveraging a blend of metrics, logs, and traces (often referred to as the three pillars of observability), organizations can gain a holistic view of their systems, enabling proactive detection and resolution of issues before they impact end users. A full stack observability solution ensures comprehensive monitoring and analysis of the entire application stack, facilitating real-time insights and shifting from a reactive to a proactive IT culture.Key Components of Full Stack ObservabilitySource: DynatraceMetrics are quantitative data points that track the performance of various system components over time. Common examples include CPU usage, memory consumption, and request latency. Metrics provide a quick snapshot of system health. They enable teams to monitor trends and detect anomalies that might indicate performance degradation. By analyzing metrics, organizations can make data-driven decisions to optimize system efficiency.Logs are detailed records of events that occur within an application. They are invaluable for debugging and troubleshooting specific issues. Logs can show errors, warnings, and informational messages. They offer contextual information that helps understand the sequence of events leading up to an issue. Proper log management can significantly reduce incidents’ mean time to resolution (MTTR).Tracing involves following a request’s journey through different parts of the system. It shows the interaction between components and helps identify bottlenecks or failures. Traces provide a granular view of requests propagating through various services and databases. This visibility is crucial for pinpointing where performance issues originate, enabling more effective troubleshooting and optimization efforts. Telemetry data is essential in providing in-depth insights into the performance and behavior of applications and infrastructure, especially in a distributed IT environment.The Importance of Full Stack Observability and End-to-End VisibilityUnderstanding what full stack observability entails is crucial, but recognizing its importance is even more vital. With the increasing complexity of modern applications, traditional monitoring tools often fall short of providing the necessary insights. Full stack observability helps correlate performance metrics with business outcomes, ensuring that every aspect of the tech stack contributes to the organization’s overall success. Here’s why full stack observability is indispensable in today’s digital age:Full stack observability provides a comprehensive view of your IT environment, from infrastructure to applications. It leverages data correlation to offer deeper insights, enabling proactive and predictive visibility across IT infrastructures and services. This holistic approach ensures that potential issues are identified and resolved before they impact end-users, maintaining the quality and reliability of software delivery.Enhanced Performance MonitoringModern applications use microservices architectures, distributed systems, and cloud-native technologies. Microservices architectures utilize application programming interfaces (APIs) to facilitate service communication. This complexity makes pinpointing performance issues challenging. Full stack observability allows teams to monitor the performance of every stack layer, ensuring that no part goes unchecked. For instance, slow database queries or inefficient code paths can be identified and optimized, improving overall application performance.Proactive Issue Resolution and Root Cause AnalysisOne of the primary benefits of full stack observability is its ability to detect potential issues before they escalate into major problems. By continuously monitoring metrics, logs, and traces, teams can spot anomalies and trends that indicate underlying issues. This proactive approach enables quicker resolution times, reducing downtime and improving user experience.Improved Collaboration and EfficiencyFull stack observability fosters collaboration among development, operations, and security teams. Aligning IT and business teams helps prioritize business goals and explore revenue-generating opportunities. With a unified view of the application stack, teams can collaborate more effectively to diagnose and resolve issues. Eliminating operational silos is crucial to improving cross-functional collaboration. This shared visibility breaks down silos and ensures that everyone is on the same page, leading to improved efficiency and faster incident response times.Implementing Full Stack ObservabilitySource: SimformOrganizations need to implement full stack observability to leverage the benefits fully. Here’s a step-by-step guide to get started:Step 1: Define Observability ObjectivesBefore diving into the technical implementation, it is crucial to define clear objectives. What do you want to achieve with full-stack observability? Common goals include reducing mean time to resolution (MTTR), improving application performance, and enhancing user experience. Having specific, measurable objectives will guide the implementation process.Step 2: Choose the Right ToolsSelecting the appropriate tools is critical for effective full stack observability. Traditional domain-centric monitoring tools often face strain on their capabilities, highlighting the need to minimize reliance on manual processes by using full-stack observability to empower teams and improve performance. Numerous tools specialize in different aspects of observability, such as Prometheus for metrics, ELK Stack for logs, and Jaeger for tracing. Many platforms offer integrated solutions that combine metrics, logs and traces into a single pane of glass, providing unified observability.Step 3: Instrument Your ApplicationsInstrumentation involves adding monitoring capabilities to your applications and infrastructure. This can be achieved through libraries, agents, or SDKs provided by observability tools. Ensure you instrument all stack layers, from the front to the back end, to capture comprehensive data.Step 4: Set Up Alerts and DashboardsConfiguring alerts is vital for proactive issue detection. Gathering and correlating system data is essential to provide fuller visibility and understanding. Define thresholds for key metrics and set up alerts to notify relevant teams when these thresholds are breached. Additionally, create dashboards that visualize the collected data, making monitoring and analyzing system health in real time easier.Step 5: Continuously ImproveFull stack observability is not a one-time effort. Continuously review and refine your observability practices based on the insights you gain. Update instrumentation regularly, revisit alert thresholds, and enhance dashboards to ensure they provide the most relevant information.Real-World Use Cases of Full Stack ObservabilityLet’s explore some real-world use cases to further understand full-stack observability and its impact. Automated root cause analysis, powered by AI and visual aids, is crucial in quickly identifying the source of issues, allowing teams to focus on higher-order tasks.E-commerce WebsitesE-commerce platforms experience high traffic volumes, especially during peak seasons like Black Friday. Full stack observability enables these platforms to monitor performance across the entire stack, from user interactions on the website to backend payment processing systems. By identifying and addressing performance