Anton Palyonko

Anton Palyonko

1605057354

Kubernetes in the Context of On-premises Edge and Network Edge Computing

For cloud native edge computing applications there is a need for facilitating data processing at the edge of the network to stay at proximity to the source of events with characteristics of ultra-low latency and high bandwidth connectivity, enabling innovative and cutting-edge applications such as video analytics, virtual reality, the Internet of Things (IoT) and self-driving cars.

This webinar will introduce you to the Open Network Edge Services Software (OpenNESS), an open source, reference cloud-native architecture that enables the Cloud Services Providers (CSPs) and Communication Services Providers (CoSPs) to deploy end-to-end on-premises and network edge services leveraging various CNCF ingredients with Kubernetes at its core - an implementation that delivers recipes for generating new network edge capabilities such as:

  • Abstracted platform & network complexity
  • Enhanced dataplane
  • Hardware accelerators management
  • Dynamic discovery & optimal apps/services placement
  • Open integration with Cloud Native Functions (CNFs)

#kubernetes #devops

What is GEEK

Buddha Community

Kubernetes in the Context of On-premises Edge and Network Edge Computing
Christa  Stehr

Christa Stehr

1602964260

50+ Useful Kubernetes Tools for 2020 - Part 2

Introduction

Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.

According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.

(State of Kubernetes and Container Security, 2020)

And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.

(State of Kubernetes and Container Security, 2020)

#blog #tools #amazon elastic kubernetes service #application security #aws kms #botkube #caylent #cli #container monitoring #container orchestration tools #container security #containers #continuous delivery #continuous deployment #continuous integration #contour #developers #development #developments #draft #eksctl #firewall #gcp #github #harbor #helm #helm charts #helm-2to3 #helm-aws-secret-plugin #helm-docs #helm-operator-get-started #helm-secrets #iam #json #k-rail #k3s #k3sup #k8s #keel.sh #keycloak #kiali #kiam #klum #knative #krew #ksniff #kube #kube-prod-runtime #kube-ps1 #kube-scan #kube-state-metrics #kube2iam #kubeapps #kubebuilder #kubeconfig #kubectl #kubectl-aws-secrets #kubefwd #kubernetes #kubernetes command line tool #kubernetes configuration #kubernetes deployment #kubernetes in development #kubernetes in production #kubernetes ingress #kubernetes interfaces #kubernetes monitoring #kubernetes networking #kubernetes observability #kubernetes plugins #kubernetes secrets #kubernetes security #kubernetes security best practices #kubernetes security vendors #kubernetes service discovery #kubernetic #kubesec #kubeterminal #kubeval #kudo #kuma #microsoft azure key vault #mozilla sops #octant #octarine #open source #palo alto kubernetes security #permission-manager #pgp #rafay #rakess #rancher #rook #secrets operations #serverless function #service mesh #shell-operator #snyk #snyk container #sonobuoy #strongdm #tcpdump #tenkai #testing #tigera #tilt #vert.x #wireshark #yaml

Zelma  Gerlach

Zelma Gerlach

1621616520

Edge Computing: Device Edge vs. Cloud Edge

It sometimes makes sense to treat edge computing not as a generic category but as two distinct types of architectures: cloud edge and device edge.

Most people talk about edge computing as a singular type of architecture. But in some respects, it makes sense to think of edge computing as two fundamentally distinct types of architectures: Device edge and cloud edge.

Although a device edge and a cloud edge operate in similar ways from an architectural perspective, they cater to different types of use cases, and they pose different challenges.

Here’s a breakdown of how device edge and cloud edge compare.

Edge computing, defined

First, let’s briefly define edge computing itself.

Edge computing is any type of architecture in which workloads are hosted closer to the “edge” of the network — which typically means closer to end-users — than they would be in conventional architectures that centralize processing and data storage inside large data centers.

#cloud #edge computing #cloud computing #device edge #cloud edge

Maud  Rosenbaum

Maud Rosenbaum

1601051854

Kubernetes in the Cloud: Strategies for Effective Multi Cloud Implementations

Kubernetes is a highly popular container orchestration platform. Multi cloud is a strategy that leverages cloud resources from multiple vendors. Multi cloud strategies have become popular because they help prevent vendor lock-in and enable you to leverage a wide variety of cloud resources. However, multi cloud ecosystems are notoriously difficult to configure and maintain.

