DCCWOA: A multi-heuristic fault tolerant scheduling technique for cloud computing environment

On-demand, automatic resource delivery in a transparent manner to users is a remarkable feature offered by the cloud computing environment. User demands are met by dynamically provisioning the cloud resources. Incidental failures during task execution in cloud could be attributed to a variety of reasons. Such failures bring down the cloud performance. Recently, a variety of intelligent task scheduling algorithms have been demonstrated to address several issues in cloud scheduling. Most of these algorithms neglect the fault tolerance criterion, which if addressed appropriately could contribute to better cloud performance. In this research, we had proposed a Dynamic Clustering Cuckoo Whale Optimization Algorithm (DCCWOA) for carrying out efficient scheduling in the cloud by paying equal attention to the fault tolerance parameter. The proposed fault tolerance aware algorithm addresses the scheduling of tasks by maintaining a tab on the currently available resources such that unfortunate failures of autonomous tasks get effectively addressed leading to reduced failures. The performance of the proposed fault tolerance aware DCCWOA has been compared with Ant Colony Optimization algorithm (ACO), Genetic Algorithm (GA) and League Championship Algorithm (LCA)with respect to makespan, failure ratio and failure slowdown parameters under three different scenarios, where in each scenario the number of tasks were appropriately varied. It has been found that the proposed DCCWOA had produced an improvement of 58.19%, 19.88% and 29.32% under scenario 1 for makespan, failure ratio and failure slowdown parameters respectively when compared to ACO, GA and LCA algorithms respectively. Detailed experimental results for scenarios 1, 2 and 3 had been presented in the results section of this article. Results obtained prove the efficacy of the proposed algorithm in overcoming the faults and increasing the scheduling performance of the cloud with respect to the failure rate.