Abstract
For the last few years, Cloud computing has been considered an attractive high-performance computing platform for individuals as well as organizations. The Cloud service providers (CSPs) are setting up data centers with high performance computing resources to accommodate the needs of Cloud users. The users are mainly interested in the response time, whereas the Cloud service providers are more concerned about the revenue generation. Concerning these requirements, the task scheduling for the users’ applications in Cloud computing attained focus from the research community. Various task scheduling heuristics have been proposed that are available in the literature. However, the task scheduling problem is NP-hard in nature and thus finding optimal scheduling is always challenging. In this research, a resource-aware dynamic task scheduling approach is proposed and implemented. The simulation experiments have been performed on the Cloudsim simulation tool considering three renowned datasets, namely HCSP, GoCJ, and Synthetic workload. The obtained results of the proposed approach are then compared against RALBA, Dynamic MaxMin, DLBA, and PSSELB scheduling approaches concerning average resource utilization (ARUR), Makespan, Throughput, and average response time (ART). The DRALBA approach has revealed significant improvements in terms of attained ARUR, Throughput, and Makespan.This fact is endorsed by the average resource utilization results (i.e., 98 % for HCSP dataset, 75 % for Synthetic workload (improve ARUR by 72.00 %, 77.33 %, 78.67 %, and 13.33 % as compared to RALBA, Dynamic MaxMin, DLBA and PSSELB respectively), and 77 % for GoCJ (i.e., the second best attained ARUR)).
Highlights
Cloud computing [1]–[4] has become an attractive and popular platform for individuals and organizations that provides the solution for execution and storage of large applications
We considered that N represents the total number of cloudlets and M shows the total number of Virtual Machines (VM) on the Cloud datacenter
The results have shown an improvement of 44.97 %, 38.74 %, and 31.20 % in the average response time against RALBA, Dynamic Load Balancing Algorithm (DLBA), and PSSELB respectively
Summary
Cloud computing [1]–[4] has become an attractive and popular platform for individuals and organizations that provides the solution for execution and storage of large applications. 2) These algorithms can map the new batch without considering the load of the previous batch and can add new VMs for the newly arrived batch of tasks This issue can result in load imbalance, under-utilization of Cloud resources, and overload some VMs that can lead to performance degradation [18]. These approaches fail to handle any run time problems during the execution of the task. Dynamic [20], [21] task scheduling algorithms provide more flexibility as compared to the static and batch dynamic approaches These heuristics can work with minimum information regarding resources and tasks.
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