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Deadline and budget-constrained archimedes optimization algorithm for workflow scheduling in cloud

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Abstract

Cloud computing has revolutionized various domains over the past decade, providing accessible computational and storage resources at reduced costs. The exponential growth in data volumes and processing complexity, particularly due to the proliferation of IoT devices and applications across fields such as business, education, and agriculture, requires scalable computing resources and efficient processing. Workflow scheduling in cloud computing, an NP-hard optimization problem, involves allocating resources to tasks within a workflow and determining their execution sequence. Despite numerous heuristic, metaheuristic, and hybrid approaches, there remains a need for scheduling algorithms with lesser computational complexity to optimize makespan and cost efficiency, as well as ensure SLA compliance. This paper introduces a novel multi-objective metaheuristic solution, the Deadline and Budget constrained Archimedes Optimization Algorithm (ADB), which addresses workflow scheduling by optimizing makespan and cost while adhering to deadline and budget constraints. Extensive experiments on a well-known cloud simulation tool, Workflowsim, using scientific workflows demonstrate significant improvements in makespan (20%), cost (5%), resource utilization (15%), and energy consumption (9%). Performance observations on Pareto optimality metrics show that our approach has a higher hypervolume for 80% cases, it dominates state of the art by at least 83%, and the s-metric value of our approach is lower for 95% cases, alongside statistical validation using t-tests and ANOVA, confirming the efficacy of our method compared to state-of-the-art approaches.

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References

  1. Wu, Fuhui, Wu, Qingbo, Tan, Yusong: Workflow scheduling in cloud: a survey. J. Supercomput. 71, 3373–3418 (2015)

    Article  Google Scholar 

  2. Wang, Yang, Lu, Paul: Dataflow detection and applications to workflow scheduling. Concurr. Comput.: Pract. Exp. 23(11), 1261–1283 (2011)

    Article  Google Scholar 

  3. Xiao, Zhen, Song, Weijia, Chen, Qi.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Tran. Parallel Distrib. Syst. 24(6), 1107–1117 (2012)

    Article  Google Scholar 

  4. Rodriguez, Maria Alejandra, Buyya, Rajkumar: A taxonomy and survey on scheduling algorithms for scientific workflows in iaas cloud computing environments. Concurr. Comput.: Pract. Exp. 29(8), e4041 (2017)

    Article  Google Scholar 

  5. Zhao, Laiping, Ren, Yizhi, Sakurai, Kouichi: Reliable workflow scheduling with less resource redundancy. Parallel Comput. 39(10), 567–585 (2013)

    Article  MathSciNet  Google Scholar 

  6. Sanaj, M.S., Prathap, P.M. Joe: An enhanced round robin (err) algorithm for effective and efficient task scheduling in cloud environment. In 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), pages 107–110. IEEE, (2020)

  7. Tuli, Shreshth, Sandhu, Rajinder, Buyya, Rajkumar: Shared data-aware dynamic resource provisioning and task scheduling for data intensive applications on hybrid clouds using aneka. Future Gener. Comput. Syst. 106, 595–606 (2020)

    Article  Google Scholar 

  8. Ma, Xiaojin, Xu, Huahu, Gao, Honghao, Bian, Minjie: Real-time multiple-workflow scheduling in cloud environments. IEEE Trans. Netw. Serv. Manag. 18(4), 4002–4018 (2021)

    Article  Google Scholar 

  9. Tang, Xiaoyong, Cao, Wenbiao, Tang, Huiya, Deng, Tan, Mei, Jing, Liu, Yi., Shi, Cheng, Xia, Meng, Zeng, Zeng: Cost-efficient workflow scheduling algorithm for applications with deadline constraint on heterogeneous clouds. IEEE Trans. Parallel and Distrib. Syst. 33(9), 2079–2092 (2021)

    Article  Google Scholar 

  10. Medara, Rambabau, Singh, Ravi Shankar, Kumar, U. Selva, Barfa, Suraj: Energy efficient virtual machine consolidation using water wave optimization. In 2020 IEEE congress on evolutionary computation (CEC), pages 1–7. IEEE, (2020)

