6 Repos
Systems that automatically adjust the number of active compute instances based on real-time traffic and resource demand.
Explore 6 awesome GitHub repositories matching devops & infrastructure · Auto Scaling Groups. Refine with filters or upvote what's useful.
This project is a comprehensive educational curriculum designed to build proficiency across modern infrastructure, cloud-native technologies, and systems administration. It functions as a reference library and interview preparation resource, offering a structured collection of conceptual questions, practical coding challenges, and hands-on scenarios that cover the full spectrum of software delivery and operational workflows. The repository distinguishes itself through a modular, domain-specific structure that links instructional problem statements with verified implementation examples. By emp
Explains how to configure compute capacity to adjust dynamically based on real-time traffic demand.
The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It
Organizes infrastructure components into logical collections to apply consistent scaling policies.
This repository provides a comprehensive library of code examples for implementing event-driven, serverless backend architectures. It serves as a practical guide for building scalable cloud-native applications that execute logic in isolated environments, triggered by infrastructure events or HTTP requests rather than persistent server processes. The collection demonstrates how to leverage managed infrastructure to automate backend workflows, including the use of asynchronous task queuing to maintain system stability during high traffic. It highlights patterns for secure API hosting, enabling
Provides managed infrastructure that automatically scales compute resources based on real-time traffic.
The Kubernetes Cluster Autoscaler is a mechanism that automatically adjusts the number of nodes in a cluster to match the resource demands of pending pods. It functions as a cloud infrastructure scaler that manages the desired capacity of scaling groups to ensure sufficient compute resources for workloads. The system manages cloud infrastructure automation by adjusting node counts when resources are insufficient or nodes are underutilized. It includes a manager for scaling groups using mixed instance policies to balance on-demand and spot instances for cost and availability. The project also
Manages AWS Auto Scaling Groups to adjust the number of active compute instances based on pending pod requirements.
Moto is a cloud service mockery framework and API mock server that simulates AWS infrastructure locally. It allows developers to test cloud-dependent code and verify infrastructure-as-code templates without deploying real resources or incurring costs. The project functions as an SDK interceptor that can patch existing service clients to redirect requests to a local mock environment. It can also be run as a standalone HTTP server, enabling any programming language to interact with the simulated endpoints. The framework covers a vast array of simulated capabilities, including data storage, com
Simulates auto scaling groups, launch configurations, and dynamic capacity adjustments.
OpenWhisk is a serverless cloud platform designed for deploying and executing stateless functions in response to API calls or events. It serves as a complete serverless stack, providing an API gateway for functions, a function-as-a-service runtime manager, and an event-driven workflow engine. The platform distinguishes itself through a polyglot execution model that supports multiple language runtimes and allows for the creation of custom runtimes using Docker containers. It enables complex logic through function orchestration and composition, allowing multiple functions to be chained into seq
Automatically scales the number of active function containers based on real-time traffic and resource demand.