bottlenecks, e-commerce companies can ensure a seamless customer shopping experience.Financial ServicesFinancial institutions rely heavily on the performance and reliability of their applications. Full stack observability helps these organizations adhere to strict regulatory requirements by providing detailed audit trails through logs and traces. Additionally, it aids in detecting fraudulent activities and minimizing downtime, which is critical for maintaining customer trust.Healthcare SystemsIn healthcare, application downtime can have severe consequences. Full stack observability empowers healthcare providers to monitor the performance and availability of critical systems, such as electronic health records (EHR) and telemedicine platforms. By ensuring the continuous operation of these systems, observability contributes to better patient care and outcomes.Challenges and Best PracticesWhile full stack observability offers numerous benefits, it also presents some challenges. IT teams are crucial in integrating runtime application security and querying telemetry data to ensure seamless observability. Here are common obstacles and best practices for addressing them:Data OverloadCollecting metrics, logs, and traces from every stack component can result in data overload. This data deluge can make it challenging for teams to parse through and find meaningful insights, potentially leading to missed critical issues or delays in identifying problems. Organizations should focus on collecting data that aligns with their specific observability objectives to manage this effectively. By prioritizing meaningful data, such as key performance indicators (KPIs) and business-critical metrics, teams can ensure they only monitor what truly matters.This approach reduces the volume of data and makes it easier to identify patterns and anomalies that may indicate underlying issues. In addition to prioritizing meaningful data, sampling techniques can significantly mitigate data overload. Sampling involves capturing a subset of data points representative of the overall dataset, reducing the total amount of data collected without sacrificing the ability to detect trends and outliers.Intelligent filtering further aids in managing data volume by automatically excluding redundant or non-essential information, allowing teams to concentrate on actionable insights. Combining these strategies with effective data aggregation and visualization tools can enhance the clarity and utility of the collected data, enabling quicker and more accurate decision-making.Tool IntegrationIntegrating multiple observability tools can be complex, mainly when dealing with a diverse technology stack that includes various programming languages, frameworks, and infrastructure components. The lack of interoperability between different tools can lead to fragmented data, making gaining a comprehensive view of the system’s health difficult. Organizations should choose observability tools that offer seamless integrations with other essential tools in their tech stack to address this complexity.Tools with built-in connectors and APIs can simplify the integration process, ensuring smooth data flow and reducing the manual effort required to correlate information from disparate sources. Moreover, opting for platforms that provide comprehensive observability solutions can further ease the burden of tool integration. These platforms typically combine metrics, logs, and traces into a unified interface, eliminating the need for multiple standalone tools.For instance, integrated observability platforms like Datadog, New Relic, and Splunk offer a single-pane-of-glass view, making monitoring and analyzing the entire application stack easier. Utilizing such platforms simplifies the operational workflow and enhances the ability to detect and resolve issues promptly by providing holistic visibility into the system’s performance and health.Security and ComplianceEnsuring the security and compliance of observability data is paramount, especially for industries with stringent regulatory requirements, such as finance and healthcare. Observability data often contains sensitive information, including user activity logs and transaction details, which can be targets for cyberattacks. Implementing robust access controls is crucial to protect this data. Role-based access controls (RBAC) ensure only authorized personnel can view and interact with sensitive information, minimizing the risk of unauthorized access and data breaches.Additionally, enforcing strict authentication mechanisms, such as multi-factor authentication (MFA), adds an extra layer of security. Data encryption is another critical aspect of maintaining security and compliance. Encrypting observability data in transit and at rest protects it from interception and unauthorized access. Organizations should use industry-standard encryption protocols to ensure the highest level of data protection. Regular audits of observability practices are also essential to maintain compliance with regulatory standards.Audits help identify potential security gaps and verify that observability processes align with organizational policies and regulations. Leveraging compliance-focused observability tools that offer features like audit logs, access tracking, and compliance reporting can facilitate easier management of regulatory requirements and ensure continuous adherence to security standards.Continuous TrainingObservability tools and practices evolve rapidly, and keeping your team updated on the latest advancements is crucial for maintaining effective observability. Regular training sessions and workshops can help team members stay abreast of new tools, features, and best practices, ensuring they are well-equipped to handle contemporary observability challenges. Hands-on training provides practical experience, allowing team members to apply new knowledge directly to their work.Offering access to certification programs related to observability tools and methodologies can further validate skills and knowledge, fostering a culture of continuous learning and professional development. Encouraging knowledge sharing within the organization is another vital component of constant training. Creating forums, internal wikis, or chat channels where team members can share insights, tips, and experiences promotes peer learning and collaboration.Participation in industry conferences, webinars, and meetups focused on observability also provides opportunities to learn from experts, network with peers, and stay informed about emerging trends. Adopting a mindset of continuous improvement by regularly reviewing and refining observability processes based on feedback and new learnings ensures that the organization remains agile and responsive to evolving observability needs.ConclusionIn conclusion, understanding and effectively implementing full stack observability can significantly enhance modern applications’ performance, reliability, and security. By providing end-to-end visibility into all application stack layers, full stack observability empowers organizations to proactively detect and resolve issues, improve collaboration, and deliver superior user experiences. As the digital landscape evolves, embracing full stack observability will be a crucial strategy for staying ahead in the competitive market.Do you need help with implementing full stack observability in your organization?

