Q-learning based active monitoring with weighted least connection round robin load balancing principle for serverless computing

Abstract

Serverless computing is considered one of the most promising technologies for real-time applications, with function as a service (FaaS) managing service requests in serverless computing. Load balancing played a vital role in assigning tasks in serverless computing for customers; user requests were controlled by load balancing algorithms and managed using machine learning techniques to deliver results and performance metrics within specified time limits. All serverless computing applications aimed to achieve optimal performance based on the most effective load balancing techniques, which directed requests to the appropriate servers in a timely manner. This research focused on developing a novel Q-learning based active monitoring with least connection round robin load balancing principle (Q-LAMWLR LB) for serverless computing to address the aforementioned challenge. Also, aimed to intelligently assign requests to serverless computing based on the number of requests arriving at the load balancer and how intelligently they could be directed to the appropriate server. This work utilized standard techniques to calculate the average response time for each scheduling algorithm and develop a novel intelligent load-balancing technique in serverless computing. Required experiment were conducted and the results are giving the improvement as compared to other load balancing principles. The further research in this area also identified and presented.