列表调度和人工蜂群相结合的高能效云工作流调度方法

doi:10.6040/j.issn.1672-3961.0.2025.116

Abstract

Abstract: To address the high energy consumption problem caused by the increasing growth of data centers in the field of cloud workflow scheduling, a workflow scheduling method combining list scheduling and artificial bee colonies, called LisABC, was proposed, which aimed to meet the deadline constraints and reduce the execution energy consumption. Considering the impact of task scheduling sequences on the energy consumption of workflow execution, a new honey source(scheduling solution)encoding was designed; by leveraging the characteristics of workflow structure, a hierarchical method for generating effective task scheduling sequences based on workflow layering was proposed; a scheduling rule that took into account both the sub-term deadline constraints of tasks and the minimization of task execution energy consumption was proposed. Combined with list scheduling ideas, the honey source solution was decoded into a scheduling plan. Experimental results showed that compared with existing methods, LisABC had been able to effectively reduce the energy consumption of the workflow while meeting deadline constraints, and had been well adapted to scientific workflows of different scales.

Key words: cloud computing, workflow scheduling, energy consumption, artificial bee colony, list scheduling

CLC Number:

TP393

Wang Yun, Zhu Xiaoze, Jiang Xinhuan, Tian Xiumei, Wu Zhiqiang. An energy-efficient cloud workflow scheduling method combining list schedulind and artificial bee colony algorithm[J].Journal of Shandong University(Engineering Science), 2026, 56(5): 26-39.