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What Is Infrastructure Automation?

In an era where businesses are rapidly transforming through digital advancements, Infrastructure Automation has emerged as a critical aspect of IT operations. This blog delves into infrastructure automation solutions, exploring their definition, benefits, common tools, and best practices to ensure your organization stays ahead in the competitive landscape.Introduction to Infrastructure AutomationIT infrastructure automation uses software to create repeatable instructions and processes to replace or reduce human interaction with IT systems. This includes the automated provisioning, management, monitoring, and scaling of various infrastructure components like servers, networks, storage, and applications.By automating these processes, organizations can achieve greater efficiency, consistency, and speed in their IT operations, essential for meeting the demands of modern, dynamic business environments.The Need for Infrastructure AutomationIn today’s fast-paced digital world, manual infrastructure management is time-consuming and prone to errors. As businesses scale and their IT environments become more complex, the need for cloud infrastructure automation becomes apparent. Let’s delve deeper into why infrastructure automation is crucial:ScalabilityAs businesses grow, manually managing infrastructure becomes highly impractical. Scaling infrastructure to meet increasing demand involves provisioning new servers, configuring them, and integrating them into the existing environment. This process can be tedious and error-prone when done manually. Automation tools like Terraform and the Ansible Automation Platform can provision new instances in seconds, ensuring that resources are allocated dynamically based on real-time needs. This approach saves time and eliminates the risks associated with manual scaling, such as configuration drift and human errors.ConsistencyEnsuring configurations are applied consistently across servers and environments is critical for maintaining system integrity and performance. Configuration management tools, such as Puppet and Chef, are used to configure provisioned infrastructure resources and manage the state of infrastructure using state files. Manual processes often lead to discrepancies in configurations due to human error or oversight. Infrastructure as Code (IaC) tools like Puppet and Chef ensure that configuration files are version-controlled and applied uniformly across all environments.This eliminates the “works on my machine” problem, ensuring that development, staging, and production environments are identical. Consistent configurations also make it easier to troubleshoot issues and enforce compliance standards.SpeedAutomated processes can deploy and manage infrastructure faster than human intervention. In traditional setups, deploying a new environment could take days or even weeks due to the need for manual setup, configuration, and testing. Automation accelerates this process by rapidly deploying pre-defined templates and scripts to deploy infrastructure components.For instance, a tool like Kubernetes can automatically deploy, scale, and manage containerized applications, reducing deployment times from hours to minutes. This speed is crucial for agile development practices and enables continuous integration and continuous deployment (CI/CD) pipelines by integrating with a version control system.Cost Efficiency of Infrastructure ResourcesReducing the need for manual intervention decreases labor costs and minimizes downtime. Manual infrastructure management requires significant human resources, leading to higher operational expenses. Automation reduces these costs by handling repetitive tasks, freeing up IT staff to focus on more strategic initiatives. Furthermore, automated systems can optimize resource usage, shutting down underutilized instances and scaling resources based on demand.This dynamic resource allocation ensures that you only pay for what you use, leading to substantial cost savings. Tools like AWS Auto Scaling and Azure Scale Sets exemplify this principle by automatically adjusting the number of active instances to match workload requirements. Additionally, tools with huge community support, such as Terraform, provide extensive resources and modules, further enhancing cost efficiency.Enhanced SecurityAutomated systems can enforce security policies more reliably than manual methods. Security is paramount in any IT environment, and manual processes often fail to implement policies consistently, exposing vulnerabilities. Automation tools can embed security checks and compliance requirements directly into the deployment pipelines. For example, using tools like HashiCorp Vault for secrets management ensures that sensitive information is stored securely and accessed only by authorized entities.Automated compliance checks can continuously monitor the infrastructure for policy violations and remediate issues quickly, reducing the risk of security breaches. Moreover, infrastructure monitoring tools like Nagios can monitor various resources within both on-prem and cloud environments, ensuring that security patches and updates are applied automatically and protecting all systems against known vulnerabilities.Key Components of Infrastructure AutomationInfrastructure automation involves several key components that seamlessly enhance IT operations, including network management. Let’s explore each of these components in greater detail:Configuration ManagementConfiguration management automates the setup and maintenance of servers and software configurations, ensuring consistency across various environments. Tools like Ansible, Puppet, and Chef enable administrators to define infrastructure as code (IaC), which allows the desired state of the infrastructure to be specified through scripts. These tools ensure that any changes made to the configuration are applied uniformly, reducing the risk of configuration drift. Additionally, they support idempotency, meaning that using the same script multiple times will always result in the same configuration state. This approach not only reduces manual errors but also simplifies the process of scaling and replicating environments.ProvisioningProvisioning