A Consensus-based Auto-scaling Approach For Serverless Environments

Efficient management of computing resources has always been a significant concern for users. An automatic scaling system can help in managing hardware resources by adapting to the system's performance history. It can increase or decrease resources automatically, without human intervention, based on predefined criteria. This ensures smooth program execution without any disruption caused by changes in the operating environment. This study focuses on serverless environments, which rely on functions. We model these functions using graph theory, analyze their dependencies, and identify the most critical bottlenecks in the graph. We then use two approaches, supervised and unsupervised, to predict the scalability of bottleneck resources. To be more sure of the scaling decision, the consensus mechanism compares the predictions of the models, and the best model's result is considered the final scaling decision, which creates consistency between the results obtained from the methods. Results show that supervised approaches perform better than unsupervised approaches in the automatic scaling problem. The models implemented in this research can determine the scaling result with ۹۸% accuracy, which is a ۲.۵% improvement compared to previous works.

Keywords:

Autoscaling , Cloud Environment , Machine Learning , Workload Prediction , Ensemble Learning.