RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role. A number of technique have been proposed in literature. However, majority these fail resource utilization that causes miss tasks deadlines. This is because are not deadline-aware. In this research, Resource deadline Aware Dynamic Load-balancer (RADL) for Cloud, presented. The scheme evenly distribute incoming workload compute-intensive independent at run-time. addition, RADL approach has capability accommodate newly arrived (with shorter deadlines) efficiently reduce rejection. scheduler monitors/updates VM status Experimental results show attained up 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, 259.14% improvement average utilization, meeting deadlines, lower makespan, response time, penalty cost, execution cost respectively as compared state-of-the-art heuristics using three benchmark datasets.