automates setting up virtual machines, containers, and cloud services, significantly speeding up deployment times. Tools like Terraform and AWS CloudFormation allow users to define infrastructure resources using high-level configuration languages, making it easier to manage complex setups. These tools support infrastructure as code (IaC), enabling repeatable and consistent deployment processes across different environments. By automating resource allocation, provisioning tools reduce the need for manual intervention, thereby minimizing human errors and operational overhead. Additionally, many provisioning tools support multi-cloud environments, allowing seamless integration and management of resources across different cloud providers.OrchestrationOrchestration coordinates multiple automated tasks and workflows to ensure cohesive operations across the infrastructure. Tools like Kubernetes, Apache Mesos, and Red Hat OpenShift orchestrate containerized applications, managing their deployment, scaling, and operations. These tools define workflows that automate complex sequences of tasks, such as deploying applications, configuring them, and ensuring their dependencies are met. Orchestration tools also handle service coordination, ensuring that services start in the correct order and maintain the required state during operation. This capability is critical for maintaining high availability and performance, especially in dynamic and distributed environments.Monitoring and LoggingMonitoring and logging provide continuous infrastructure oversight to ensure optimal performance and quickly identify issues. Tools like Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), and Splunk collect real-time metrics and log data from various sources. Monitoring tools track resource usage, application performance, and system health, providing insights into the state of the infrastructure. They can trigger alerts based on predefined thresholds, enabling rapid response to potential issues before they escalate. Logging tools aggregate logs from different sources, offering advanced search capabilities and visualizations that aid in troubleshooting and in-depth analysis of system behavior.ScalingScaling automatically adjusts resources based on demand to maintain performance and cost-efficiency, which is essential for managing dynamic workloads. Horizontal scaling involves adding or removing instances to handle changes in load, which tools like Kubernetes can manage automatically based on metrics like CPU and memory usage. Vertical scaling adjusts the resources allocated to a single instance, with cloud providers like AWS and Azure offering services that can dynamically resize instances based on demand. Auto-scaling policies define the conditions under which scaling occurs, ensuring the system can respond to workload fluctuations without manual intervention. This approach optimizes resource usage, reduces costs, and ensures that applications remain performant under varying loads.Popular Tools for Infrastructure AutomationSeveral tools have gained prominence for their ability to streamline infrastructure automation:AnsibleAnsible is a powerful automation platform that automates complex IT tasks using a simple, human-readable language called YAML. It leverages an agentless architecture, meaning no software must be installed on the target machines, simplifying the management process and reducing overhead. Ansible is highly flexible and can manage both Linux and Windows systems, making it a versatile choice for diverse environments. Additionally, it supports declarative configuration, allowing users to define the desired state of their infrastructure, which Ansible then ensures is met by executing the necessary commands.TerraformTerraform is an open-source tool allowing users to define and provide data center infrastructure using a high-level configuration language called HashiCorp Configuration Language (HCL). It supports infrastructure as code (IaC), enabling the consistent and repeated creation, update, and management of infrastructure resources. Terraform is provider-agnostic, meaning it can manage resources across multiple cloud providers like AWS, Azure, and Google Cloud, as well as on-premises environments. Its modular approach allows for reusable configurations, making it easier to manage complex infrastructures efficiently.PuppetPuppet provides a robust framework for managing the entire lifecycle of your infrastructure, from provisioning to configuration to ongoing maintenance. It uses declarative language to define system states and employs an agent-server architecture to enforce these configurations across nodes. Puppet’s model-driven approach ensures that desired states are consistently applied, regardless of the underlying platform or operating system. With features like reporting and auditing, Puppet offers insights into configuration changes and compliance, aiding in governance and operational control.ChefChef enables infrastructure as code (IaC), allowing you to automate server deployment, configuration, and management through scripts written in Ruby-based DSL (domain-specific language). It uses a client-server architecture where Chef clients (nodes) regularly pull configurations from a central Chef server. Chef’s recipes and cookbooks allow for easily reusable and shareable configurations, promoting team collaboration and standardization. Chef integrates seamlessly with various cloud platforms, providing native support for managing resources in multi-cloud environments.Kubernetes for Cloud InfrastructureKubernetes orchestrates containerized applications, automating deployment, scaling, and operations to ensure that applications run reliably across different environments. It manages containerized workloads and services, facilitating both declarative configuration and automation. Kubernetes’ core concepts include Pods (the smallest deployable units), Services (which expose Pods to network traffic), and ReplicaSets (which ensure a specified number of replicas are running). By handling tasks like load balancing, service discovery, and automated rollouts and rollbacks, Kubernetes significantly reduces the operational burden on development and operations teams.Benefits of Infrastructure AutomationImplementing infrastructure