A cooperative coevolutionary genetic programming hyper-heuristic for multi-objective makespan and cost optimization in cloud workflow scheduling
提出一种协同进化遗传编程超启发式方法，自动生成任务调度和虚拟机分配规则，同时优化完工时间和成本，在测试中超体积指标比基准启发式提高72.91%。
This study presents a novel multi-objective approach for NP-hard workflow scheduling in cloud computing environments. Traditional rule-based heuristics offer flexibility but lack consistent superiority across all criteria and scenarios. The development of specific rules usually requires tedious manual refinement by experts. To overcome this limitation, our method leverages evolutionary computation and simulation to automatically derive new rules. Moreover, workflow scheduling involves two crucial and related aspects: task scheduling and the allocation of virtual machines. Our contribution includes three distinct algorithms: two that address each decision separately and a third approach that coevolves priority rules for both decisions simultaneously. Computational tests demonstrate superior performance, with an exploration of rules yielding a 72.91% larger hypervolume for optimizing makespan and costs compared to benchmark heuristics from the literature. The validation on unseen instances shows a 90.26% improvement in hypervolume performance, highlighting the robustness of our approach. • Introducing a novel cooperative coevolutionary GPHH framework for WSCC. • Simultaneously coevolving task scheduling and VM selection rules. • Addressing conflicting objectives in WSCC, focusing on the makespan and costs. • Incorporating the NSGA-II for comprehensively exploring the objective space. • The HVR was increased by up to 72.91% compared to benchmark heuristics.