Tri-Objective Workflow Scheduling and Optimization in Heterogeneous Cloud Environments

Cloud computing has become the most popular distributed computing paradigm among others which delivers scalable resources for efficient execution of large-scale scientific workflows. However, the large number of user requests and the limited cloud resources have posed a significant challenge on resource allocation, scheduling/mapping, power consumption, monetary cost, and so on. Therefore, how to schedule and optimize workflow execution in a cloud environment has become the most critical factor in improving the overall performance. Moreover, Multi-objective Optimization Problems (MOPs) along with heterogeneous cloud environments have made resource utilization and workflow scheduling even more challenging. In this work, we propose a novel algorithm, named Multi-objective Optimization for Makespan, Cost and Energy (MOMCE), to efficiently assign tasks to cloud resources in order to reduce total execution time, monetary cost, and energy consumption of scientific workflows. The experimental results have demonstrated the optimization stability and robustness of MOMCE algorithm for achieving a better fitness value in comparison with other existing algorithms.