automation offers numerous advantages:1. Enhanced EfficiencyAutomated tasks free IT staff to focus on strategic projects rather than routine maintenance, including operating system management. This increases overall productivity and allows for more innovative work within the organization.2. Improved Consistency and ReliabilityAutomation ensures that infrastructure configurations are consistent, reducing the risk of errors and discrepancies that can occur with manual intervention. This reliability is crucial for maintaining uptime and performance.3. Faster DeploymentAutomated provisioning and configuration management enable rapid deployment of new environments, essential for agile development practices and swift responses to market changes.4. ScalabilityInfrastructure automation tools can dynamically adjust resources to meet varying demands, ensuring your systems can scale up or down efficiently without manual oversight.5. Cost SavingsAutomation can significantly cut operational costs by reducing the need for manual intervention and minimizing errors. Additionally, optimized resource usage leads to better cost management.6. Enhanced SecurityAutomation can enforce security policies consistently across the infrastructure, ensuring compliance and reducing the risk of security breaches.Challenges and ConsiderationsWhile the benefits are substantial, implementing infrastructure automation is not without its challenges:Complexity: Setting up and configuring automation tools can be complex, requiring specialized knowledge and skilled personnel. The initial learning curve for understanding scripting languages, configuration files, and integration processes can be steep. This complexity necessitates a well-planned implementation strategy to ensure successful adoption and operation.Initial Costs: Significant upfront costs may be involved in purchasing automation tools and training staff to use them effectively. Investing in high-quality tools and comprehensive training programs is essential but can strain budgets, especially for smaller organizations. These initial expenses must be weighed against the long-term cost savings and efficiency gains automation provides.Cultural Resistance: Shifting from manual processes to automation can be difficult for staff accustomed to traditional methods. Employees may be hesitant to adopt new technologies due to fear of job displacement or lack of familiarity. Addressing these concerns through transparent communication, involvement in the transition process, and demonstration of the benefits of automation can help mitigate resistance.Maintenance: Automated systems require maintenance and updates, which require ongoing attention to ensure they remain effective and secure. Regular updates to automation scripts, tools, and underlying infrastructure are necessary to adapt to changing requirements and vulnerabilities. This continuous maintenance demands dedicated resources and a proactive approach to managing effectively.Best Practices for Implementing Infrastructure AutomationTo effectively implement infrastructure automation, consider these best practices:Start Small: Begin with automating simple, repetitive tasks such as server provisioning or software installation, and gradually move to more complex processes like deployment pipelines or orchestration workflows. This incremental approach allows your team to adapt and learn without feeling overwhelmed by the intricacies of full-scale automation. Starting small also enables you to achieve quick wins, which can build confidence and demonstrate the value of automation to stakeholders.Invest in Training: Ensure your team is well-trained in the tools and processes involved in automation, including understanding scripting languages like YAML, Python, or Ruby and automation frameworks like Ansible, Terraform, and Kubernetes. Comprehensive training programs and hands-on workshops can help your team gain the necessary skills to manage automated environments effectively. Investing in continuous education ensures your staff stays updated with the latest advancements and best practices in automation.Prioritize Documentation: Maintain comprehensive documentation for all automated processes, including configuration scripts, deployment procedures, and troubleshooting guides. Detailed documentation helps troubleshoot issues efficiently, ensuring any team member can quickly understand and resolve problems. It also ensures continuity if key personnel leave the organization, as new team members can easily get up to speed by referencing the documentation.Emphasize Security: Incorporate best practices into your automation scripts and processes by directly embedding security checks, access controls, and compliance requirements into your code. Regularly update and audit your automation tools to protect against vulnerabilities and ensure they adhere to the latest security standards. Emphasizing security from the outset helps prevent potential breaches and ensures your automated infrastructure remains secure and compliant.Monitor and Optimize: Monitor your automated systems using tools like Prometheus, Grafana, or ELK Stack to ensure they function correctly and efficiently. Collect real-time metrics and logs to gain insights into system performance and identify areas for improvement. Use these insights to optimize your automation strategies, adjusting to enhance efficiency, reduce costs, and improve overall system reliability.ConclusionInfrastructure automation is a transformative approach that enables organizations to manage their IT resources more efficiently and effectively. Businesses can implement automation strategies that drive innovation, scalability, and cost savings by understanding its components, benefits, and challenges. As the digital landscape continues to evolve, embracing infrastructure automation will be essential for staying competitive and meeting future demands.Are you ready to begin your journey toward a fully automated infrastructure? Start small, invest in the right tools, and watch your organization’s IT operations become more streamlined and efficient.Feel free to reach out or explore additional resources for insights and guidance on infrastructure automation. Your path to a more agile and responsive IT environment starts here.