References

[1] Ye L J, Yang L W, Zhao X C, et al. An energy-awaremultistages hybrid scheduling approach for IoT workflow applications with reliability constraint in cloud computing systems[J]. IEEE Internet of Things Journal, 2025, 12(15): 29670-29681.
[2] Walia N K, Kaur N, Alowaidi M, et al. An energy-efficient hybrid scheduling algorithm for task scheduling in the cloud computing environments[J]. IEEE Access, 2021, 9: 117325-117337.
[3] 李奥, 时军艳. 云计算技术在计算机网络数据管理中的应用研究[J]. 信息记录材料, 2025, 26(8): 154-156. Li Ao, Shi Junyan. Research on the application of cloud computing technology in computer network data management [J]. Information Recording Materials, 2025, 26(8): 154-156.
[4] 张辉, 张博, 但扬溪, 等. 基于云计算技术和物联网的大数据集成模型[J]. 电子设计工程, 2025, 33(17): 132-135. Zhang Hui, Zhang Bo, Dan Yangxi, et al. Big data integration model based on cloud computing technology and Internet of Things [ J ]. Electronic Design Engineering, 2025, 33(17): 132-135.
[5] Jothish Kumar M, Kumar Reddy G V K, Iyapparaja, et al. Comparative analysis of cloud computing and the gig economy pricing strategies [C] //2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems(ICSES). Chennai, India: IEEE, 2024: 1-8.
[6] Zou Z H. Research on user information security based on cloud computing[C] //2023 IEEE 7th Information Technology and Mechatronics Engineering Conference(ITOEC). Chongqing, China: IEEE, 2023, 7: 35-39.
[7] Saeedizade E, Ashtiani M. Scientific workflow scheduling algorithms in cloud environments: a comprehensive taxonomy, survey, and future directions[J]. Journal of Scheduling, 2025, 28(1): 1-63.
[8] Mittal P, Kumar S, Sharma S. Genetic algorithm based deterministic workflow scheduling with load balancing[C] //2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking(CICTN). Ghaziabad, India: IEEE, 2025: 662-667
[9] Lin L D, Pan L, Liu S J. SpotDAG: an RL-based algorithm for DAG workflow scheduling in heterogeneous cloud environments[J]. IEEE Transactions on Services Computing, 2024, 17(5): 2904-2917.
[10] Ye L J, Yang L W, Xia Y Q, et al. A cost-driven intelligence scheduling approach for deadline-constrained IoT workflow applications in cloud computing[J]. IEEE Internet of Things Journal, 2024, 11(9): 16033-16047.
[11] Shobeiri P, Akbarian Rastaghi M, Abrishami S, et al. PCP-ACO: a hybrid deadline-constrained workflow scheduling algorithm for cloud environment[J]. The Journal of Supercomputing, 2024, 80(6): 7750-7780.
[12] Qing J H, Xu S X, Qin Y H, et al. A study of workflow task scheduling strategy for makespan and cost optimization in jointcloud computing[C] //Proceedings of the 2025 4th International Conference on Intelligent Systems, Communications and Computer Networks. Nanjing, China: ACM, 2025: 35-42.
[13] Karishma, Kumar H. GWO based energy-efficient workflow scheduling for heterogeneous computing systems[J]. Soft Computing, 2025, 29(7): 3469-3508.
[14] Medishetti S K, Kurupati R, Donthi R K, et al. Energy and deadline aware scheduling in multi cloud environment using water wave optimization algorithm[J]. International Journal of Intelligent Systems and Applications, 2025, 17(3): 48-64.
[15] Srivastava V, Kumar R. Energy and deadline aware workflow scheduling using adaptive remora optimization in cloud computing[J]. Scalable Computing: Practice and Experience, 2025, 26(1): 490-502.
[16] IEA. Energy and AI[R/OL].(2025-04-10)[2025-07-11]. https://www.iea.org/reports/energy-and-ai
[17] Choudhary A, Rajak R. A novel strategy for deterministic workflow scheduling with load balancing using modified min-min heuristic in cloud computing environment[J]. Cluster Computing, 2024, 27(5): 6985-7006.
[18] Mao J. Improving energy efficiency and security of DVFS-Enabled clouds[D]. Auburn: Auburn University, 2022: 23-24.
[19] Verma P, Maurya A K, Yadav R S. A survey on energy-efficient workflow scheduling algorithms in cloud computing[J]. Software: Practice and Experience, 2024, 54(5): 637-682.
[20] Abdelhafez A, Lastovetsky A. Energy and performance analysis of parallel heterogeneous genetic algorithms under various CPU and GPU DVFS Governors: a preliminary study on predictive profiling[C] //Proceedings of the Genetic and Evolutionary Computation Conference. Malaga, Spain: ACM, 2025: 782-790.
[21] Zhang Z Y, Zhao Y, Li H, et al. DVFO: learning-based DVFS for energy-efficient edge-cloud collaborative inference[J]. IEEE Transactions on Mobile Computing, 2024, 23(10): 9042-9059.
[22] Wang Q, Mei X, Liu H, et al. Energy-aware non-preemptive task scheduling with deadline constraint in DVFS-enabled heterogeneous clusters[J]. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(12): 4083-4099.
[23] Wang W, Ren Q, Yao G S. Energy and reliability aware cloud workflow scheduling with uncertain task execution times[C] //2025 5th International Conference on Consumer Electronics and Computer Engineering(ICCECE). Dongguan, China: IEEE, 2025: 482-487.
[24] Barredo P, Puente J. Energy-aware cooperative multi-fitness evolutionary algorithm for workflow scheduling in cloud computing[J]. Natural Computing, 2025, 24(3): 557-570.
[25] Chafi S E, Balboul Y, Fattah M, et al. Novel PSO-based algorithm for workflow time and energy optimization in a heterogeneous fog computing environment[J]. IEEE Access, 2024, 12: 41517-41530.
[26] Gao Y Q, Zhang S Y, Zhou J T. A hybrid algorithm for multi-objective scientific workflow scheduling in IaaS cloud[J]. IEEE Access, 2019, 7: 125783-125795.
[27] Topcuoglu H, Hariri S, Wu M Y. Performance-effective and low-complexity task scheduling for heterogeneous computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2002, 13(3): 260-274.
[28] Karaboga D, Basturk B. On the performance of artificial bee colony(ABC)algorithm[J]. Applied Soft Computing, 2008, 8(1): 687-697.
[29] 张开元, 杨火根, 夏小云, 等. 作业车间调度的邻域单次搜索人工蜂群算法[J].制造业自动化, 2024, 46(9): 95-103. Zhang Kaiyuan, Yang Huogen, Xia Xiaoyun, et al.A single neighborhood search artificial bee colony algorithm for job shop scheduling[J]. Manufacturing Automation, 2024, 46(9): 95-103.
[30] 黄澄, 袁东风, 张海霞. 基于狮群算法的数字孪生车间调度问题优化[J]. 山东大学学报(工学版), 2021, 51(4): 17-23. Huang Cheng, Yuan Dongfeng, Zhang Haixia. Optimization of digital twin job scheduling problem based on lion swarm algorithm [J]. Journal of Shandong University(Engineering Science), 2021, 51(4): 17-23.
[31] 卿朝进,赵桂毅,刘宇畅,等. 基于数字高程模型和人工蜂群算法的地面防空部署策略[J]. 西华大学学报(自然科学版), 2026, 45(1): 59-71. Qing Chaojin, Zhao Guiyi, Liu Yuchang, et al. Ground air defense deployment strategy based on digital elevation model and artificial bee colony algorithm[J]. Journal of Xihua University(Natural Science Edition), 2026, 45(1): 59-71.
[32] Rodriguez M A, Buyya R. Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds[J]. IEEE Transactions on Cloud Computing, 2014, 2(2): 222-235.
[33] Girgis M R, Mahmoud T M, Azzam H M. GA-based QOS-aware workflow scheduling of deadline tasks in grid computing[J]. Knowledge and Information Systems, 2024, 66(5): 2859-2884.
[34] Hassan H A, Salem S A, Saad E M. A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment[J]. Future Generation Computer Systems, 2020, 112: 431-448.
[35] Deelman E, Vahi K, Juve G, et al. Pegasus, a workflow management system for science automation[J]. Future Generation Computer Systems, 2015, 46: 17-35.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

