Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

高效执行科学工作流程是云计算中一项具有挑战性的任务，需要高性能计算来处理不断增长的数据集。由于科学工作流程应用程序中任务的相互依赖性，节能资源分配对于在异构物理机上运行的大规模应用程序至关重要。因此，本文提出了一种基于混合启发式算法的节能型云计算服务（HH-ECO），它为资源分配，任务调度和科学工作流的优化提供了重要的解决方案。为了确保高效执行能源，HH-ECO专注于通过自适应突变和节能感知迁移策略执行非主要工作流程任务。HH-ECO采用基于混沌的粒子群优化（C-PSO）原理，通过在不局部收敛的情况下生成全局最佳计划来优化资源分配，任务调度和资源迁移。具有自适应突变的C-PSO避免了全局最佳化的恶化，同时找到了放置虚拟机的最佳主机，并确保了适当的资源分配计划。通过在基于C-PSO的任务调度过程中考虑工作流任务优先级关系，新的混合启发式方法有效地解决了多目标组合优化问题，而在工作流任务之间没有优势。与现有的混合启发式工作流调度算法（HHWS）和分布式动态VM管理（DDVM）之类的现有方法相比，基于Cloudsim的仿真研究可提供出色的结果。