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Ignite Business Acceleration and Tap into the Might of Distributed Storage Systems

Managing and storing massive amounts of data has become critical for organizations in today’s digital era. Traditional centralized storage systems often need help to cope with modern applications’ scale, performance, and fault tolerance demands. This is where distributed storage systems come into play. In this blog, we will delve into the inner workings of distributed storage systems, exploring the types of data they can handle, their advantages, how to choose the right system, and some famous examples.Source : BMCDifferent Data Types for Distributed StorageDistributed storage systems are built to effectively manage extensive spectrums of data variations – ranging from structured data that strictly adheres to a schema or model (such as relational databases or RDBMS), semi-structured data like XML/JSON files containing structured tags or markers to loose-format, high complexity unstructured data (often found in text, multimedia).These streamlined systems accommodate many data types through specialized components. On the one hand, they handle traditional databases efficiently, utilizing their characteristic algorithms to synchronize data across geographically diverse nodes while maintaining strict ACID (Atomicity, Consistency, Isolation, Durability) properties required for transaction-oriented operations.For file systems and object storage, the distributed storage arrangements synergistically integrate pertinent protocols dealing with file semantics, allowing scalable high-capacity conservatories for binary data objects. Such mechanisms facilitate immediate access to files or delivery of concurrent read and write operations via RESTful APIs, fueling high-performance computing (HPC) environments.Parallelly, distributed key-value stores are capacitated to architect massive horizontally scalable storehouses for web-scale integers. They provide commendable low-latency performance for highly replicated, lightning-speed read/write endeavors. They support significant data operations with beneficent, paramount performance, such as real-time analytics, personalized content delivery, and caching.Unlocking the Benefits of Distributed StorageDistributed storage systems provide several advantages, including:Scalability: One undeniable advantage of distributed storage systems is their peerless capability for horizontal scaling. As your organization continues to create and accumulate data, these systems are designed to grow with you. Assets are allowed to expand across servers, rather than in a hierarchical manner, fostering an environment ready for extensive new data streams or increasing quantity of data cases. Fault-Tolerance: Built on the very concept of decentralization, distributed storage systems intelligently replicate data across multiple connected nodes (both within and across geographically dispersed locations) to inherently serve built-in redundancy. If one node encounters a problem or breaks down, the entire data stake is not jeopardized but can be resourced from other points in the network. Performance: Enhanced performance figures are consistently achieved through distributed storage due to the congruent delegation of tasks finely openly allocated across linked nodes. Each node operates independently in its mastered field, whereas chores, like read and write operations, get strikingly shared, effectively leading to positive combined enhancements and faster execution. Flexibility: Distributed storage architecture is hallmarked by a high degree of plasticity. The use-case agnostic storage design can ideally leverage everything ranging from serving high-performance computing demands via quick access to storage clusters, omnipresent requisites with geographically distributed data, low-latency data retrieval scenarios in OLTP (Online transaction processing) set-ups to fully ingrained analytic processing capabilities for deciphering voluminous valuable business insights. Factors to Consider When Selecting Distributed StorageChoosing the right distributed storage system depends on several factors, including:Data Requirements: Parameters such as the projected data size (including current volumes and growth estimates), the inherent structure of data (structured like SQL, semi-structured like SQL-NoSQL hybrids, or unformatted streams of data like log events), and the anticipated patterns of data interaction should underpin the decision-making process. For instance, it is essential to understand whether the system should cater to infrequent but complex data queries demanding high processing power or recurring and simple read-and-write operations requesting low latency delivery. Consistency Trade-offs: When deciding on infrastructural components, ascertain your application’s consistency requirements. Some systems vouch for strong consistency, aspiring to achieve ‘linearizability’ where every operation appears atomically and in a specific order, thereby ensuring stringent control and high data authenticity. On the other spectrum is ‘eventual consistency,’ a model championed by other systems that may initially tolerate temporary inconsistencies but guarantees ultimate data replication unity across nodes over a period. Performance and Scalability: It is recommended to investigate performance indicators of prospective systems meticulously. These include read and write latencies as they directly influence user experience and operational efficiency. Equally important is to assess the system’s ability to scale horizontally and ascertain if it can dynamically increase capacity by connecting several hardware or software entities to work symbiotically as a unified network. This