1598314860
Prevent Downtime with Proper Kubernetes Resource Planning
Why you need Resource Planning
Out of the box, Kubernetes does an excellent job of managing containers. Your Kubernetes cluster may just work perfectly fine right now. Things are humming along perfectly if you just started your container workflow. As your teams grow in size and your cluster hosts more nodes, stability issues will start to surface.
By default, container pods run with unbounded limits. A single pod in the system is capable of consuming as much CPU and memory as available on that node. If you deploy a large application on a node with limited resources, it is possible that the lack of memory or CPU resources will degrade the application and nearby apps within that node. Kubernetes may kill pods as the node reaches out of memory. Worse, it may start throttling critical vital applications to support lesser sensitive ones. An application with an inefficient codebase can easily spiral out of control. Poorly coded or rogue microservices can jeopardize the entire ecosystem through replication. Next, different teams may be spinning up more replicas than they are entitled to. In short, a wide variety of issues will crop up if you don’t properly manage the resources of your growing container platform.
Example of a database application consuming 100% CPU utilization over a 40 minute period. Unchecked, the node suffered from severe degradation of services. Using Limits, K8 has the capabilities to keep rogue applications in line to restore stability.
With a little effort, these issues can be prevented with resource planning. By using Resource requests and limits, users can impose restrictions to a single pod or a group of pods in a namespace. By assigning defined values, you can ensure critical apps have the highest level of Quality of Service (QoS) they deserve.
Understanding Kubernetes Requests vs Limits
Kubernetes employs requests and limits to control resources. Requests are guaranteed resources that a container is entitled to use. Limits, on the other hand, are the maximum resources or threshold a container can use. After reaching the limits, containers will be restricted. If a container requests a resource, Kubernetes will only schedule it on an available node that can provide those resources. These resources and limit are defined in the standard YAML configuration of your containers.
In Kubernetes, there are two types of resources: CPU and Memory. CPU is measured in core units, and memory is specified in bytes.
CPU
CPU resources are measured in millicore. If a node has 2 cores, the node’s CPU capacity would be represented as 2000m. The unit suffix m stands for “thousandth of a core.”
1000m or 1000 millicore is equal to 1 core. 4000m would represent 4 cores. 250 millicore per pod means 4 pods with a similar value of 250m can run on a single core. On a 4 core node, 16 pods each having 250m can run on that node.
Next, unless the apps require multi-core processing such as a multi-threaded database, the best practice is to define to 1000 or below. Then, run more replicas to scale out those applications. It is important to note, pods will never be scheduled if they are defined more than the node’s capacity. A pod cannot have a definition of 3000m on a 2 core node.
Keep in mind, CPU is a compressible resource. In simple terms, applications will start throttling once they hit the CPU limits. Throttling can adversely affect your application’s performance by making it run slower. Kubernetes will not terminate those apps. Hence, you should take this into consideration as you architect your applications.
Memory
Memory is measured in bytes. However, you can express memory with various suffixes (E,P,T,G,M,K and Ei, Pi, Ti, Gi, Mi, Ki) to express mebibytes (Mi) to petabytes (Pi). Most simply use Mi.
Like CPU, pods will never be scheduled if they require more resources than the capacity of a node. Unlike CPU, memory is not compressible. You can’t make memory run slower or faster like CPU or network throttling. Pods will be terminated if it reaches the memory limit.
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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.
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.
#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
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
1598244600
7 Resource Scheduling Errors and How to Mitigate Them
The project management landscape has drastically changed over the last decade. Emphasis on efficiency and reporting while juggling between several projects simultaneously amid uncertainties is responsible for the change.
These drastic changes often contribute to projects falling through the cracks, mainly due to poor project performance. Lack of proper resource scheduling and planning tools disrupts project performance. According to Gartner, poor project performance has resulted in approximately $50-$150 billion in revenue loss and productivity.
Resources plug into every phase of your project, be it planning, scheduling, and executing. In a shocking revelation by HBR, 1 out of 6 IT projects incurs a schedule overrun by 70%. A logical system that lets you identify and deploy resources exactly when you need ensures project success. This is where resource scheduling can help. First off,
What Is Resource Scheduling?
Resource scheduling is the process of identifying and allocating resources. It is a crucial element of project planning with specified start and end dates for each task in a project. In short, it sets the stage for the intelligent distribution of resources to project tasks.
However, common scheduling errors jeopardize the project plan and ultimately cause project delays.
