Chester Conforte, Author at Gigaom

GigaOm Key Criteria for Evaluating Cloud-Native Globally Distributed File System Solutions

Chester Conforte — Mon, 04 Nov 2024 17:02:58 +0000

File storage has long been an important element of enterprise data storage infrastructure, so it’s no surprise that now, more than ever, users ask for file services in the cloud.

At first, cloud providers focused on block and object storage, leaving file services out of their initial offerings. Block and object storage work well for many use cases, and newer applications can be designed to make file storage unnecessary. However, the reality is that in many circumstances, file storage is preferable for the following reasons:

Simplicity: File storage is the most user-friendly storage, making it a go-to for developers who want to build more portable applications and simplify the sharing of machine- and human-generated data.
Lift and shift: As more enterprises transition to the public cloud for their primary IT infrastructure, “lift-and-shift” migrations are now commonplace. In this scenario, users want to replicate the same services they had in their on-premises data center, including POSIX-compliant file systems, data services, and all the other enterprise features they are accustomed to.
Performance: Though object storage performance has improved by leaps and bounds, file systems still provide the best combination of performance, usability, and scalability for many workloads. They’re still the primary interface for most big data, AI/ML, and high-performance computing (HPC) applications, and they usually offer data services such as snapshots to improve data management operations.
Collaboration: With hybrid cloud infrastructures and distributed organizations spanning the world, the ability to seamlessly access data from everywhere facilitates teamwork while keeping data under control.

In recent years, file systems have evolved from being cloud-friendly to cloud-native, offering better integration with elastic infrastructure elements across public and private cloud providers. This feature brought several advantages to end users:

Better scalability: File systems specifically designed to work with multiprotocol elastic storage elements provide a good balance between performance and cost.
Cost tuning: Policy-driven tiering mechanisms allow cold data to be moved to low-cost storage, saving precious resources in the high-performance tier.
Dynamic data placement: Many modern file systems can replicate data to remote file or object stores, in the cloud or on-premises. This makes it possible to synchronize and serve datasets across different infrastructures to optimize compute proximity, which lowers latency.
Disaster recovery: Syncing data to a remote object store enables users to leverage a lower cost storage repository and repopulate a file system when necessary.

Business Imperative
These capabilities are particularly important as vendors optimize their file systems to take advantage of newer classes of flash memory, distributed computing, and networking technologies. This enables users to design, build, and scale highly optimized IO/$/GB ratios across disparate infrastructure providers and hardware paradigms. Furthermore, as more users embrace hybrid and multicloud strategies, his storage infrastructure fits very well in this context.

That trend highlights why cloud-native file systems are becoming increasingly successful, especially as they mature and bridge the gap between on-premises and public cloud storage service providers while delivering a globally consistent experience.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of deploying a cloud-native globally distributed file system, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a cloud-native globally distributed file system, we provide an overall Sector Adoption Score (Figure 1) of 4.2 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a cloud-native globally distributed file system is a compelling candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for cloud-native globally distributed file systems are explained in more detail in the Sector Brief section that follows.

Key Criteria for Evaluating Cloud-Native Globally Distributed File System Solutions

Sector Adoption Score

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Figure 1. Sector Adoption Score for Cloud-Native Globally Distributed File System

This is the first year that GigaOm has reported on the cloud-native globally distributed file system space in the context of our Key Criteria and Radar reports. This GigaOm Key Criteria report highlights the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) for selecting an effective cloud-native globally distributed file system. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the market, identify leading cloud-native globally distributed file systems, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The GigaOm Key Criteria report provides a detailed decision framework for IT and executive leadership assessing enterprise technologies. Each report defines relevant functional and nonfunctional aspects of solutions in a sector. The Key Criteria report informs the GigaOm Radar report, which provides a forward-looking assessment of vendor solutions in the sector.

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GigaOm Radar for Cloud-Native Globally Distributed File Systems

Chester Conforte — Fri, 18 Oct 2024 15:00:53 +0000

File storage remains a crucial component of every enterprise data storage infrastructure, so it’s no surprise that now, more than ever, users ask for file services in the cloud. Use cases and workloads are different, though, and evaluating the right technology is mandatory to support applications and users correctly.

Initially, cloud providers neglected to add file services to their product portfolios, concentrating instead on block and object storage. Block and object storage cover many use cases, and new applications can be developed so that file storage is unnecessary. However, the reality is that in many circumstances, file storage is preferable.

