Reinforcement learning-based controller for adaptive workflow scheduling in multi-tenant cloud computing

Abstract

Multi-tenancy is an essential feature in cloud computing and is a major component to achieve scalability and energy-efficient solution to gain high level of economic benefits. As the cloud, computing is gaining more audiences and high user base, the problem of scheduling the computational workflow for multi-tenant cloud scheduling is becoming a difficult task to achieve. In this study, we present a learning-based scheduler for catering heterogeneous software and hardware resources in the context of multi-tenant cloud computing. The experimentation has been carried out with the help of green cloud simulator and the results are compared with the state of the art techniques like minimum completion time, first come first serve and backfilling. The experimental results exhibit that the presented algorithm provides an effective means of utilizing cloud resources in addition with drastic reduction in cost of operation.