An energy-efficient cloud workflow scheduling method combining list schedulind and artificial bee colony algorithm

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 11

Metrics

Comments

Recommended 0

[1]	ZHANG Xiao, CHENG Sijin, WANG Yi, LI Xinyi, XU Yuzhang, HU Zhouyue, ZHANG Hengxu. Method for constructing multi-level chains of trust for distributed energy resources in virtual power plants [J]. Journal of Shandong University(Engineering Science), 2026, 56(3): 177-186.
[2]	Xinlu ZONG,Jiayuan DU. Evacuation simulation model based on multi-target driven artificial bee colony algorithm [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 1-6.
[3]	Dexin LI, Chonglin ZONG, Jiarui WANG, Haifeng ZHANG, Chang LIU, Dawei HUANG. Day-ahead optimal scheduling considering the constraints of UHVDC transmission and wheeling contracts [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 69-75.
[4]	YE Mingquan, GAO Lingyun, WAN Chunyuan. Gene expression data classification based on artificial bee colony and SVM [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 10-16.
[5]	HE Dongzhi, ZHANG Jifeng, ZHAO Pengfei. Parallel implementing probabilistic spreading algorithm using MapReduce programming mode [J]. Journal of Shandong University(Engineering Science), 2015, 45(5): 22-28.
[6]	CHEN Hongxing, ZHOU Fengyu, TIAN Tian, JIANG Zhifei, CHEN Zhumin. Design of SOA interface model in service robot cloud computing platform [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(4): 31-39.
[7]	TIAN Guohui, XU Yaxiong. Cloud robotics: concept, architectures and key technologies [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(6): 47-54.
[8]	JI Tao, LI Yong-zhong. Blind data processing in cloud computing based on trusted computing mechanisms [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(5): 30-34.
[9]	ZHANG Ling-wei, WAN Wen-qiang. Study on the cost-sensitive ensemble learning algorithm based on the cloud computing platform [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(4): 19-23.
[10]	HAO Qian-hua1, HE Qing-hua1,2*, ZHU Jun-lin1, LI Sai-bai1, CHEN Zheng1, SHU Min-fei1. Experimental research of energy consumption of an electric forklift hydraulic lifting system with pressure accumulators [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(6): 80-84.
[11]	Meng Xiang-xu， Liu　Shi-jun， Wu Lei， Pan Li. Model and key technologies of cloud manufacturing [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(5): 13-20.