capability guarantees sustained service even in the face of voluminous data accrual or high concurrent connection. Deployment Model: Pick the platform environment in alignment with your organization’s defined infrastructure preferences, operational needs, and enterprise strategy. You could opt for an on-premises deployment ensuring maximum control and potential compliance adherence, a cloud-based deployment capitalizing on scalability, simplicity, and cost-effectiveness, or a hybrid model, which effectively marries both on-premises and cloud components to optimize operational agility, cost, and performance dependencies while adhering to data locality regulation. Exploring Distributed Storage SolutionsThere are numerous distributed storage systems available, each catering to different use cases. Here are a few notable examples:Apache Hadoop Distributed File System (HDFS): HDFS is a widely used distributed file system designed for big data processing. It offers high fault tolerance, scalability, and compatibility with the Hadoop ecosystem. Amazon S3: Amazon Simple Storage Service (S3) is a popular object storage service that provides virtually unlimited scalability, durability, and low-cost storage for various applications. Apache Cassandra: Cassandra is a highly scalable, distributed database management system known for its ability to handle massive amounts of structured and unstructured data with high availability and fault tolerance. Google Cloud Storage: Google Cloud Storage offers a scalable and secure object storage service designed for storing and retrieving large amounts of data with strong consistency and global accessibility. Embracing the Power of Distributed Storage Systems Distributed storage systems have revolutionized the way organizations manage and store data. By offering scalability, fault tolerance, and performance, they provide robust solutions for modern data-intensive applications. When choosing a distributed storage system, it’s essential to consider factors such as data requirements, consistency trade-offs, performance, and deployment models. With the right strategy in place, organizations can unlock the full potential of their data infrastructure and drive innovation in today’s digital landscape.

Aziro Marketing

Immunize Customer Experience With These Cloud Storage Security Practices

Cloud Storage, a Great ChoiceA 21st-century industry looking for uncompromising scalability and performance cannot possibly come across Cloud Storage and say, “I’ll pass.” Be it fintech or healthcare, small-sized customers, or multi-national clients; cloud storage is there to store and protect all business sensitive data for all business use cases. While modern services like smart data lakes, automated data backup and restore, mobility, and IoT revamp the customer experience, cloud storage would ensure impeccable infrastructure for data configuration, management, and durability. Any enterprise working with cloud storage is guaranteed to enjoy:Optimized Storage CostsMinimized Operational OverheadContinuous MonitoringLatency-Based Data TieringAutomated Data Backup, Archival & RestoreThroughput Intensive Storage AndSmart Workload ManagementHowever, such benefits come with a pre-requisite priority for the security of the cloud storage infrastructure. The data center and the network it operates in need to be highly secured from internal and external mishaps. Therefore, in this blog, we will discuss the various practices which would help you ensure the security of your cloud storage infrastructure. For a more technical sense of these practices, we will talk about one of the most popular cloud storage services – Amazon S3. However, the discussion around practices will be more generic to ensure that you can use them for any cloud storage vendor of your choice.Comprehending Cloud Storage SecurityA recent study suggests that 93% of companies are concerned about the security risks associated with the cloud. The technical architects and admins directly in contact with cloud storage solutions often face security issues that they don’t fully comprehend. With an increasing number of ransomware and phishing attacks, the organization might often find themselves skeptical about migrating the data. So, how does one overcome these doubts and work towards a secure, business-boosting storage infrastructure? The answer, actually, is two-part:External Security – The security of the storage infrastructure itself is more of a vendor’s job. For instance, in the case of Amazon S3, AWS takes the onus of protecting the infrastructure that you trust your data with. Managing the cloud storage infrastructure makes sense for the vendor to carry out regular tests, audit, and verify the security firewalls of the cloud. Moreover, a lot of data compliance issues rightly fall under the vendor’s scope of responsibility so that you don’t have to worry about the administrative regulations for your data storage.Internal Security – Ensuring the security from the inside is where you, as a cloud storage service consumer, share the responsibility. Based on the services you’ve employed from your cloud storage vendor, you are expected to be fully aware of the sensitivity of your data, the compliance regulations of your organization, and the regulations mandatory as per the local authorities in your geography. The reason behind these responsibilities is the control you get as a consumer over the data that goes into the cloud storage. While the vendor would provide you with a range of security tools and services, it should be your final choice that would align with the sensitivity of your business data.Thus, in this blog, we will discuss all the security services and configurations you can demand from your vendor to ensure that cloud