Lift and shift: As more enterprises adopt the public cloud as their primary IT infrastructure, “lift and shift” migrations have become increasingly common. In this scenario, users want to replicate the same services they had in their on-premises data center, including POSIX-compliant file systems, data services, and all the other enterprise features they are accustomed to.

Simplicity: File storage is the most user-friendly storage, and many developers still prefer this familiar interface because it enables them to build even more portable applications while simplifying the sharing of machine- and human-generated data.

Performance: Though object storage performance is improving by leaps and bounds, file systems still provide the best combination of performance, usability, and scalability for many workloads. File storage is still the primary interface for the majority of big data, AI/ML, and high-performance computing (HPC) applications, and usually offers data services such as snapshots to improve data management operations.
Collaboration: With hybrid cloud infrastructures and globally distributed organizations becoming the norm, the ability to seamlessly access data from everywhere simplifies teamwork while keeping data under control. This use case, already on the rise before COVID-19, was dramatically boosted during the pandemic. A growing number of organizations have since reevaluated their remote work policies. While some advocate for a full return to the office, many organizations are instead implementing hybrid work policies that still require remote access for their employees.

In addition, the COVID-19 pandemic sped up existing digital transformation initiatives and forced reluctant organizations to transform at a much faster pace. This accelerated transformation drove the adoption of cloud-native, globally distributed file systems, as organizations embraced cloud-based solutions to maximize agility and increase productivity.

In recent years, file systems have become more cloud-friendly, offering better integration with object storage. This feature brought several advantages to end users:

Better scalability. Policy-driven tiering mechanisms allow cold data to be moved to S3-compatible storage, saving precious resources in the high-performance tier.
The best combination of speed and $/GB. File storage gateways specifically designed to integrate with object storage backends provide a good balance of performance and cost.
Simplified data migrations and synchronization. Many file systems can replicate data to remote file or object stores in the cloud or on-premises. This makes it possible to synchronize and serve datasets across different infrastructures to optimize compute-data proximity, which improves latency.
Disaster recovery. Syncing data to a remote object store enables users to leverage a cheaper storage repository in the cloud and populate a file system only if necessary.

In today’s digital landscape, the value of globally distributed file systems is more significant than ever. As vendors enhance their systems to leverage flash memory and improve access speed, organizations can construct multitier infrastructures that optimize cost per gigabyte. This evolution is crucial as businesses increasingly adopt hybrid and multicloud strategies for their infrastructure needs. Globally distributed file systems address one of the most pressing challenges for distributed organizations: the proliferation of duplicate data and the inefficiencies of centralized data strategies. These systems mitigate the issue of data gravity, which often hinders the agility of modern distributed teams. By reducing technical inertia, globally distributed file systems empower enterprises to seize new opportunities in emerging fields like edge computing, artificial intelligence, and analytics, thus driving innovation and competitive advantage.

With this trend in mind, it’s easy to understand the reasons for the success of file storage in the cloud, especially if it can also be integrated with on-premises and other cloud storage.

This is our first year evaluating the cloud-native globally distributed file system space in the context of our Key Criteria and Radar reports. In previous years, we evaluated the “cloud file storage” market and produced two Radar reports, one for distributed cloud file storage and one for high-performance cloud file storage. However, for this evaluation, we’ve updated the scope and table stakes (inclusion criteria), and are considering this year’s report a V1.0 as a result.

This GigaOm Radar report examines seven of the top cloud-native globally distributed file system solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading cloud-native globally distributed file system offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

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Backup to the Future!

Chester Conforte — Tue, 01 Oct 2024 17:25:32 +0000

“I finally invented something that works!” This iconic quote from Back to the Future perfectly encapsulates the transformative journey of cloud-native data protection. As organizations increasingly migrate to the cloud, the landscape of data protection is evolving at a breakneck pace, much like Marty McFly’s adventures through time. Let’s take a closer look at the current state of the market, its history, and what the future holds for cloud-native data protection.

A Brief History of Cloud-Native Data Protection

“And in the future, we don’t need horses. We have motorized carriages called automobiles,” explains Doc Brown in Back to the Future III. Similarly, the journey of data protection has been nothing short of revolutionary. Initially, organizations relied on traditional on-premises solutions, which were often cumbersome and expensive. As cloud adoption grew, these legacy systems struggled to keep up with the dynamic and scalable nature of cloud environments. This led to the emergence of cloud-native data protection solutions, designed to leverage the inherent benefits of the cloud.

