Stochastic Task Scheduling in Cloud Environment for Uncertainty Workload

Abstract

Task scheduling under random parameters in the cloud environment poses a challenge for solving problems relating to resource management and task completion time. In most of the literature study on cloud task scheduling, assume that scheduling parameters are known and can be estimated before applying on the processor, which is not practical due to scheduling parameter variations and performance fluctuations of virtual machines. When all the parameters are known before scheduling the existing task scheduling techniques offered by major cloud providers are efficient. However, there were many challenges when the scheduling parameters vary with time or uncertainty arises before assigning to the scheduler newline