Adaptive Tasmanian Devil Optimization algorithm based efficient task scheduling for big data application in a cloud computing environment

One of the most difficult issues in cloud computing is scheduling tasks on appropriate resources on the cloud.This is significant because multiple tasks may need to be efficiently scheduled across different virtual machines to maximize resource utilization and minimize makespan. As a result, various efforts have been made to use metaheuristic algorithms to tackle the task scheduling problem. However, these techniques may occasionally experience early convergence and be trapped in local search. This research proposes a multi-objective-based task scheduling in cloud computing for big data applications to address these issues. To accomplish this goal, the adaptive Tasmanian Devil Optimization (ATDO) method is created in this study, with a focus on resolving challenging optimization issues. Following that, the opposition-based learning technique (OBL) is combined with TDO to maintain the population diversity and improve convergence on the ideal answer. In addition, cost, makespan,and resource utilization are taken into account when designing the multi-objective function (MOF). The proposed strategy included efficient solution representation, efficient fitness function derivation, TDO, and OBL operators. The effectiveness of the strategy is examined using several evaluation metrics, and its efficacy is compared with those of other approaches.The proposed method takes a minimum time of 2134 ms for scheduling 1000 tasks and 20.97 degree of imbalance.