storage is an ally against your competition and not another headache for your business.Confirm Data DurabilityThe durability of infrastructure should be among the first pre-requisites for storing mission-critical data on the cloud. Redundant storage of data objects across multiple devices ensures reliable data protection. Amazon S3, for that matter, uses its PUT and PUTObject operations to copy the data objects at multiple facilities simultaneously. These facilities are then vigilantly monitored for any loss so that immediate repairs can be arranged. Some of the important practices to ensure data durability are:Versioning – Ensure that the data objects are versioned. This will allow recovering older data objects in the face of any internal or external application failure.Role-Based Access – Setting up individual accounts for each user with rightful liberties and restrictions discourages data leakage due to unnecessary access.Encryption – Server-side and in-transit data encryption modules provide an additional layer of protection, assuring that the data objects aren’t harmed during business operations. Amazon S3, for instance, uses Federal Information Processing Standard (FIPS) 140-2 validated cryptographic modules for such purpose.Machine Learning – Cloud Storage vendors also offer machine learning-based data protection modules that recognize the business sensitivity of data objects and alert the storage admins about unencrypted data, unnecessary access, and shared sensitive data objects. Amazon Macie is one such tool offered by AWS.Making the Data UnreadableThe in-transit data (going in and out of the cloud storage data centers) is vulnerable to network-based attacks. Measures need to be taken to ensure that this data, even if breached, is of no use to the attacker. The best method to achieve this is Data Encryption. Encryption modules like SSL/TLS are available to make sure that the data is unreadable without proper decryption keys. The cloud storage vendors provide server-side and client-side encryption strategies for the same purpose. In the case of Amazon S3, the objects can be encrypted when they are stored and decrypted back when they are downloaded. You, as a client, can manage the encryption keys and choose the suitable tools for your requirements.Managing the Traffic MischiefWhile the traffic on the public network is vulnerable to data thievery, the private network might often fall prey to internal mismanagement. To avoid both cases, most cloud vendors offer security sensitive APIs. These help the application operate with transport layer security while working with cloud storage data. TLS1.2 or above are usually recommended for modern data storage infrastructures, including the cloud. Talking about Amazon S3 in particular, AWS offers VPN and private link connections like Site-to-site and Direct connect to support safe connectivity for on-premise networks. To connect with other resources in the region, S3 uses a Virtual private cloud (VPC) endpoint that ensures that the requests are limited to and from the Amazon S3 bucket and VPC cloud.SSL cipher suites provide the guidelines for secure network operations. A category of such cipher suites supports what is known as Perfect Forward Secrecy – which essentially makes sure that the encryption and decryption keys are regularly changed. As a client, you should look for cloud storage service providers that support such suites in order to ensure a secure network. Amazon S3, for this purpose, uses DHE (Diffie-Hellman Ephermal) or ECDHE (Elliptic Curve Diffie-Hellman Ephermal. Both are highly recommended suites supported by any application running on modern programming paradigms.Ask Before AccessAdmins handling cloud storage operations should follow strict access policies for resource access control. Both the resource and user-based access policies are offered by the cloud storage provider for the organization to choose from. It is imperative that you choose the right combination of these policies so that the permissions to your cloud storage infrastructure are tightly defined. A handy ally for this purpose in the case of Amazon S3 is an Access control list (ACL) where the access policies are defined for the S3 bucket, and you can easily choose the combo of your choice.Watchful MonitoringMaintain reliability, guaranteed availability, and untroubled performance are all results of a dark knight level monitoring. For cloud storage, you need a centralized monitoring dashboard of sorts that provides multi-point monitoring data. Check if your cloud vendor provides tools for:Automated single metric monitoring – Monitoring system that takes care of a specific metric and immediately flags any deviations from the expected resultsRequest Trailing – Request triggered by any user or service needs to be trailed for details like request source IP, request time, etc., to log the actions taken on the cloud storage data. Server access requests are also logged for this purpose.Security Incident Logging – Fault tolerance can only be strengthened if any and every misconduct is logged with associated metrics and the resolutions assigned for the purpose. Such logs also help for automated recommendations for future conducts related to cloud storage.ConclusionThere’ve been multiple episodes where companies serving high-profile customer-base faced humiliating attacks that went undetected over a considerable period of time. Such security gaps are not at all conducive to the customer experience we aim to serve. The security practices mentioned above will ensure that fragile corners of your cloud storage are all cemented and toughened up against the looming threats of ransomware and phishing hacks.

Aziro Marketing

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