Microesrvice Resource Autoscaler

The block diagram of our system

A resource manager on top of kubernetes that automatically finds optimum resources for deployed application without human intervention. Upon doing that, the resource manager does not violate SLO and does not need offline datasets for training. The algorithm is simple yet effective in real world scenario.

Results: Saves 33% more resources than state-of-the-art rule based autoscaling Technology used: Python, Kubernetes, Prometheus, Jaeger, Machine Learning.

Md Rajib Hossen
Md Rajib Hossen
PhD Candidate in Computer Science

My research interests include Microservices, HPC, Distributed Systems, Machine Learning, and Converged Computing