Cloud-native data protection aligns with the cloud’s operating model, offering pay-as-you-grow pricing, comprehensive workload support, and seamless cloud integration. These solutions are built using modern cloud principles such as microservices, containerization, and serverless architectures, providing greater scalability, resilience, and agility compared to traditional monolithic applications.

The Current State of the Market

“Wait a minute, Doc. Are you telling me you built a time machine … out of a DeLorean?” Just as Doc Brown’s DeLorean was a game changer, cloud-native data protection solutions have revolutionized how organizations safeguard their data. Today, the market is characterized by a diverse array of solutions that cater to various needs, from backup and disaster recovery to cyber resilience and compliance.

Key players in the market, such as Clumio, Rubrik, Commvault, and Acronis, offer robust solutions that integrate seamlessly with public cloud providers like AWS, Azure, and Google Cloud. These solutions provide features such as end-to-end encryption, granular recovery options, and advanced threat detection, ensuring that data is protected against both accidental loss and malicious attacks.

Emerging Technologies and Future Trends

“If my calculations are correct, when this baby hits 88 miles per hour, you’re gonna see some serious s–t,” said Doc Brown of his invention. The future of cloud-native data protection is also brimming with potential, driven by emerging technologies and evolving market demands. Here are some key trends to watch:

AI and ML: AI and ML are set to play a pivotal role in enhancing data protection. These technologies can automate threat detection, predict potential vulnerabilities, and optimize backup and recovery processes. For instance, Commvault’s AI-driven capabilities help organizations detect threats quickly and ensure clean recovery points.

Serverless architectures: The adoption of serverless architectures is expected to increase, providing greater scalability and cost efficiency. Solutions like Rubrik’s serverless design simplify deployment and management, allowing organizations to focus on their core business activities.

Multicloud and hybrid environments: As organizations continue to adopt multicloud and hybrid strategies, data protection solutions will need to offer seamless integration across different cloud platforms. This will ensure consistent protection and management of data, regardless of where it resides.

Cyber resilience: With the rise in cyber threats, the focus on cyber resilience will intensify. Solutions will need to offer advanced features such as data immutability, air-gapped backups, and proactive threat monitoring to safeguard against ransomware and other attacks.

The Next 12-24 Months: Market and Technology Evolution

The next couple of years will see significant advancements in cloud-native data protection, reshaping the market and technology landscape. “Where we’re going, we don’t need roads,” but here’s what to expect:

Increased adoption of SaaS models: The shift toward software-as-a-service (SaaS) models will continue, offering organizations flexible, pay-as-you-go options. This will reduce the need for heavy upfront investments and allow for more agile and scalable data protection strategies.

Enhanced integration with cloud-native services: Data protection solutions will increasingly integrate with cloud-native services such as Kubernetes and serverless functions. This will enable more efficient and automated protection of containerized and serverless workloads.

Focus on data privacy and compliance: As data privacy regulations become more stringent, solutions will need to offer robust compliance features. This includes capabilities for data discovery, classification, and policy-based management to ensure adherence to regulations like GDPR and CCPA.

Expansion of cyber resilience capabilities: The emphasis on cyber resilience will lead to the development of more sophisticated threat detection and response mechanisms. Solutions will leverage AI and ML to provide real-time insights and automated remediation, minimizing the impact of cyber incidents.

Preparing for the Future

“Your future is whatever you make it.” To stay ahead in the evolving landscape of cloud-native data protection, organizations should take proactive steps to position themselves for success.

Embrace automation: Leverage AI and ML to automate data protection processes, from threat detection to backup and recovery. This will enhance efficiency and reduce the risk of human error.

Adopt a multicloud strategy: Ensure your data protection solution supports multicloud and hybrid environments. This will provide flexibility and resilience, allowing you to protect data across different platforms.

Focus on cyber resilience: Implement advanced cyber resilience features such as data immutability, air-gapped backups, and proactive threat monitoring. This will safeguard your data against evolving cyber threats.

Stay compliant: Keep abreast of data privacy regulations and ensure your data protection solution offers robust compliance features. This will help you avoid costly fines and protect your organization’s reputation.

