Optimizing bag-of-tasks scheduling on cloud data centers using hybrid swarm-intelligence meta-heuristic.

Usually, a large number of concurrent bag-of-tasks (BoTs) application execution requests are submitted to cloud data centers (CDCs), which needs to be optimally scheduled on the physical cloud resources to obtain maximal performance. In the current paper, NP-Hard cloud task scheduling (CTS) problem for scheduling concurrent BoT applications is modeled as an optimization problem involving minimization of makespan and energy consumption. Whale optimization algorithm (WOA) has been found effective in solving a wide range of optimization problems. However, standard WOA has certain deficiencies such as inadequate exploration ability, slow convergence, high computation complexity, and insufficient exploration–exploitation phase trade-off. These deficiencies eventually result in unacceptable results when the original WOA is applied over task scheduling optimization problems. To address these limitations, a multi-objective scheduling algorithm called OWPSO is suggested, which incorporates opposition-based learning (OBL) and particle swarm optimization (PSO) mechanisms into the standard WOA method. Firstly, the OBL method is applied to produce an optimal initial population to enhance the exploration and convergence speed of the proposed OWPSO approach in the successive generations. Secondly, PSO and OBL methods are incorporated in the exploration phase of the standard WOA approach to enhance exploration ability further. Thirdly, a fitness-based switching mechanism is added to provide an adequate exploration–exploitation phase trade-off. Finally, a discrete resource allocation heuristic is incorporated in the OWPSO to provide an efficient resource allocation. Simulation experiments over the CloudSim simulator reveal that OWPSO approach results in makespan reduction in the range of 1.68−18.38% (for CEA-Curie workloads), 2.10−24.32% (for HPC2N workloads), and energy consumption reduction in the range of 0.93−14.70% (for CEA-Curie workloads), and 0.73−25.94% (for HPC2N workloads) over other well-known meta-heuristics. Statistical tests and box plots further revealed the robustness of proposed OWPSO algorithm. [ABSTRACT FROM AUTHOR]