  11. Jia, Ya-Hui., Chen, Wei-Neng., Yuan, Huaqiang, Gu, Tianlong, Zhang, Huaxiang, Gao, Ying, Zhang, Jun: An intelligent cloud workflow scheduling system with time estimation and adaptive ant colony optimization. IEEE Trans. Syst., Man, and Cybern.: Syst. 51(1), 634–649 (2018)

    Article  Google Scholar 

  12. Chen, Xuan, Cheng, Long, Liu, Cong, Liu, Qingzhi, Liu, Jinwei, Mao, Ying, Murphy, John: A woa-based optimization approach for task scheduling in cloud computing systems. IEEE Syst. J. 14(3), 3117–3128 (2020)

    Article  Google Scholar 

  13. Anwar, Nazia, Deng, Huifang: A hybrid metaheuristic for multi-objective scientific workflow scheduling in a cloud environment. Appl. Sci. 8(4), 538 (2018)

    Article  Google Scholar 

  14. Manasrah, Ahmad M., Ali, Hanan Ba: Workflow scheduling using hybrid ga-pso algorithm in cloud computing. Wireless Communications and Mobile Computing, 2018, (2018)

  15. Medara, Rambabu, Singh, Ravi Shankar, et al.: Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization. Simul. Model. Pract. Theory 110, 102323 (2021)

    Article  Google Scholar 

  16. Medara, Rambabu, Singh, Ravi Shankar, Sompalli, Mahesh: Energy and cost aware workflow scheduling in clouds with deadline constraint. Concurr. Comput.: Pract. Exp. 34(13), e6922 (2022)

    Article  Google Scholar 

  17. Taghinezhad-Niar, Ahmad, Pashazadeh, Saeid, Taheri, Javid: Workflow scheduling of scientific workflows under simultaneous deadline and budget constraints. Clust. Comput. 24(4), 3449–3467 (2021)

    Article  Google Scholar 

  18. Taghinezhad-Niar, Ahmad, Pashazadeh, Saeid, Taheri, Javid: Energy-efficient workflow scheduling with budget-deadline constraints for cloud. Computing 104(3), 601–625 (2022)

    Article  MathSciNet  Google Scholar 

  19. Chen, Zong-Gan., Zhan, Zhi-Hui., Lin, Ying, Gong, Yue-Jiao., Gu, Tian-Long., Zhao, Feng, Yuan, Hua-Qiang., Chen, Xiaofeng, Li, Qing, Zhang, Jun: Multiobjective cloud workflow scheduling: a multiple populations ant colony system approach. IEEE Trans. Cybern. 49(8), 2912–2926 (2018)

    Article  Google Scholar 

  20. Gupta, Swati, Agarwal, Isha, Singh, Ravi Shankar: Workflow scheduling using jaya algorithm in cloud. Concurr. Comput.: Pract. Exp. 31(17), e5251 (2019)

    Article  Google Scholar 

  21. Pham, Thanh-Phuong, Fahringer, Thomas: Evolutionary multi-objective workflow scheduling for volatile resources in the cloud. IEEE Transactions on Cloud Computing, (2020)

  22. Nabi, Said, Ahmed, Masroor: Pso-rdal: Particle swarm optimization-based resource-and deadline-aware dynamic load balancer for deadline-constrained cloud tasks. The Journal of Supercomputing, pages 1–31, (2022)

  23. Singh, Sweta, Kumar, Rakesh: Energy efficient optimization with threshold based workflow scheduling and virtual machine consolidation in cloud environment. Wireless Personal Communications, pages 1–22, (2022)

  24. Sefati, SeyedSalar, Mousavinasab, Maryamsadat, Farkhady, Roya Zareh: Load balancing in cloud computing environment using the grey wolf optimization algorithm based on the reliability: performance evaluation. J. Supercomput. 78(1), 18–42 (2022)

    Article  Google Scholar 

  25. Khurana, S., Singh, R.: Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking. EAI Endorsed Trans. Scalable Inf. Syst. 7(24), e7 (2019)

    Google Scholar 

  26. Sharma, Mohan, Garg, Ritu: Higa: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers. Eng. Sci. Technol. Int. J. 23(1), 211–224 (2020)