Invest in training: Equip your IT teams with the skills and knowledge needed to manage modern data protection solutions. This will ensure they can effectively leverage the latest technologies and best practices.

Next Steps

“Great Scott!” The future of cloud-native data protection is bright, with emerging technologies and evolving market demands driving innovation. By embracing these advancements and preparing for the future, organizations can ensure their data is protected, resilient, and compliant. As Doc Brown wisely said, “Your future is whatever you make it, so make it a good one.”

To learn more, take a look at GigaOm’s cloud-native data protection Sonar report. This report provides a comprehensive overview of the market, outlines the criteria you’ll want to consider in a purchase decision, and evaluates how a number of vendors perform against those decision criteria.

GigaOm Sonar for Cloud-Native Data Protection

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GigaOm Sonar for Cloud-Native Data Protection

Chester Conforte — Wed, 07 Aug 2024 15:00:39 +0000

Many organizations are investing heavily in the cloud to improve their agility and optimize the total cost of ownership (TCO) of their infrastructure. They are moving applications and data to the public cloud to take advantage of its flexibility only to discover that, when not properly managed, public cloud costs can quickly spiral out of control. This can leave them exposed to unintentional risk due to their lack of experience managing diverse shared-responsibility models.

Data storage and protection are among the biggest pain points along many cloud adoption journeys. Many of the services available in a cloud need to be enhanced and hardened to deliver the predictable reliability and availability of more traditional enterprise technology architectures. The tools for managing the protection of data, state, and configuration saved in those systems must operate well beyond simple snapshot-based data protection.

In addition, organizations are taking advantage of the flexibility and broad set of offerings provided by public clouds. It is not uncommon for an organization to consume cloud services from different public cloud providers. In that context, multicloud support and the ability to abstract management complexities behind a unified management pane drastically simplify data protection operations and increase operational efficiency.

Cloud-native data protection solutions are designed to add enterprise-class data protection functionality that leverages modern cloud patterns of infinitely scalable storage, microservices, containerization, functions, automation, orchestration capabilities, and DevOps practices. These features enable greater scalability, resilience, and agility compared to traditional monolithic appliance, virtual, or cloud-hosted applications while improving data management processes and costs.

Compared to traditional backup solutions, cloud-native data protection offers several advantages by streamlining operations, minimizing duplication of infrastructure costs and effort, and leveraging investments in cloud adoption to more tightly align with a business’s strategic digital transformation objectives.

In this regard, organizations should take into account some important factors of cloud-native data protection solutions:

Speed: When properly integrated, cloud-native backup can take advantage of the underlying cloud’s elasticity, scalability, and competitive differentiators to speed up backup and restore operations. It’s not uncommon for cloud-native backup solutions to blur the lines between data protection, cyber security, and business continuity. This hybridization has the intended consequence of delivering supreme levels of resilience and business agility in a continuously evolving cyber landscape.
Granularity: One of the biggest limitations of traditional backups is the inability to easily restore the “what, when, and how it was” in the event of a data loss incident. To do this, users typically have to go through layers of people, processes, and technology inefficiencies to find, restore, and recover data to a known good state–often leaving teams to hunt and peck for nuggets of data using coarse-grained tools. This slow and error-prone process bleeds a business’s velocity and ultimately impacts its ability to maintain high levels of execution and momentum.
Cyber resiliency: Creating distance between source and backup targets is the basis of every safety and security practice in data protection, especially with the increasing number of cyber-attacks becoming the norm. The 3-2-1 principle, originating from the era of on-premises data storage, remains a widely referenced guideline in modern data protection. This well-established principle can still serve as a valuable framework for business and security decision-makers for enhancing their cyber infrastructure against contemporary data risks. In essence, the 3-2-1 backup rule advocates for maintaining three copies of data across two distinct storage media types, with one copy stored off-site. Data immutability, air gapping, security scanning, and proactive remediation are foundational capabilities for hedging against a worsening security and compliance threat landscape. The ultimate objective of a cloud-native backup solution is the peace of mind that comes from knowing that when (not if) a loss event occurs, your data is protected.
Cost: Often delivered within a software-as-a-service (SaaS) model, cloud-native data protection solutions provide a similar pay-as-you-grow approach through which the organization can immediately reap the benefits of data protection while either owning the orchestration of managing the data protection infrastructure or transferring the difficulties to the software provider. Unlike traditional big iron deployments or virtualized software approaches, customers do not need to handle the costs associated with over-architecting monolithic solutions for corner case scenarios. Cloud-native data protection solutions offer a more agile and dynamic approach to selectively scaling and balancing innovation, maturity, use, and cost for its consumer base.