This article explains how you can leverage Kubernetes to reduce multi cloud complexities and improve stability, scalability, and velocity.

Kubernetes: Your Multi Cloud Strategy

Maintaining standardized application deployments becomes more challenging as your number of applications and the technologies they are based on increase. As environments, operating systems, and dependencies differ, management and operations require more effort and extensive documentation.

In the past, teams tried to get around these difficulties by creating isolated projects in the data center. Each project, including its configurations and requirements were managed independently. This required accurately predicting performance and the number of users before deployment and taking down applications to update operating systems or applications. There were many chances for error.

Kubernetes can provide an alternative to the old method, enabling teams to deploy applications independent of the environment in containers. This eliminates the need to create resource partitions and enables teams to operate infrastructure as a unified whole.

In particular, Kubernetes makes it easier to deploy a multi cloud strategy since it enables you to abstract away service differences. With Kubernetes deployments you can work from a consistent platform and optimize services and applications according to your business needs.

The Compelling Attributes of Multi Cloud Kubernetes

Multi cloud Kubernetes can provide multiple benefits beyond a single cloud deployment. Below are some of the most notable advantages.

Stability

In addition to the built-in scalability, fault tolerance, and auto-healing features of Kubernetes, multi cloud deployments can provide service redundancy. For example, you can mirror applications or split microservices across vendors. This reduces the risk of a vendor-related outage and enables you to create failovers.

#kubernetes #multicloud-strategy #kubernetes-cluster #kubernetes-top-story #kubernetes-cluster-install #kubernetes-explained #kubernetes-infrastructure #cloud

Anton Palyonko

Anton Palyonko

1605057354

Kubernetes in the Context of On-premises Edge and Network Edge Computing

For cloud native edge computing applications there is a need for facilitating data processing at the edge of the network to stay at proximity to the source of events with characteristics of ultra-low latency and high bandwidth connectivity, enabling innovative and cutting-edge applications such as video analytics, virtual reality, the Internet of Things (IoT) and self-driving cars.

This webinar will introduce you to the Open Network Edge Services Software (OpenNESS), an open source, reference cloud-native architecture that enables the Cloud Services Providers (CSPs) and Communication Services Providers (CoSPs) to deploy end-to-end on-premises and network edge services leveraging various CNCF ingredients with Kubernetes at its core - an implementation that delivers recipes for generating new network edge capabilities such as:

  • Abstracted platform & network complexity
  • Enhanced dataplane
  • Hardware accelerators management
  • Dynamic discovery & optimal apps/services placement
  • Open integration with Cloud Native Functions (CNFs)

#kubernetes #devops

What Is Edge Computing

The concept of Edge Computing is inspired by CDN technology. CDN stands for Content Delivery Networks. A CDN typically works to bring the content (images, video, script files) on the Internet closer to its users. This helps faster streaming of content with proper load handling. This is how YouTube, Netflix, etc delivery content to different regions without getting overwhelmed by the massive data rates required for streaming services.

Edge Computing brings both data and computations closer to its users!

In Edge Computing the data and computation are localized so that the response times and bandwidth requirements are significantly reduced. This also supports Green Computing, where the computations are done with minimal use of resources.

Origin of Edge Computing

With the increase of network-attached devices due to the popularity of IoT, the amount of data generated has increased exponentially. This increase demands massive computing and analytics at data centres which increase the utilization of bandwidth.

In Edge Computing the computational component is pushed towards the ends of the network, i.e. the users’ end. This eliminates the need for servers to continuously work on behalf of each user, but rather invest their time on analytics at a much higher level (e.g. community analytics, community-based recommendations, etc). So how would personal data be processed?

Data is processed at the users’ end using the connected devices such as smart phones, smart home hubs, smart TVs, etc.

For example, your sleep data is processed within your phone, where most data for this process is collected either from your phone, watch or using all devices with the same account logged in. Also, iPhone face detection runs completely offline and learns continuously based on your facial changes that take place over time.

#computer-science #networking #edge-computing #data-science