An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing
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, developin...
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2025-07-01
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| author | Min Cui Yipeng Wang |
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| description | 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. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-4369aea496ff4bfabd4794b0a9a006d42025-08-20T03:36:30ZengMDPI AGSensors1424-82202025-07-012515470510.3390/s25154705An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud ComputingMin Cui0Yipeng Wang1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaWorkflow 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.https://www.mdpi.com/1424-8220/25/15/4705workflow schedulingmulti-objective optimizationHEFTWOALévy flight |
| spellingShingle | Min Cui Yipeng Wang An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing Sensors workflow scheduling multi-objective optimization HEFT WOA Lévy flight |
| title | An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing |
| title_full | An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing |
| title_fullStr | An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing |
| title_full_unstemmed | An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing |
| title_short | An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing |
| title_sort | effective qos aware hybrid optimization approach for workflow scheduling in cloud computing |
| topic | workflow scheduling multi-objective optimization HEFT WOA Lévy flight |
| url | https://www.mdpi.com/1424-8220/25/15/4705 |
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