For this report, we focused on cloud-native data protection tools and did not include on-premises vendors previously covered in GigaOm’s Radar reports on hybrid cloud data protection (HCDP) for SMBs and large enterprises. Among the criteria for inclusion, we looked at both SaaS and pure-play software providers built with modern cloud principles (such as microservices or a serverless architecture) and using scalable storage paradigms. We also looked at each solution’s ability to predominantly protect SaaS, hyperscaler-native, and private cloud workloads while not abandoning traditional endpoint and enterprise workloads, ideally in multicloud scenarios. Finally, we also looked at solutions that offer seamless, cloud-like consumption models, offering pay-as-you-grow economics coupled with elastic scalability.

This is the second year that GigaOm has reported on the cloud-native data protection space in the context of our Sonar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Sonar report provides an overview of the market’s vendors and their available offerings, outlines the key characteristics that prospective buyers should consider when evaluating solutions, and equips IT decision-makers with the information they need to select the best solution for their business and use case requirements.

ABOUT THE GIGAOM SONAR REPORT

This GigaOm report focuses on emerging technologies and market segments. It helps organizations of all sizes to understand a new technology, its strengths and its weaknesses, and how it can fit into the overall IT strategy. The report is organized into five sections:

Overview: An overview of the technology, its major benefits, and possible use cases, as well as an exploration of product implementations already available in the market.

Considerations for Adoption: An analysis of the potential risks and benefits of introducing products based on this technology in an enterprise IT scenario. We look at table stakes and key differentiating features, as well as considerations for how to integrate the new product into the existing environment.

GigaOm Sonar Chart: A graphical representation of the market and its most important players, focused on their value proposition and their roadmap for the future.

Vendor Insights: A breakdown of each vendor’s offering in the sector, scored across key characteristics for enterprise adoption.

Near-Term Roadmap: 12- to 18-month forecast of the future development of the technology, its ecosystem, and major players in this market segment.

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Unlocking the Future of Edge Computing: The Pivotal Role of Kubernetes in Navigating the Next Network Frontier

Chester Conforte — Wed, 27 Mar 2024 13:43:43 +0000

Edge computing represents a significant shift in the IT landscape, moving data processing closer to the source of data generation rather than relying on centralized data centers or cloud-based services that involve transmission over longer distances, imposing higher latency. The distributed edge approach is increasingly important, as the volume of data generated by smart internet of things (IoT) sensors and other edge devices continues to grow exponentially.

Edge Flavors Differ

The diversity of edge devices, ranging from low-power, small form factor multicore devices to those with embedded GPUs, underscores a tremendous opportunity to unlock new network capabilities and services. Edge computing addresses the need for real-time processing, reduced latency, and enhanced security in various applications, from autonomous vehicles to smart cities and industrial IoT.

In my research, it became evident that the demand for edge connectivity and computing is being addressed by a diverse market of projects, approaches, and solutions, all with different philosophies about how to tame the space and deliver compelling outcomes for their users. What’s clear is a palpable need for a standardized approach to managing and orchestrating applications on widely scattered devices effectively.

Kubernetes to the Rescue

Kubernetes has emerged as a cornerstone in the realm of distributed computing, offering a robust platform for managing containerized applications across various environments. Its core principles, including containerization, scalability, and fault tolerance, make it an ideal choice for managing complex, distributed applications. Adapting these principles to the edge computing environment, however, presents special challenges, such as network variability, resource constraints, and the need for localized data processing.

Kubernetes addresses these challenges through features like lightweight distributions and edge-specific extensions, enabling efficient deployment and management of applications at the edge.

Additionally, Kubernetes plays a pivotal role in bridging the gap between developers and operators, offering a common development and deployment toolchain. By providing a consistent API abstraction, Kubernetes facilitates seamless collaboration, allowing developers to focus on building applications while operators manage the underlying infrastructure. This collaboration is crucial in the edge computing context, where the deployment and management of applications across a vast number of distributed edge devices require tight integration between development and operations.

Common Use Cases for Deployment

With common deployment in sectors like healthcare, manufacturing, and telecommunications, the adoption of Kubernetes for edge computing is set to increase. This will be driven by the need for real-time data processing and the benefits of deploying containerized workloads on edge devices. One of the key use cases driving the current wave of interest for edge is the use of AI inference at the edge.

