Clustered queuing model for task scheduling in cloud environment

Abstract

With the advent of big data and Internet of Things (IoT), optimal task scheduling problem for heterogeneous multi-core virtual machines (VMs) in cloud environment has garnered greater attention from researchers around the globe. The queuing model used for optimal task scheduling is to be tuned according to the interactions of the task with the cloud resources and the availability of cloud processing entities. The queue disciplines such as First-In First-Out, Last-In First-Out, Selection In Random Order, Priority Queuing, Shortest Job First, Shortest Remaining Processing Time are all well-known queuing disciplines applied to handle this problem. We propose a novel queue discipline which is based on k-means clustering called clustered queue discipline (CQD) to tackle the above-mentioned problem. Results show that CQD performs better than FIFO and priority queue models under high demand for resource. The study shows that: in all cases, approximations to the CQD policies perform better than other disciplines; randomized policies perform fairly close to the proposed one and, the performance gain of the proposed policy over the other simulated policies, increase as the mean task resource requirement increases and as the number of VMs in the system increases. It is also observed that the time complexity of clustering and scheduling policies is not optimal and hence needs to be improved.