    Google Scholar 

  27. Shukri, Sarah E., Al-Sayyed, Rizik, Hudaib, Amjad, Mirjalili, Seyedali: Enhanced multi-verse optimizer for task scheduling in cloud computing environments. Exp. Syst. Appl. 168, 114230 (2021)

    Article  Google Scholar 

  28. Sun, Zaixing, Zhang, Boyu, Gu, Chonglin, Xie, Ruitao, Qian, Bin, Huang, Hejiao: Et2fa: A hybrid heuristic algorithm for deadline-constrained workflow scheduling in cloud. IEEE Transactions on Services Computing, (2022)

  29. Attiya, Ibrahim, Elaziz, Mohamed Abd, Abualigah, Laith, Nguyen, Tu N., El-Latif, Ahmed A Abd: An improved hybrid swarm intelligence for scheduling iot application tasks in the cloud. IEEE Transactions on Industrial Informatics, (2022)

  30. Pirozmand, Poria, Javadpour, Amir, Nazarian, Hamideh, Pinto, Pedro, Mirkamali, Seyedsaeid, Ja’fari, Forough: Gsaga: a hybrid algorithm for task scheduling in cloud infrastructure. J. Supercomput. 78(15), 17423–17449 (2022)

    Article  Google Scholar 

  31. Taghinezhad-Niar, Ahmad, Taheri, Javid: Security, reliability, cost, and energy-aware scheduling of real-time workflows in compute-continuum environments. IEEE Transactions on Cloud Computing, (2024)

  32. Taghinezhad-Niar, Ahmad, Taheri, Javid: Reliability, rental-cost and energy-aware multi-workflow scheduling on multi-cloud systems. IEEE Trans. Cloud Comput. 11(3), 2681–2692 (2022)

    Article  Google Scholar 

  33. Wu, Quanwang, Zhou, MengChu, Zhu, Qingsheng, Xia, Yunni, Wen, Junhao: Moels: multiobjective evolutionary list scheduling for cloud workflows. IEEE Trans. Autom. Sci. Eng. 17(1), 166–176 (2019)

    Article  Google Scholar 

  34. Hashim, Fatma A., Hussain, Kashif, Houssein, Essam H., Mabrouk, Mai S., Al-Atabany, Walid: Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl. Intell. 51, 1531–1551 (2021)

    Article  Google Scholar 

  35. Juve, Gideon, Chervenak, Ann, Deelman, Ewa, Bharathi, Shishir, Mehta, Gaurang, Vahi, Karan: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. 29(3), 682–692 (2013)

    Article  Google Scholar 

  36. Zhang, Longxin, Ai, Minghui, Liu, Ke, Chen, Jianguo, Li, Kenli: Reliability enhancement strategies for workflow scheduling under energy consumption constraints in clouds. IEEE Transactions on Sustainable Computing, (2023)

  37. Yu, Jia, Buyya, Rajkumar: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Progr. 14(3–4), 217–230 (2006)

    Google Scholar 

  38. Rodriguez, Maria Alejandra, Buyya, Rajkumar: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)

    Article  Google Scholar 

  39. Rizvi, Naela, Ramesh, Dharavath, Wang, Lipo, Basava, Annappa: A workflow scheduling approach with modified fuzzy adaptive genetic algorithm in iaas clouds. IEEE Trans. Serv. Comput. 16(2), 872–885 (2022)

    Article  Google Scholar 

  40. Qiu, Huixian, Xia, Xuewen, Li, Yuanxiang, Deng, Xianli: A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence. Swarm and Evol. Comput. 78, 101291 (2023)

    Article  Google Scholar 

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Contributions

S.K. did all the implementations, generated the results, performed the validation tests, and wrote the main manuscript with continuous guidance and support of R.S.S. Figures and tables were prepared by R.S.S.All authors reviewed the manuscript.

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Correspondence to Shweta Kushwaha.

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Kushwaha, S., Singh, R.S. Deadline and budget-constrained archimedes optimization algorithm for workflow scheduling in cloud. Cluster Comput 28, 117 (2025). https://doi.org/10.1007/s10586-024-04702-1

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