The benefits of using Kubernetes at the edge include not only improved business agility but also the ability to rapidly deploy and scale applications in response to changing demands. The AI-enabled edge is a prime example of how edge Kubernetes can be the toolchain to enable business agility from development to staging to production all the way out to remote locations.

With growing interest and investment, new architectures that facilitate efficient data processing and management at the edge will emerge. These constructs will address the inherent challenges of network variability, resource constraints, and the need for localized data processing. Edge devices often have limited resources, so lightweight Kubernetes distributions like K3s, MicroK8s, and Microshift are becoming more popular. These distributions are designed to address the challenges of deploying Kubernetes in resource-constrained environments and are expected to gain further traction. As deployments grow in complexity, managing and securing edge Kubernetes environments will become a priority. Organizations will invest in tools and practices to ensure the security, compliance, and manageability of their edge deployments.

How to Choose the Right Kubernetes for Edge Computing Solution for Your Business

When preparing for the adoption and deployment of Kubernetes at the edge, organizations should take several steps to ensure a smooth process. Although data containers have been around in some form or fashion since the 1970s, modern computing and its use of Kubernetes orchestration is still early in its lifecycle and lacking maturity. Even with its status as the popular standard for distributed computing, the use of Kubernetes in industry has still not hit adoption parity with virtualized computing and networking.

Business Requirements
Enterprises should first consider the scale of their operations and whether Kubernetes is the right fit for their edge use case. Deployment of Kubernetes at the edge must be weighed against the organization’s appetite to manage the technology’s complexity. It’s become evident that Kubernetes on its own is not enough to enable operations at the edge. Access to a skilled and experienced workforce is a prerequisite for its successful use, but due to its complexity, enterprises need engineers with more than just a basic knowledge of Kubernetes.

Solution Capabilities
Additionally, when evaluating successful use cases of edge Kuberentes deployments, six key features stand out as critical ingredients:

Ecosystem integrations
Flexible customizations
Robust connectivity
Automated platform deployment
Modern app deployment mechanisms
Remote manageability

How a solution performs against these criteria is an important consideration to take into account when buying or building an enterprise-grade edge Kubernetes capability.

Vendor Ecosystem
Lastly, the ability of ecosystem vendors and service providers to manage complexity should be seriously considered when evaluating Kubernetes as the enabling technology for edge use cases. Enterprises should take stock of their current infrastructure and determine whether their edge computing needs align with the capabilities of Kubernetes. Small-to-medium businesses (SMBs) may benefit from partnering with vendors or consultants who specialize in Kubernetes deployments.

Best Practices for a Successful Implementation
Organizations looking to adopt or expand their use of Kubernetes at the edge should focus on three key considerations:

Evaluate and choose the right Kubernetes distribution: Select a Kubernetes distribution that fits the specific needs and constraints of your edge computing environment.
Embrace multicloud and hybrid strategies: Leverage Kubernetes’ portability to integrate edge computing with your existing cloud and on-premises infrastructure, enabling a cohesive and flexible IT environment.
Stay abreast of emerging trends: Monitor the latest developments in the edge Kubernetes sector, including innovations in lightweight distributions, AI/ML integration, and security practices. Edge Kubernetes is at the forefront of modern edge computing. By participating in communities and forums, companies get the unique opportunity to share knowledge, learn from peers, and shape the future of the space.

The integration of Kubernetes into edge computing represents a significant advance in managing the complexity and diversity of edge devices. By leveraging Kubernetes, organizations can harness the full potential of edge computing, driving innovation and efficiency across various applications. The standardized approach offered by Kubernetes simplifies the deployment and management of applications at the edge, enabling businesses to respond more quickly to market changes and capitalize on new business opportunities.

Next Steps

The role of Kubernetes in enabling edge computing will undoubtedly continue to be a key area of focus for developers, operators, and industry leaders alike. The edge Kubernetes sector is poised for significant growth and innovation in the near term. By preparing for these changes and embracing emerging technologies, organizations can leverage Kubernetes at the edge to drive operational efficiency, innovation, and competitive advantage for their business.

To learn more, take a look at GigaOm’s Kubernetes for edge computing Key Criteria and Radar reports. These reports provide a comprehensive overview of the market, outline the criteria you’ll want to consider in a purchase decision, and evaluate how a number of vendors perform against those decision criteria.