Let us look at some of the common scheduling blunders and how to mitigate them.
#capacity planning #resource allocation #resource analysis #resource planning #capacity based planning #data analysis
1598314860
Prevent Downtime with Proper Kubernetes Resource Planning
Why you need Resource Planning
Out of the box, Kubernetes does an excellent job of managing containers. Your Kubernetes cluster may just work perfectly fine right now. Things are humming along perfectly if you just started your container workflow. As your teams grow in size and your cluster hosts more nodes, stability issues will start to surface.
By default, container pods run with unbounded limits. A single pod in the system is capable of consuming as much CPU and memory as available on that node. If you deploy a large application on a node with limited resources, it is possible that the lack of memory or CPU resources will degrade the application and nearby apps within that node. Kubernetes may kill pods as the node reaches out of memory. Worse, it may start throttling critical vital applications to support lesser sensitive ones. An application with an inefficient codebase can easily spiral out of control. Poorly coded or rogue microservices can jeopardize the entire ecosystem through replication. Next, different teams may be spinning up more replicas than they are entitled to. In short, a wide variety of issues will crop up if you don’t properly manage the resources of your growing container platform.
Example of a database application consuming 100% CPU utilization over a 40 minute period. Unchecked, the node suffered from severe degradation of services. Using Limits, K8 has the capabilities to keep rogue applications in line to restore stability.
With a little effort, these issues can be prevented with resource planning. By using Resource requests and limits, users can impose restrictions to a single pod or a group of pods in a namespace. By assigning defined values, you can ensure critical apps have the highest level of Quality of Service (QoS) they deserve.
Understanding Kubernetes Requests vs Limits
Kubernetes employs requests and limits to control resources. Requests are guaranteed resources that a container is entitled to use. Limits, on the other hand, are the maximum resources or threshold a container can use. After reaching the limits, containers will be restricted. If a container requests a resource, Kubernetes will only schedule it on an available node that can provide those resources. These resources and limit are defined in the standard YAML configuration of your containers.
In Kubernetes, there are two types of resources: CPU and Memory. CPU is measured in core units, and memory is specified in bytes.
CPU
CPU resources are measured in millicore. If a node has 2 cores, the node’s CPU capacity would be represented as 2000m. The unit suffix m stands for “thousandth of a core.”
1000m or 1000 millicore is equal to 1 core. 4000m would represent 4 cores. 250 millicore per pod means 4 pods with a similar value of 250m can run on a single core. On a 4 core node, 16 pods each having 250m can run on that node.
Next, unless the apps require multi-core processing such as a multi-threaded database, the best practice is to define to 1000 or below. Then, run more replicas to scale out those applications. It is important to note, pods will never be scheduled if they are defined more than the node’s capacity. A pod cannot have a definition of 3000m on a 2 core node.
Keep in mind, CPU is a compressible resource. In simple terms, applications will start throttling once they hit the CPU limits. Throttling can adversely affect your application’s performance by making it run slower. Kubernetes will not terminate those apps. Hence, you should take this into consideration as you architect your applications.
Memory
Memory is measured in bytes. However, you can express memory with various suffixes (E,P,T,G,M,K and Ei, Pi, Ti, Gi, Mi, Ki) to express mebibytes (Mi) to petabytes (Pi). Most simply use Mi.
Like CPU, pods will never be scheduled if they require more resources than the capacity of a node. Unlike CPU, memory is not compressible. You can’t make memory run slower or faster like CPU or network throttling. Pods will be terminated if it reaches the memory limit.
#kubernetes
1601305200
Microsoft Announces General Availability Of Bridge To Kubernetes
Recently, Microsoft announced the general availability of Bridge to Kubernetes, formerly known as Local Process with Kubernetes. It is an iterative development tool offered in Visual Studio and VS Code, which allows developers to write, test as well as debug microservice code on their development workstations while consuming dependencies and inheriting the existing configuration from a Kubernetes environment.
Nick Greenfield, Program Manager, Bridge to Kubernetes stated in an official blog post, “Bridge to Kubernetes is expanding support to any Kubernetes. Whether you’re connecting to your development cluster running in the cloud, or to your local Kubernetes cluster, Bridge to Kubernetes is available for your end-to-end debugging scenarios.”
Bridge to Kubernetes provides a number of compelling features. Some of them are mentioned below-
#news #bridge to kubernetes #developer tools #kubernetes #kubernetes platform #kubernetes tools #local process with kubernetes #microsoft