OG-RADL: overall performance-based resource-aware dynamic load-balancer for deadline constrained Cloud tasks

Cloud computing is a scalable computing paradigm that provides computing services to users over the Internet. To achieve high user satisfaction and improve the performance of the Cloud, a balanced distribution of users tasks plays a vital role. In the literature, a number of task scheduling and load-balancing schemes have been proposed. However, the majority of these scheduling heuristics focus on a single evaluation parameter (i.e., makespan or resource utilization, etc.) as a scheduling objective. Improving one parameter may not guarantee an increase in the overall performance of the Cloud. There is a need to have such algorithms that focus on improving the overall performance of the Cloud by taking into account multiple evaluation parameters. In this paper, an Overall Performance-based Resource-aware Dynamic Load-balancer (OG-RADL) for deadline constrained Cloud tasks is proposed. OG-RADL has the ability to distribute the workload of independent and compute-intensive tasks according to the resource computation capability at run time. Moreover, a novel normalization technique is proposed that overcome the limitations of existing normalization techniques. The OG-RADL enhance load balancing, support deadline constrained tasks, and improve the overall performance gain of the Cloud. The experimental result shows that the proposed approach OG-RADL outperforms as compared to existing task scheduling algorithms named DLBA, DC-DLBA, Dy-MaxMin, RALBA, PSSELB, and MODE in terms of the overall performance of the Cloud.