If you’re not yet a GigaOm subscriber, you can access the research using a free trial.

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GigaOm Radar for Kubernetes for Edge Computing

Chester Conforte — Thu, 14 Mar 2024 15:00:30 +0000

The escalating growth of data generation at the edge has fueled demand for strategic compute capabilities positioned closer to the source. In response, Kubernetes has emerged as an enticing, standards-based platform for constructing applications geared toward processing data at the edge.

As Kubernetes is the predominant standard for orchestrating containers on a large scale, its adoption at the edge seamlessly extends the orchestration and management capabilities that have made Kubernetes a prevailing choice in cloud and data center environments. The diverse array of use cases and challenges encountered in edge environments necessitates a robust solution like Kubernetes to aid customers in navigating the intricacies.

Today, connected and smart devices span a range of industries, from retail and transportation to healthcare and manufacturing. Whether in remote wind farms or autonomous vehicles, embedded computing devices are generating copious amounts of data, data that emanates from locations far beyond the traditional confines of data centers and cloud environments. Processing such data in centralized locations poses considerable challenges due to intermittent and unreliable connectivity, constrained bandwidth, and high latency. The logical solution is to process data in geographic proximity to its source.

Kubernetes stands out as an ideal platform for building the applications that handle this data. Container-based applications, requiring fewer resources than traditional operating systems, prove well-suited for the constrained resources of rugged, embedded-device form factors. In comparison to static approaches like programmable logic controllers, a general-purpose computing platform, such as Kubernetes, provides greater flexibility. Furthermore, Kubernetes adheres to an operating model familiar to data center operators, offering economies of scope and scale. The same application development methods can be seamlessly applied to cloud-based, traditional data center, and edge-based Kubernetes clusters.

The deployment of Kubernetes for edge computing generally follows one of two common approaches:

Platform: This approach supports a broad spectrum of hardware devices and existing infrastructure components, allowing significant flexibility and choice in both hardware and deployment location enabled by Kubernetes.
Appliance: Resembling the approach taken in hyperconverged infrastructure, this method combines a highly opinionated selection of compute, storage, networking, and orchestration with a vendor-selected software stack enabled by Kubernetes.

Some offerings may combine aspects of both approaches. Platform deployments prove beneficial when existing infrastructure is in place or when customers prefer to maintain a high level of control over the approach and placement of the solution. In contrast, appliance deployments are advantageous when customers prefer vendors to assume responsibility for the qualification and support of the hardware and software combination for hyperspecific, well-defined use cases.

As data processing capabilities, analytics, and real-time decision-making continue migrating closer to where data originates, the concept of edge computing has evolved to enable new market opportunities. Within edge computing, there are gradations of “edge” that provide different capabilities and cater to specialized use cases.

The near edge sits between cloud data centers and the extreme edge, consisting of mini data centers like cell towers, central offices, and campus facilities. The near edge provides compute power, storage, and networking closer to users and devices than the cloud, enabling use cases like content delivery networks, data aggregation from internet of things (IoT) devices, and real-time data analytics requiring very low latency. Kubernetes container orchestration is well-suited for near-edge applications since it allows centralized deployment and management of containerized workloads at this intermediate edge layer.
Moving closer to the data source, the far edge resides on-premises, very close to endpoint devices and sensors. This includes small ruggedized servers or integrated compute sitting inside devices within retail stores, factory floors, and vehicles. The far edge focuses on extremely low latency use cases like AR/VR, industrial automation and control, and autonomous vehicles. Lightweight Kubernetes distributions are optimized to provide the same Kubernetes container benefits but at the resource-constrained far edge.
Finally, at the furthest reach is the device edge, consisting of the endpoints themselves—various sensors, gateways, controllers, and microcontrollers. These devices collect and preprocess data and communicate with far-edge servers. Being highly optimized for specific functions, device edge elements contain only necessary compute, memory, storage, and power suited for embedded environments. Software like Podman can deploy containerized logic directly on devices without full Kubernetes orchestration.

This is our second year evaluating the Kubernetes for edge computing space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines nine of the top Kubernetes for edge computing solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading Kubernetes for edge computing offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The post GigaOm Radar for Kubernetes for Edge Computing appeared first on Gigaom.

