Reliability Model of Cloud Computing Job Scheduling Based on Discrete Time Markov Chain

Abstract

Cloud computing primarily focuses on providing secure and reliable access to geographically distributed resources. Cloud computing is a parallel and distributed system that simplifies the virtualization of distributed computing. Job scheduling is one of the most important services in cloud computing. Job scheduling is used to schedule users' jobs to allocate suitable resources in a cloud environment. Recently, several efficient job scheduling algorithms have been proposed for cloud computing. The purpose of designing these algorithms is to achieve time and cost optimization for job execution. On the other hand, it is necessary to provide a model to assess the reliability of the different job scheduling algorithms. Unfortunately, no accurate and complete model has been proposed to evaluate the reliability of these algorithms. Scheduling failures are inevitable in cloud computing due to the harsh deployment environment, resource migration, non-compliance time or cost, etc. Therefore, providing a reliability model is necessary for successful scheduling algorithms. In this paper, a reliability model based on a discrete-time Markov chain is incorporated for the famous scheduling algorithms MCT, MET, BCO, HCOC, and PPO. We propose an approach to evaluating the reliability of scheduling algorithms based on time and cost constraints. The proposed approach is modeled by a Discrete Time Markov Chain, and the results show the reliability of scheduling algorithms in different states.