TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment

Abstract

Due to the completely random and dynamic nature of the cloud environment, as well as the high volume of jobs, one of the significant challenges in this environment is proper online job scheduling. Most of the algorithms are presented based on heuristic and meta-heuristic approaches, which result in their inability to adapt to the dynamic nature of resources and cloud conditions. In this paper, we present a distributed online algorithm with the use of two different learning automata for each scheduler to schedule the jobs optimally. In this algorithm, the placed workload on every virtual machine is proportional to its computational capacity and changes with time based on the cloud and submitted job conditions. In proposed algorithm, two separate phases and two different LA are used to schedule jobs and allocate each job to the appropriate VM, so that a two phase adaptive algorithm based on LA is presented called TPALA. To demonstrate the effectiveness of our method, several scenarios have been simulated by CloudSim, in which several main metrics such as makespan, success rate, average waiting time, and degree of imbalance will be checked plus their comparison with other existing algorithms. The results show that TPALA performs at least 4.5% better than the closest measured algorithm.