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Cluster-proportional-autoscaler Alternatives
Similar projects and alternatives to cluster-proportional-autoscaler
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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keda
KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
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vcluster
vCluster - Create fully functional virtual Kubernetes clusters - Each vcluster runs inside a namespace of the underlying k8s cluster. It's cheaper than creating separate full-blown clusters and it offers better multi-tenancy and isolation than regular namespaces.
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metrics-server
Scalable and efficient source of container resource metrics for Kubernetes built-in autoscaling pipelines.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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dotnet-pressure-api
An API that can apply memory and CPU pressure to test autoscaling rules in Kubernetes
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better cluster-proportional-autoscaler alternative or higher similarity.
cluster-proportional-autoscaler reviews and mentions
Posts with mentions or reviews of cluster-proportional-autoscaler.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-06.
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Practical Introduction to Kubernetes Autoscaling Tools with Linode Kubernetes Engine
The Cluster Proportional Autoscaler (CPA) is a horizontal pod autoscaler that scales replicas based on the number of nodes in the cluster. Unlike other autoscalers, it does not rely on the Metrics API and does not require the Metrics Server. Additionally, unlike other autoscalers we saw, a CPA is not scaled with a Kubernetes resource but instead uses flags to identify target workloads and a ConfigMap for scaling configuration. The following diagram illustrates the components of the CPA:
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K8s ephemeral application environments
Namespacing each environment would give you isolation, depending on how your service discovery works within the environment. You could consider the horizontal proportional autoscaler (or maybe KEDA) and hook it up to a metric for the queue depth. https://github.com/kubernetes-sigs/cluster-proportional-autoscaler
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Kubernetes Cluster Over-Provisioning: Proactive App Scaling
If we want to configure dynamic overprovisioning of a cluster (e.g. 20% of resources in the cluster) then we need to use Horizontal Cluster Proportional Autoscaler.
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A note from our sponsor - SaaSHub
www.saashub.com | 10 May 2024
Stats
Basic cluster-proportional-autoscaler repo stats
3
589
5.8
18 days ago
kubernetes-sigs/cluster-proportional-autoscaler is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of cluster-proportional-autoscaler is Go.
Popular Comparisons
- cluster-proportional-autoscaler VS autoscaler
- cluster-proportional-autoscaler VS k8s-prometheus-adapter
- cluster-proportional-autoscaler VS Overprovisioner
- cluster-proportional-autoscaler VS metrics-server
- cluster-proportional-autoscaler VS vcluster
- cluster-proportional-autoscaler VS dotnet-pressure-api
- cluster-proportional-autoscaler VS k9s
- cluster-proportional-autoscaler VS cluster-api
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