Matt Jallo, Author at Gigaom

GigaOm Radar for SaaS Management Platforms (SMPs)

Matt Jallo — Fri, 07 Jun 2024 15:00:33 +0000

Companies are adopting software-as-a-service (SaaS) applications at an ever-increasing rate to achieve their digital transformation goals. SaaS applications currently represent half or more of application inventories, and this demands an integrated, data-driven operations model to help organizations manage SaaS application spend, vendor sourcing, IT operations, and security compliance. A SaaS management platform (SMP) can help.

To be considered full-featured, an SMP should enable SaaS financial operations (SaaSFinOps) to handle spend and contract renewal, as well as SaaS operations (SaaSOps) for user lifecycle and sensitive data management. Most SMPs started with a focus on one or the other of these areas and are now evolving to support both. They’re doing so by continuing to mature SMP foundations while investing in intelligence and emerging capabilities. The expectation is that solutions that deliver a full-featured platform will see wider adoption, while those that do not will become niche providers or strategically differentiate into other spaces like identity risk management.

Full-featured SMPs enable a cross-functional, integrated SaaS application operating model that provides several benefits, including:

Visibility of SaaS application usage, spend, security risks, and entitlements.
Seamless integration for discovering and managing applications.
Cost savings from reducing underutilized and redundant applications.
Operational efficiencies and governance enabled by automated workflows and low-code development tools.
On-time contract renewals with optimized entitlements aligned to application usage.
Reduced security risks through discovery and remediation of unauthorized applications and exposed sensitive data.
Continuous improvement enabled by insights, recommendations, benchmarks, and machine learning (ML).
Increased user engagement, productivity, and satisfaction through use of an application catalog, along with sentiment surveys, intelligent provisioning recommendations, collaboration analytics, and intuitive integration with commonly used collaboration tools.

SMPs are generally priced per user seat based on the number of users in the company’s identity provider (IdP) directory. In many cases, they offer a base tier, then higher tiers or add-ons for premium features. The ability to negotiate depends on the volume of users and the level at which the vendor sets the entry point. It’s important to map the desired features to each vendor’s pricing model and evaluate relative differences in pricing, as well as the floor and ceiling pricing across vendors. This market is still evolving, so pricing when renewing subscriptions is likely to change. Most vendors bundle basic support in the per-seat license costs. Some provide managed services for data curation, integration, and vendor management at additional cost.

This is our third year evaluating the SMP 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 10 of the top SMPs 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 SaaS management offerings, 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.

The post GigaOm Radar for SaaS Management Platforms (SMPs) appeared first on Gigaom.

GigaOm Key Criteria for Evaluating SaaS Management Platforms (SMPs)

Matt Jallo — Wed, 22 May 2024 18:15:28 +0000

Software as a service (SaaS) applications represent 50% or more of most companies’ application portfolios. These applications require new operating procedures for managing spending, contract renewals, user lifecycles, and security controls.

As Figure 1 shows, SaaS application management platforms (SMPs) enable more efficient operations by consolidating cost and application usage data through integration, creating an application inventory, identifying contract entitlements and cost savings, automating operational lifecycles, and enforcing security controls.

Figure 1. SMP Functions

SaaS applications require standard processes to address spending, operational, and security challenges. Unnecessary costs may be incurred by over-committing to contract entitlements or maintaining redundant or underused applications. Operational overhead can snowball when access, licensing, and user requests are managed manually with no time for proactive trimming the inventory of existing applications. Security risks escalate without proactive monitoring and remediation to protect sensitive data being stored in the applications.

Small-to-medium businesses (SMBs) and large enterprises alike benefit from investing in an SMP:

SMPs use discovery to create an application catalog with visibility into all applications in use. The catalog enables collaboration and shared accountability across finance, IT, security, and sourcing departments for cost savings and operational efficiency. It is enriched with application spend, licensing, and usage data obtained directly from the managed applications.

Spend management enables cost savings by identifying opportunities to reduce application usage. Contract management enables cost savings by determining where and when to cut or cross-level an entitlement.

Operational efficiencies are achieved by using workflow automation to offload operations from people performing repetitive tasks. Security risks are reduced by leveraging application integration to identify locations where sensitive data is stored and using workflow automation to remediate access to exposed sensitive data.

Business Imperative
SaaS applications are easy to start using; in many cases all that is needed is a credit card and an internet connection. While this is a great model in terms of deployment and accounting, it makes it more difficult for IT to manage the application portfolio. The effects of this can be severe; at times, organizations may be paying too much due to a lack of volume licensing or lax management of unused licenses, or an inability to anticipate usage may lead to hardware and networking shortfalls. Even worse, sensitive company data may make its way into the wrong hands or properly secured identity providers go unused and no record is kept of access to important systems.

An inability to manage an organization’s SaaS applications makes it even more difficult for IT to partner with business units. Beyond saving money and mitigating risk, SaaS management platforms make it easy for employees to access the software that they need when they need it, alleviating a common frustration that organizations have experienced in the age of digital work.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of an SMP 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 SMP, we provide an overall Sector Adoption Score (Figure 2) of 4.6 out of 5, with 5 indicating the strongest possible recommendation to adopt. This suggests that an SMP is a very credible candidate for deployment and worthy of thoughtful consideration.

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

Key Criteria for Evaluating SMPs

Sector Adoption Score

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Figure 2. Sector Adoption Score for SMPs

This is the third year that GigaOm has reported on the SMP 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 SMP. 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 SMPs, 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 SaaS Management Platforms (SMPs) appeared first on Gigaom.

Save Money and Increase Performance on the Cloud

Matt Jallo — Wed, 15 May 2024 13:13:24 +0000

One of the most compelling aspects of cloud computing has always been the potential for cost savings and increased efficiency. Seen through the lens of industrial de-verticalization, this clear value proposition was at the core of most organizations’ decision to migrate their software to the cloud.

The Value Proposition of De-Verticalization

The strategic logic for de-verticalization is illustrated by the trend which began in the 1990s of outsourcing facilities’ maintenance and janitorial services.

A company that specializes in–let’s say–underwriting insurance policies must dedicate its mindshare and resources to that function if it expects to compete at the top of its field. While it may have had talented janitors with the necessary equipment on staff, and while clean facilities are certainly important, facilities maintenance is a cost center that does not provide a strategic return on what matters most to an insurance company. Wouldn’t it make more sense for both insurance and janitorial experts to dedicate themselves separately to being the best at what they do and avail those services to a broader market?

This is even more true for a data center. The era of verticalized technology infrastructure seems largely behind us. Though it’s a source of nostalgia for us geeks who were at home among the whir of the server rack fans, it’s easy enough to see why shareholders might have viewed it differently. Infrastructure was a cost center within IT, while IT as a whole is increasingly seen as a cost center.

The idea of de-verticalization was first pitched as something that would save money and allow us to work more efficiently. The more efficient part was intuitive, but there was immediate skepticism that budgets would actually shed expenses as hoped. At the very least it would be a long haul.

The Road to Performance and Cost Optimization

We find ourselves now somewhere in the middle of that long haul. The efficiencies certainly have come to pass. Having the build script deploy a new service to a Kubernetes cluster on the cloud is certainly nicer than waiting weeks or months for a VM to be approved, provisioned, and set up. But while the cloud saves the company money in the aggregate, it doesn’t show up as cheaper at the unit level. So, it’s at that level where anything that can be shed from the budget will be a win to celebrate.

This is a good position to be in. Opportunities for optimization abound under a fortuitous new circumstance: the things that technologists care about, like performance and power, dovetail precisely with the things that finance cares about, like cost. With the cloud, they are two sides of the same coin at an almost microscopic level. This trend will only accelerate.

To the extent that providers of computational resources (whether public cloud, hypervisors, containers, or any self-hosted combination) have effectively monetized these resources on a granular level and made them available a la carte, performance optimization and cost optimization sit at different ends of a single dimension. Enhancing a system’s performance or efficiency will reduce resource consumption costs. However, cost reduction is limited by the degree to which trade-offs with performance are tolerable and clearly demarcated. Cloud resource optimization tools help organizations strike the ideal balance between the two.

Choosing the Right Cloud Resource Optimization Solution

With that premise in mind, selecting the right cloud resource optimization solution should start by considering how your organization wants to approach the problem. This decision is informed by overall company philosophy and culture, what specific problems or goals are driving the initiative, and an anticipation of where overlapping capabilities may fulfill future business needs.

If the intent is to solve existing performance issues or to ensure continued high availability at future scale while knowing (and having the data to illustrate) you are paying no more than is necessary, focus on solutions that lean heavily into performance-oriented optimization. This is especially the case for companies that are developing software technology as part of their core business.

If the intent is to rein in spiraling costs or even to score some budgeting wins without jeopardizing application performance, expand your consideration to solutions that offer a broader FinOps focus. Tools with a FinOps focus tend to emphasize informing engineers of cost impacts, and may even make some performance tuning suggestions, but they are overall less prescriptive from an implementation standpoint. Certain organizations may find this approach most effective even if they are approaching the problem from a performance point of view.

Now that many organizations have successfully migrated large portions of their application portfolio to the cloud, the remaining work is largely a matter of cleaning up and keeping the topology tidy. Why not trust that job to a tool that is purpose-made for optimizing cloud resources?

Next Steps

To learn more, take a look at GigaOm’s cloud resource optimization 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.

The post Save Money and Increase Performance on the Cloud appeared first on Gigaom.

GigaOm Radar for Cloud Resource Optimization

Matt Jallo — Mon, 06 May 2024 15:00:58 +0000

Cloud resources that are not optimized can prove costly. Cloud resource optimization solutions provide a holistic view of an organization’s public or private cloud infrastructure. They deliver resource configuration suggestions that balance cost, performance, and other objectives. The most valuable of these solutions provide effective and reliable resource configuration recommendations, integrate into deployment pipelines, and enhance management processes.

Furthermore, as cloud usage continues to outpace the rate at which IT operational analysts can be hired, automated optimization of these resources directly impacts the bottom-line of the cloud bill and the effectiveness of existing IT staff (who are freed up to work on higher-value business objectives). Taking an hour to determine whether a machine would benefit from less or more vCPU may seem hardly worth the time and effort but may reveal an imbalance that can generate significant excess spending or risk if neglected at scale. This is the kind of task the analytics engines within resource management solutions can handle effectively and expediently.

As you evaluate cloud resource optimization solutions, it’s important to keep the following in mind:

Cloud resource optimization is closely aligned with the financial operations (FinOps) and cloud management platform (CMP) tooling categories, and solutions may lean in one of those directions with a strategy of providing a single solution.

Private cloud, public cloud, and Kubernetes resources all require oversight and optimization, and solutions tend to be stronger in one area than another. You’ll need to determine where your resource challenges exist today and what improvements you want to have made 12 to 18 months from now.

It can be beneficial to delegate resource and cost optimization to individual teams, with some limited central oversight. Individual teams are closely aligned with the performance needs of their applications and, if motivated properly and given the right tools, will ensure a balance is reached between cost and performance.

This is our third year evaluating the cloud resource optimization 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 11 of the top cloud resource optimization solutions in the market 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 category and its underlying technology, identify leading cloud resource optimization 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 Cloud Resource Optimization appeared first on Gigaom.

GigaOm Key Criteria for Evaluating Cloud Resource Optimization Solutions

Matt Jallo — Tue, 16 Apr 2024 19:23:09 +0000

Cloud resource optimization solutions provide a holistic view across an organization’s public or private cloud infrastructure. They deliver and provision resource configuration recommendations that balance cost, performance, and other objectives.

Before the rapid adoption of public cloud infrastructures, it was common for application teams to overprovision their virtual machine (VM) resource requests, since provisioning could take a long time and they had little insight into future requirements. IT operations teams could manage the cost and resource overprovisioning issues by overallocating their infrastructure, and did so often by an order of magnitude.

Organizations today provision many more cloud resources across public clouds. These services effectively offer the opportunity to pay only for what is needed and dynamically scale up or down as the need changes. This often presents challenges with cost and resource usage. The question of precisely how much is needed becomes relevant in a new way. Application teams still overprovision their cloud requests, while public clouds charge for exactly what is provisioned, which leads to waste. Sometimes this lack of diligence is just an old habit, but even with effective processes and controls, it can be difficult to safely and precisely predict runtime requirements by hand. Any degree of overprovisioning translates into a wasted expense, but when managed by hand, it would be imprudent to not leave a comfortable margin for safety. After all, availability or performance issues would ultimately impact the bottom line more profoundly than overspending on a safety margin for resources.

Application teams deploy many more systems than they used to, as the provisioning time is instantaneous, and there might be very little oversight of their spending for cloud resources. Modern software architecture demands greater subdivision of monolithic applications into microservices, and this multiplies the potential overhead expenses incurred by safe resource provisioning. Automating resource configuration is a clear solution to this problem because it assures users that their software can effectively scale to any level without spending a dime on unneeded resources. This would be anywhere from impractical to impossible to do by hand.

Business Imperative
As organizations continue to deploy cloud resources across multiple public and private clouds, each with potentially different resource monitoring solutions, they need solutions that can provide a holistic view across all cloud resources, which will identify resource configuration optimizations that balance performance and cost. This includes the need to make specific recommendations and potentially act on those recommendations without burdening operations engineers.

Cloud resource optimization solutions have been around for years and provide reliable and mature management of traditional IaaS (such as the dynamic provisioning and configuration of VMs). For large organizations that have migrated legacy software from private data centers, this continues to be the most relevant capability. As cloud-native software development proceeds, it is important that these solutions keep pace with evolving paradigms, such as containerized deployments, and platform as a service (PaaS) architectures, such as serverless APIs, which inherently provide a more solid foundation for scale. This year’s report will take a closer look at that evolution.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of a cloud resource optimization 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 cloud resource optimization solution, we provide an overall Sector Adoption Score (Figure 1) of 4.4 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a cloud resource optimization solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for cloud resource optimization are explained in more detail in the Sector Brief section that follows.

Key Criteria for Evaluating Cloud Resource Optimization Solutions

Sector Adoption Score

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Figure 1. Sector Adoption Score for Cloud Resource Optimization

This is the third year that GigaOm has reported on the cloud resource optimization 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 cloud resource optimization solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the category and its underlying technology, identify leading cloud resource optimization 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 Cloud Resource Optimization Solutions appeared first on Gigaom.

Insights from Runtime: How Kubernetes Resource Management Influences Cloud Development

Matt Jallo — Mon, 18 Mar 2024 20:31:50 +0000

The evolution of software portability converges naturally with the proliferation of easily scalable infrastructure made available by cloud computing providers. The clearest example is the variety of managed Kubernetes services, which often share a footprint with clusters on infrastructure as a service (IaaS) virtual machines or even on-premises hardware in large organizations. Most architectural roadmaps call for some form of cloud-native greenfield development, along with the cloud enablement of legacy software, as a digital migration strategy.

Playing Catch Up

Virtually all businesses that have existing investments in custom software wind up in some form of perpetual catch-up game with the state of the art. It’s the nature of the IT business, and it is more important to close that gap effectively than quickly. After all, we should expect to reap benefits from this continual technological advancement. In the absence of tangible rewards and improved delivery of specified outcomes, chasing the vanguard has the tendency to lead us in arbitrary directions.

One hiccup many organizations have encountered in deploying software to container clusters orchestrated by Kubernetes is unexpected increases in cost, especially cloud costs. In many circumstances, it was anticipated that such a migration might increase costs and complexity in the near term, but it was considered part of a beneficial strategic trade off, and/or it was expected that these things could otherwise be brought quickly under control.

Put a pin on the architectural roadmap to mark this spot, halfway down a long desert highway and almost out of gas. After six months of work, you’ve migrated an important API to a bank of microservices on a managed Kubernetes service. There are no new features, it costs more to run, but it is sailing way up there in the cloud. Who gets to tell the CIO?

Quick Fixes and Long-Term Solutions are Here

If you find yourself in this situation, you’re not alone and there is good news. There are likely many opportunities to pare down costs without impacting performance. They range from low hanging fruits that in many cases can bring costs back to ground level within weeks to new opportunities for performance and availability increases made possible through deep insight into usage patterns.

There are a variety of great Kubernetes resource management solutions on the market that provide a turnkey solution. They can be used both as an immediate remedy if the problem is acute and as an engine for continuous improvement.

The low hanging fruit is typically a matter of fine-tuning the way workloads are configured and provisioned. In the extreme (but still prevalent) example, developers put off this “tuning” as a second phase. Resource configuration may have been an afterthought and neglected entirely, but in most cases, it really is prudent to “implement first, optimize second.”

A common response to these unexpected cost increases in the cloud has been for organizations to enact policies or bureaucratic measures to revisit how these resources have been provisioned and get a handle on the situation. While such policies may be a valuable addition to the continuous delivery process, they will not take you very far toward solving the problem. The ideal balance for meeting performance objectives without over-provisioning resources is not obvious and frequently changes.

Automation Supercharges the Utility of Identified Trends and Patterns in Data

Everyone is learning as they go and beyond high level prescriptions, it is difficult and impractical to achieve a reliable fine tune without automation. Kubernetes resource management solutions offer automatic analysis and management of resources from the starting gate and exceed the capabilities of the underlying platforms in fundamental ways.

One example is through the application of AI/ML. While the capabilities of AI/ML methodologies are burgeoning by the day, for two decades they have been a reliable way of analyzing big sets of data and spotting patterns or trends that would be difficult to intuit or identify through alternative methods. By analyzing the usage patterns, resource consumption, important metrics like CPU, memory, and even custom application events, they automatically balance Kubernetes resources in a tight loop. These tools ensure that you are paying only for what you need.

Running Smarter and More Efficiently

Paying only for what you need is a key advantage of cloud computing. While it may have been possible 20 years ago to manage resources in a tight loop of analysis, there would have been little benefit because capacity was necessarily provisioned to the highest anticipated need. With cloud computing, you can use as little or as much as you need when you need it. However, with some types of cloud-native development, you still need to know how much you need, and as it turns out, that’s not a readily available factor in the equation.

Realtime insight into runtime resource requirements will, over time, shift left into the development process and architectural considerations. Architecturally, it is clear why breaking down monolithic designs into portable, ephemeral, and autonomous services permits certain advantages. But it is not always clear how that is best achieved or what unknown overhead tradeoffs lurk in the darkness. Just as a debugger provides inside details to inform a running process and APM tools provide a wealth of visibility into intra operating systems, Kubernetes resource management solutions provide intimate insight into how these systems scale under real world usage. Over time, such knowledge will make its way back into the initial design of the software and help to unlock the inherent benefits of cloud computing.

Next Steps

To learn more, take a look at GigaOm’s Kubernetes resource management Key Criteria and Radar reports. These reports provide a comprehensive view 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.

The post Insights from Runtime: How Kubernetes Resource Management Influences Cloud Development appeared first on Gigaom.

GigaOm Radar for Kubernetes for Edge Computing

Matt Jallo — 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

Matt Jallo — 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.

GigaOm Radar for Kubernetes Resource Management

Matt Jallo — Fri, 16 Feb 2024 16:00:37 +0000

Kubernetes, the increasingly popular container orchestration system, provides an abstraction layer that ensures containerized applications and services can be deployed quickly and run easily across any public or private infrastructure. However, it can also add significant complexity, such as by allowing developers to define or omit their resource requests. Organizations often operate many Kubernetes clusters across public and private infrastructure resources, and capacity management and the proper allocation of resources may be put off to a later stage or neglected entirely. Kubernetes resource management solutions provide a way for developers to shift this important function left in a near-automated fashion, with better results than could be obtained by hand.

Kubernetes resource management platforms analyze resource usage and configuration continuously across all Kubernetes platforms within an organization. These solutions surface configuration issues, guide application and platform owners toward configurations that align with the needs of their business, and optimize the availability of platforms and applications while minimizing infrastructure waste.

Kubernetes resource management is a relatively new space, but one that continues to grow adjacent to the larger cloud management market. Continued advances in artificial intelligence (AI) and machine learning (ML) provide new opportunities for fine-tuning operational parameters and optimizing resource usage and costs, and are a key focus of Kubernetes resource management solutions. Established general-purpose Kubernetes or cloud management platforms focus on the efficient deployment, configuration, DevOps enablement, and runtime management of containerized applications.

While there is significant overlap between more generalized cloud management platforms and Kubernetes resource management solutions, this report focuses on tools that prioritize the optimization of resources (and provide a balance between application availability and cost). These tools tend to provide integrations with other solutions that offer deeper capabilities in DevOps, runtime management, monitoring, and FinOps. Some provide all of these capabilities as components of a single larger platform.

This is our second year evaluating the Kubernetes resource management 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 10 of the top Kubernetes resource management 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 resource management offerings, 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 non-functional 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.

The post GigaOm Radar for Kubernetes Resource Management appeared first on Gigaom.

GigaOm Key Criteria for Evaluating Kubernetes Resource Management Solutions

Matt Jallo — Tue, 23 Jan 2024 22:30:42 +0000

Kubernetes provides an abstraction layer that ensures containerized applications and services can be deployed quickly and run easily across any public or private infrastructure; however, it can also add significant complexity, such as by allowing developers to define or omit their resource requests. Organizations often operate many Kubernetes clusters across public and private infrastructure resources, and capacity management and the proper allocation of resources is put off to a later stage or sometimes neglected entirely. Kubernetes resource management solutions provide a way for developers to shift left this important function in a near-automated fashion, with better results than can be obtained by manual methods.

Kubernetes resource management is a relatively new space, one that continues to grow adjacent to the larger cloud management market. Continued advances in artificial intelligence provide new opportunities for fine tuning operational parameters and optimizing resource usage and costs and have become a key focus of Kubernetes resource management solutions. Established, general-purpose Kubernetes or cloud management platforms focus on the efficient deployment, configuration, DevOps enablement, and runtime management of containerized applications.

While there is significant overlap between more generalized cloud management platforms and Kubernetes resource management solutions, we are focusing on tools that prioritize the optimization of resources (and provide a balance between application availability and cost). These tools tend to provide integrations with other solutions that offer deeper capabilities in DevOps, runtime management, monitoring, and financial operations (FinOps). Some provide all of these capabilities as components of a single larger platform.

Business Imperative
Organizations with a large Kubernetes footprint are likely either paying too much for wasted computational resources or experiencing performance and availability problems with running workloads due to the inherent difficulty of managing this balance manually. The extent to which this imbalance occurs depends on the scale of deployed workloads, how quickly or carefully they were deployed, and how actively those workloads are being managed by skilled operations teams. In any event, better and more responsive results can be achieved by automated resource management platforms than can be achieved by hand. This challenge is likely to grow with time as additional workloads are migrated to Kubernetes or demand for those services increases.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of a Kubernetes resource management solution deployment to their business, this GigaOm Key Criteria report provides a structured assessment of the Kubernetes resource management sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of Kubernetes resource management solutions, we provide an overall Sector Adoption Score (Figure 1) for Kubernetes resource management of 4 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that Kubernetes resource management is a credible candidate for deployment and worthy of thoughtful consideration.

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

Key Criteria for Evaluating Kubernetes Resource Management Solutions

Sector Adoption Score

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Figure 1. Sector Adoption Score for Kubernetes Resource Management

This is the second year that GigaOm has reported on the Kubernetes resource management 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 non-functional requirements (business criteria) for selecting an effective Kubernetes resource management solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the category and its underlying technology, identify leading Kubernetes resource management offerings, 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 non-functional 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.

The post GigaOm Key Criteria for Evaluating Kubernetes Resource Management Solutions appeared first on Gigaom.

Matt Jallo, Author at Gigaom

GigaOm Radar for SaaS Management Platforms (SMPs)

GIGAOM KEY CRITERIA AND RADAR REPORTS

GigaOm Key Criteria for Evaluating SaaS Management Platforms (SMPs)

Key Criteria for Evaluating SMPs

Sector Adoption Score

DetersAdoption

DiscouragesAdoption

MeritsConsideration

EncouragesAdoption

CompelsAdoption

GIGAOM KEY CRITERIA AND RADAR REPORTS

Save Money and Increase Performance on the Cloud

The Value Proposition of De-Verticalization

The Road to Performance and Cost Optimization

Choosing the Right Cloud Resource Optimization Solution

Next Steps

GigaOm Radar for Cloud Resource Optimization

GIGAOM KEY CRITERIA AND RADAR REPORTS

GigaOm Key Criteria for Evaluating Cloud Resource Optimization Solutions

Key Criteria for Evaluating Cloud Resource Optimization Solutions

Sector Adoption Score

DetersAdoption

DiscouragesAdoption

MeritsConsideration

EncouragesAdoption

CompelsAdoption

GIGAOM KEY CRITERIA AND RADAR REPORTS

Insights from Runtime: How Kubernetes Resource Management Influences Cloud Development

Playing Catch Up

Quick Fixes and Long-Term Solutions are Here

Automation Supercharges the Utility of Identified Trends and Patterns in Data

Running Smarter and More Efficiently

Next Steps

GigaOm Radar for Kubernetes for Edge Computing

GIGAOM KEY CRITERIA AND RADAR REPORTS

GigaOm Key Criteria for Evaluating Kubernetes for Edge Computing Solutions

Key Criteria for Evaluating Kubernetes for Edge Computing Solutions

Sector Adoption Score

DetersAdoption

DiscouragesAdoption

MeritsConsideration

EncouragesAdoption

CompelsAdoption

GIGAOM KEY CRITERIA AND RADAR REPORTS

GigaOm Radar for Kubernetes Resource Management

GIGAOM KEY CRITERIA AND RADAR REPORTS

GigaOm Key Criteria for Evaluating Kubernetes Resource Management Solutions

Key Criteria for Evaluating Kubernetes Resource Management Solutions

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

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption