Cui Min, Wang Yipeng
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel). 2025 Jul 30;25(15):4705. doi: 10.3390/s25154705.
Workflow scheduling in cloud computing is attracting increasing attention. Cloud computing can assign tasks to available virtual machine resources in cloud data centers according to scheduling strategies, providing a powerful computing platform for the execution of workflow tasks. However, developing effective workflow scheduling algorithms to find optimal or near-optimal task-to-VM allocation solutions that meet users' specific QoS requirements still remains an open area of research. In this paper, we propose a hybrid QoS-aware workflow scheduling algorithm named HLWOA to address the problem of simultaneously minimizing the completion time and execution cost of workflow scheduling in cloud computing. First, the workflow scheduling problem in cloud computing is modeled as a multi-objective optimization problem. Then, based on the heterogeneous earliest finish time (HEFT) heuristic optimization algorithm, tasks are reverse topologically sorted and assigned to virtual machines with the earliest finish time to construct an initial workflow task scheduling sequence. Furthermore, an improved Whale Optimization Algorithm (WOA) based on Lévy flight is proposed. The output solution of HEFT is used as one of the initial population solutions in WOA to accelerate the convergence speed of the algorithm. Subsequently, a Lévy flight search strategy is introduced in the iterative optimization phase to avoid the algorithm falling into local optimal solutions. The proposed HLWOA is evaluated on the WorkflowSim platform using real-world scientific workflows (Cybershake and Montage) with different task scales (100 and 1000). Experimental results demonstrate that HLWOA outperforms HEFT, HEPGA, and standard WOA in both makespan and cost, with normalized fitness values consistently ranking first.
云计算中的工作流调度正吸引着越来越多的关注。云计算可以根据调度策略将任务分配到云数据中心中可用的虚拟机资源上，为工作流任务的执行提供一个强大的计算平台。然而，开发有效的工作流调度算法以找到满足用户特定QoS要求的最优或接近最优的任务到虚拟机分配解决方案，仍然是一个开放的研究领域。在本文中，我们提出了一种名为HLWOA的混合QoS感知工作流调度算法，以解决在云计算中同时最小化工作流调度的完成时间和执行成本的问题。首先，将云计算中的工作流调度问题建模为一个多目标优化问题。然后，基于异构最早完成时间（HEFT）启发式优化算法，对任务进行反向拓扑排序，并分配到最早完成时间的虚拟机上，以构建初始的工作流任务调度序列。此外，提出了一种基于莱维飞行的改进鲸鱼优化算法（WOA）。将HEFT的输出解用作WOA中的初始种群解之一，以加快算法的收敛速度。随后，在迭代优化阶段引入莱维飞行搜索策略，以避免算法陷入局部最优解。使用具有不同任务规模（100和1000）的实际科学工作流（CyberShake和Montage）在WorkflowSim平台上对所提出的HLWOA进行评估。实验结果表明，HLWOA在完工时间和成本方面均优于HEFT、HEPGA和标准WOA，归一化适应度值始终排名第一。
