Kubernetes remains a complex platform that receives frequent updates and new features. For IT organizations accustomed to the ease of use and stability of technologies such as virtualization, this level of flux is a concern. To operate within the existing high standards of availability and security, organizations must keep up to date with the latest Kubernetes version and surrounding ecosystem projects, ensuring that security patches, API specifications, and performance improvements are realized as soon as possible. The reality is that most organizations are not geared up to work at this pace; managing existing workload demands and learning new skills simultaneously is a challenge. Managing an ever-evolving platform like Kubernetes is a demanding task, and on top of that, operational complexities in this type of platform can create reliability issues if not addressed correctly.

IT organizations favor containers because they enable true application portability, and Kubernetes is the right platform to manage container-based applications correctly, at scale. Kubernetes realizes the possibility of true hybrid-cloud deployments, enabling organizations to build on existing data center solutions and expand into cloud environments seamlessly.

The easiest way to capture all the advantages of Kubernetes and none of the complexity that comes with it is to choose the right managed Kubernetes services. There are plenty of options in the market at the moment and although rooted in the same code base, they present several differences in technical features, consumption models, and support aspects.

Fortunately, Kubernetes didn’t suffer the proliferation of different distributions or multiple competing projects that occurred early on with the Linux OS. The core of Kubernetes is the same on all platforms, and they share the same commands, structure, and way of operating, making seamless application portability a reality.

The post GigaOm Radar for Evaluating Managed Kubernetes Solutions appeared first on Gigaom.

Connectivity back to centralized data centers may be unreliable or intermittent with mobile devices. Running the applications closer to the device and transferring only processed data and results back to a central location allows greater flexibility and faster response times. Cluster footprints can be as small or as large as necessary at the edge and can take advantage of the ruggedized form factors available to suit less hospitable environments. Most common use cases for edge Kubernetes can be found in data analytics, AI/ML workflows, image and video processing, robotic process automation, IoT, and other applications that benefit from speed in data processing and manipulation (see Figure 1).

Figure 1. Edge Kubernetes Overview

The two most common approaches to edge Kubernetes are:

• Software defined: The software-defined approach enables the use of a wide range of hardware devices as well as of existing infrastructure components. It allows a greater degree of flexibility, so you can mix and match the hardware requirements to the location.

• Appliance/specialized hardware: This approach is similar to using hyperconverged infrastructure appliances. It combines compute, storage, and networking resources with a software platform that manages and deploys the edge clusters. It may also include specialized hardware designed for AI/ML workloads that include GPUs or other dedicated hardware for a specific use case.

Solutions may support a combination of these two models. Software-defined deployments are usually beneficial when existing infrastructure is in place, possibly even existing hypervisor platforms, should a bare metal deployment not be required. The latter can be beneficial for Greenfield deployments and scenarios where bare metal deployments are not possible. Appliance deployments can also help provide a single point of contact for purchasing and supporting hardware.

How We Got Here

Over the last two decades, computing technology has improved dramatically. In fact, modern smartphone devices carry more processing power than the average data center servers used at the turn of the century. Along with this, the footprint of computing devices has shrunk. The advent of the Raspberry Pi and Next Unit of Computing platforms introduced low-power devices that can run enterprise-class applications in remote locations without sacrificing performance and stability.

Other key factors in the development of the edge and IoT space were improvements in connectivity, such as 4/5G mobile connectivity, and SD-WAN solutions that allowed for enterprise control and connectivity over consumer-grade internet connections.

Initial efforts in edge computing started within the virtualization space; however, the overhead of virtual machines and hypervisor software can make deployment at scale difficult. Deployments were limited to sites that could host enough resources to run the necessary applications, which often required additional power and cooling at the edge location. Then, along came container technology, which allowed the deployment of a minimal number of services and associated system files to support the application. The footprint required to deploy multiple applications was reduced from traditional pizza box-style servers to devices you can fit in your hand.

In recent years, Kubernetes emerged as the de facto standard for container orchestration, with wide adoption across multiple hyperscale cloud and on-premises solution providers. Bringing the scalability and orchestration capabilities of Kubernetes to the edge made running hundreds or even thousands of globally distributed data centers a reality.

The post GigaOm Sonar Report for Edge Kubernetes appeared first on Gigaom.

Kubernetes remains a complex platform that receives frequent updates and new features. For IT organizations accustomed to the ease of use and stability of technologies such as virtualization, this level of flux is a concern. In order for organizations to operate within the existing high standards of availability and security, they must keep up to date with the latest Kubernetes version and surrounding ecosystem projects, ensuring that security patches, API specifications, and performance improvements are realized as soon as possible. The reality is that most organizations are not geared up to work at this pace; managing existing workload demands and learning new skills simultaneously is a challenge. Managing an ever-evolving platform like Kubernetes is a demanding task, and on top of that, operational complexities in this type of platform can create reliability issues if not addressed correctly.

IT organizations favor containers because they enable true application portability, and Kubernetes is the right platform to manage container-based applications correctly, at scale. Kubernetes realizes the possibility of true hybrid cloud deployments, enabling organizations to build on existing data center solutions and expand into cloud environments seamlessly.

The easiest way to get all the advantages of Kubernetes and none of the complexity that comes with it is to choose the right managed Kubernetes services. There are plenty of options in the market at the moment and, although rooted in the same code base, they present several differences in technical features, consumption models, and support aspects.

Fortunately, Kubernetes didn’t see the proliferation of different distributions or many competing projects that occurred early on with the Linux OS. The core of Kubernetes is the same on all platforms, and they share the same commands, structure, and way of operating, making seamless application portability a reality.

In this report, we analyze the important features of managed Kubernetes systems to see how well they respond to enterprise needs and to enable organizations to evaluate specific solutions based on their own requirements.

The post Key Criteria for Evaluating Managed Kubernetes Solutions appeared first on Gigaom.