GigaOm Key Criteria for Evaluating Kubernetes for Edge Computing Solutions

Chester Conforte — Thu, 07 Mar 2024 20:41:14 +0000

Connected and smart devices are now in widespread use across all areas of business. In every industry—from retail to transport and logistics, healthcare to manufacturing, remote wind farms to autonomous vehicles—embedded computing devices are generating vast quantities of data on their own. This data is being created in a wide variety of locations well outside traditional data centers and cloud environments. Processing this data in traditional, more centralized locations presents a variety of challenges. Connectivity can be intermittent and unreliable, bandwidth is constrained, and latency is high due to transport over great distances. Given these limitations, it makes sense to process data closer to where it is being created.

As the de facto standard for container orchestration at scale, Kubernetes—when adopted at the edge—brings the same orchestration and management capabilities that have made it so popular in cloud and data center environments. The diverse use cases and varied challenges of edge environments sorely need something like Kubernetes to help customers manage the complexity.

Business Imperative
The addition and use of edge Kubernetes to enhance an already oversaturated market of hybrid computing capability is driven by a number of compelling business imperatives. These imperatives primarily revolve around the necessity for efficient, scalable, and secure data processing that is closer to the source of data generation.

From the perspective of the CxO, the following business drivers necessitate the use of edge Kubernetes variants.

Edge computing allows for data processing to take place closer to the source of data generation. This approach significantly reduces latency and improves response times, which is particularly crucial for applications that require real-time or near-real-time responses. Examples of such applications include internet of things (IoT) devices, autonomous vehicles, and certain industrial processes. The capacity to process data with speed and efficiency is a crucial business requirement, particularly when the mitigation of latency is vital to the successful execution of the use case.

Kubernetes is a container orchestration platform that is designed to manage and scale applications across multiple servers. This makes it particularly well-suited for managing the potentially large number of edge nodes in an edge computing environment. As businesses grow and evolve, the ability to scale operations efficiently is essential.

Security of operations is a major business imperative too. Kubernetes provides features such as role-based access control (RBAC), network segmentation, and encryption, which can help secure applications at the edge. Given the distributed nature of edge computing, this is crucial because edge computing presents additional security challenges. In today’s digital age, when cyber threats are increasingly sophisticated and prevalent, edge computing reintroduces classical security risks such as physical theft and vandalism, especially in remote locations. Edge computing can reduce the amount of data that needs to be sent across a network for processing. This can potentially result in significant cost savings. Kubernetes, with its ability to efficiently manage and scale applications, can further enhance these cost savings.

Finally, there is the potential for innovation and competitive advantage. The use of edge Kubernetes variants can enable new types of applications and services, leading to a competitive advantage. For example, it can enable more effective use of AI and analytics at the edge, leading to more insightful and timely decision-making. In a competitive commercial landscape, the ability to innovate and gain a competitive edge can be the difference between success and failure.

It is important to note that while Kubernetes offers many benefits for edge computing, it also introduces complexity and requires specific skills to manage effectively. Therefore, organizations need to carefully evaluate their needs and capabilities before deciding to adopt edge Kubernetes variants. Managed Kubernetes solutions can help address some of these challenges by shifting the operational burden of maintaining Kubernetes clusters to a vendor. This can help businesses to effectively leverage the benefits of Kubernetes while minimizing the associated challenges.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of a Kubernetes for edge computing solution deployment to the business, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a Kubernetes for edge computing solution, we provide an overall Sector Adoption Score (Figure 1) of 3.8 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a Kubernetes for edge computing solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for Kubernetes for edge computing are explained in more detail in the Sector Brief section that follows.

Key Criteria for Evaluating Kubernetes for Edge Computing Solutions

Sector Adoption Score

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Figure 1. Sector Adoption Score for Kubernetes for Edge Computing

This is the second year that GigaOm has reported on the Kubernetes for edge computing space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Key Criteria report highlights the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) for selecting an effective Kubernetes for edge computing solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the market, identify leading Kubernetes for edge computing offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The post GigaOm Key Criteria for Evaluating Kubernetes for Edge Computing Solutions appeared first on Gigaom.

Chester Conforte, Author at Gigaom

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Sector Adoption Score

DetersAdoption

DiscouragesAdoption

MeritsConsideration

EncouragesAdoption

CompelsAdoption

GIGAOM KEY CRITERIA AND RADAR REPORTS

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption