Adaptive Job-Scheduling Algorithm based on Queuing Theory in a Hybrid Cloud Environment

摘要

To resolve the problem of unreasonable resource allocations caused by the continuous arrival of different types of jobs in a hybrid cloud environment, an adaptive job-scheduling algorithm based on queuing theory is proposed. This paper analyses Job load types, and the jobs are classified according to the logistic regression method. A resource utility is used to classify the nodes in a private cloud cluster by considering the heterogeneity of the private cloud resources. Based on the job classification and the resource classification, a queuing model is established, and an adaptive genetic algorithm is used to manage the job queue's arrival rate that becomes the basis of the resource allocation. The proposed algorithm is compared with some existed similar algorithms to verify its performance in terms of job response times and throughput.