kluster-capacity

README

kluster-capacity

[中文]

Cluster capacity tool supports capacity estimation, scheduler simulation, and cluster compression. This repository was inspired by https://github.com/kubernetes-sigs/cluster-capacity.

Install

Go binaries are automatically built with each release by GoReleaser. These can be accessed on the GitHub releases page for this project.

Homebrew

This project can be installed with Homebrew:

brew tap k-cloud-labs/tap
brew install k-cloud-labs/tap/kluster-capacity

Krew

This project can be installed with Krew:

kubectl krew install kluster-capacity

From Source Code

Build the framework:

$ cd $GOPATH/src/github.com/k-cloud-labs/
$ git clone https://github.com/k-cloud-labs/kluster-capacity
$ cd kluster-capacity
$ make build

There are three available sub-commands: ce, cc, and ss, which represent capacity estimation, cluster compression, and scheduler simulation, respectively.

Capacity Estimation

Intro

As new pods get scheduled on nodes in a cluster, more resources get consumed. Monitoring available resources in the cluster is very important as operators can increase the current resources in time before all of them get exhausted. Or, carry different steps that lead to increase of available resources.

Cluster capacity consists of capacities of individual cluster nodes. Capacity covers CPU, memory, disk space and other resources.

Overall remaining allocatable capacity is an estimation. The goal is to analyze the remaining allocatable resources and estimate the available capacity that can still be consumed in terms of the number of pod instances with given requirements that can be scheduled in a cluster.

Enhancement

Here are some enhancements to the cluster capacity mentioned above.

• Support using an existing pod as a pod template directly from the cluster.
• Support batch simulation for different pod templates.

Run

run the analysis:

# use an specified pod yaml file as pod template
$ ./kluster-capacity ce --pods-from-template <path to pod templates> 
# use an existing pod from cluster as pod template
$ ./kluster-capacity ce --pods-from-cluster <namespace/name key of the pod> 

For more information about available options run:

$ ./kluster-capacity ce --help

Demonstration

Assuming a cluster is running with 4 nodes and 1 master with each node with 2 CPUs and 4GB of memory. With pod resource requirements to be 150m of CPU and 100Mi of Memory.

$ ./kluster-capacity ce --pods-from-template <path to pod templates> --verbose
Pod requirements:
	- cpu: 150m
	- memory: 100Mi

The cluster can schedule 52 instance(s) of the pod.
Termination reason: FailedScheduling: pod (small-pod-52) failed to fit in any node
fit failure on node (kube-node-1): Insufficient cpu
fit failure on node (kube-node-4): Insufficient cpu
fit failure on node (kube-node-2): Insufficient cpu
fit failure on node (kube-node-3): Insufficient cpu


Pod distribution among nodes:
	- kube-node-1: 13 instance(s)
	- kube-node-4: 13 instance(s)
	- kube-node-2: 13 instance(s)
	- kube-node-3: 13 instance(s)

Once the number of running pods in the cluster grows and the analysis is run again, the number of schedulable pods decreases as well:

$ ./kluster-capacity ce --pods-from-template <path to pod templates> --verbose
Pod requirements:
	- cpu: 150m
	- memory: 100Mi

The cluster can schedule 46 instance(s) of the pod.
Termination reason: FailedScheduling: pod (small-pod-46) failed to fit in any node
fit failure on node (kube-node-1): Insufficient cpu
fit failure on node (kube-node-4): Insufficient cpu
fit failure on node (kube-node-2): Insufficient cpu
fit failure on node (kube-node-3): Insufficient cpu


Pod distribution among nodes:
	- kube-node-1: 11 instance(s)
	- kube-node-4: 12 instance(s)
	- kube-node-2: 11 instance(s)
	- kube-node-3: 12 instance(s)

Output format

ce command has a flag --output (-o) to format its output as json or yaml.

$ ./kluster-capacity ce --pods-from-template <path to pod templates> -o json|yaml

The json or yaml output is not versioned and is not guaranteed to be stable across various releases.

Scheduler Simulation

Intro

The scheduler simulation takes all nodes, pods, and other related resources in the current cluster as input to simulate the process from having no pods to creating and scheduling all pods. This can be used to calculate the cluster compression ratio to evaluate the effectiveness of the scheduling or to measure the quality of the scheduling algorithm.

Compared to cluster compression, its results are more extreme and idealized.

Run

run the analysis:

 ./kluster-capacity ss

For more information about available options run:

$ ./kluster-capacity ss --help

It supports two termination conditions: AllSucceed and AllScheduled. The former means the program ends when all pods are successfully scheduled, while the latter means it exits after all pods have been scheduled at least once. The default is AllSucceed. The exit condition can be set using the --exit-condition flag.

Demonstration

Assuming a cluster is running with 4 nodes and 1 master with each node with 2 CPUs and 4GB of memory. With 40 pod with resource requirements to be 100m of CPU and 200Mi of Memory to schedule.

If the scheduler uses the LeastAllocated strategy, the scheduling result may be as follows:

$ ./kluster-capacity ss --verbose
Termination reason: AllSucceed: 40 pod(s) have been scheduled successfully.

Pod distribution among nodes:
        - kube-node-1: 10 instance(s)
        - kube-node-2: 10 instance(s)
        - kube-node-3: 10 instance(s)
        - kube-node-4: 10 instance(s)

Once the scheduler uses the MostAllocated strategy, the scheduling result may be as follows:

$ ./kluster-capacity ss --verbose
Termination reason: AllSucceed: 40 pod(s) have been scheduled successfully.

Pod distribution among nodes:
        - kube-node-1: 20 instance(s)
        - kube-node-2: 20 instance(s)

The scheduling result above can be analyzed to evaluate the effectiveness of the scheduling strategy and the cluster capacity compression ratio. For example, the above result represents a cluster compression ratio of 2, which means that there is 50% resource waste in an ideal situation.

Cluster Compression

Intro

Cluster compression takes the current state of the cluster, including all nodes, pods, and other relevant resources, as input, and simulates the process of compressing the cluster by removing nodes. It can be used to calculate the compression ratio of the cluster, which is a measure of how efficiently the resources are being utilized.

Compared to simulation scheduling, the results of cluster compression are generally more realistic.

Run

run the analysis:

 ./kluster-capacity cc --verbose

For more information about available options run:

$ ./kluster-capacity cc --help

Demonstration

Assuming a cluster is running with 4 nodes and 1 master with each node with 2 CPUs and 4GB of memory. With 40 pod with resource requirements to be 100m of CPU and 200Mi of Memory bind to the 4 nodes.

./kluster-capacity cc --verbose
2 node(s) in the cluster can be scaled down.

Termination reason: FailedSelectNode: could not find a node that satisfies the condition, 1 master node(s); 2 node(s) can't be scale down because of insufficient resource in other nodes;

nodes selected to be scaled down:
        - kube-node-1
        - kube-node-3

The above result indicates that with the given resource requirements for 40 pods, ensuring that all pods can be scheduled, the cluster can remove 2 additional nodes, resulting in a compression ratio of 2, which means there is 50% resource waste.

Feature

• cluster compression
• capacity estimation
• scheduler simulation
• snapshot based simulation
• fragmentation rate analysis

Enjoy it and feel free to give your opinion, thanks!

Documentation

Overview

Copyright © 2023 k-cloud-labs org

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.