This repository contains various optimizations for resource management techniques. Leveraging the faas project makes them ready-to-use for real-world clusters and simulations at the same time. This project collects different strategies for solutions around: scheduling, scaling and load balancing. It is meant to serve as a central ("market")place to implement and provide novel resource management techniques. The main goal is to provide mix & match components to conveniently evaluate different strategies.
Therefore, this project will contain only bare-minimum execution/test code. This project is intended to be used as dependency when performing sophisticated real-world evaluations or simulations.
To install all dependencies, run:
make venv
TODO
All code is contained in the faasopts module.
Currently, we focus on the three main resource management techniques: autoscaling, scheduling and load balancing.
You can find implemented approaches in the respective module, but we provide information for each in the following.
This autoscaler replicates the behavior of the official Kubernetes Horizontal Pod Autoscaler.
Originally, HPA only supported CPU-based scaling but has been extended to include various custom metrics.
To this end, we implement different variants of this autoscalers.
For example, we provide a HPA implementation that is CPU-based (HCPA) and one that takes the latency as metric (HLPA).
You can find the base HPA class and both implementations in the faasopts.autoscalers.k8s.hpa.global module.
TODO
TODO