Autoscaling volumes for Kubernetes (with the help of Prometheus)
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Updated
May 30, 2024 - Python
Autoscaling volumes for Kubernetes (with the help of Prometheus)
Automatically assign Elastic IPs to AWS EC2 Auto Scaling Group instances
Simple, elastic Kubernetes cluster autoscaler for AWS Auto Scaling Groups
Libraries, samples, and tools to help AWS customers onboard with custom resource auto scaling.
A framework that runs on AWS Lambda for autoscaling ECS clusters and services
Solution Accelerators for Serverless Spark on GCP, the industry's first auto-scaling and serverless Spark as a service
Autoscaling for Clouds
Django web application deployed to AWS via Cloudformation templates, with an infrastructure as code approach following the AWS architecture best practices. The application is a sample blog-like website which can be used as a personal portfolio.
A Framework For Intelligence Farming
This service is an automatic scaler for Liara's cloud infrastructure and does not require anything to run. It provides the ability to automatically check the app scale every few seconds and zoom in or out.
Spoptimize: Replace AWS AutoScaling instances with spot instances
A python project to scale the redis resources on Heroku platform
Some of the code used to set up an auto-scaling infrastructure on Amazon AWS
An auto-scaling solution for Amazon DocumentDB.
Kubernetes with kubespray in Centos 7 using HAPROXY to loadbalancer Kubernetes_API,http and http traffic ports using Traefik ingress.
The autoscaling simulation toolbox provides tools to experiment with policies for multilayered autoscaling (inc. VM clusters and applications). Multiverse simulator is the core of the toolbox. Using it, one can evaluate autoscaling policies under different conditions, including various applications, platforms, and workloads.
☁️ 🚀 Example Auto Scaling Fargate Cluster Created on AWS using CloudFormation
Built a Distributed Platform that is capable of managing & deploying tensor-flow models on it. The platform was built using micro-service based architecture with features like auto-scaling, scheduling, centralized logging, monitoring, notification, high-availability and fault tolerance.
Latency based Kubernetes autoscaler.
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