A Systematic Survey and Taxonomy of Energy-Efficient Workflow Allocation Techniques in Cloud Computing Environments
Abstract
The cloud computing has revolutionized the way organizations manage and store data. The cloud-based services allow businesses and individuals to access computing resources over the Internet instead of using local servers. As a result of this shift, flexibility, scalability, and cost-efficiency have been enhanced, however, energy consumption has increased. As cloud computing grows, its environmental impact also increases. The cloud infrastructure requires massive amounts of electricity supply, cooling devices, etc. Therefore, energy consumption is one of the primary concerns of cloud computing researchers. In this context, energy-efficient workflow allocation means allocating tasks to virtual machines (VMs) as efficiently as possible in order to save energy, reduce time to complete tasks and lower costs. Since cloud services operate on a pay-per-use model, where users pay based on their usage, optimizing these factors directly benefits cloud providers and users. A systematic literature review (SLR) analyzed 49 studies published from 2015 to 2024, chosen from an initial pool of 585 papers in major academic databases. In this study, a comprehensive taxonomy for energy-efficient workflow allocation (EWA) in cloud computing. It categorizes models by environment (single or multicloud), workflow type (scientific or random), allocation approach (heuristic, meta-heuristic, or hybrid), workload type (static or dynamic), and quality of service (QoS) objectives and constraints. The quantitative analysis shows that 33% of studies used meta-heuristics, 39% used heuristics, and 28% used hybrid approaches. The most common optimization objectives were energy consumption (28%), monetary cost (23%), and makespan (27%). Deadlines (46%) were the most frequently addressed quality of service (QoS) constraint. This study helps researchers in selecting effective energy-efficient workflow allocation (EWA) strategies and highlights open issues, challenges, and future research directions. It serves as a valuable reference for those investigating energy efficiency in cloud computing environments.References
R. Buyya, C. Vecchiola, and S. Selvi, Mastering cloud computing: foundations and applications programming. 2013.
T. S. Somasundaram and K. Govindarajan, ‘CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud’, Future Generation Computer Systems. May 2014, doi: 10.1016/j.future.2013.12.024.
S. Suresh and S. Sakthivel, ‘A novel performance constrained power management framework for cloud computing using an adaptive node scaling approach’, Computers and Electrical Engineering, May 2017, doi: 10.1016/j.compeleceng.2017.04.018.
Y. Duan, G. Fu, N. Zhou, … X. S.-2015 I. 8th, and undefined 2015, ‘Everything as a service (XaaS) on the cloud: origins, current and future trends’, ieeexplore.ieee.org.
D. Talia, ‘Workflow Systems for Science: Concepts and Tools’, ISRN Software Engineering, vol. 2013, pp. 1–15, Jan. 2013, doi: 10.1155/2013/404525.
G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, and K. Vahi, ‘Characterizing and profiling scientific workflows’, Future Generation Computer Systems.Mar. 2013, doi: 10.1016/j.future.2012.08.015.
R. Medara, R. S. Singh, and Amit, ‘Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization’, Simul Model Pract Theory, 2021, doi: 10.1016/j.simpat.2021.102323.
Y. Gu and C. Budati, ‘Energy-aware workflow scheduling and optimization in clouds using bat algorithm’, Future Generation Computer Systems, Dec. 2020, doi: 10.1016/J.FUTURE.2020.06.031.
Z. Li, H. Yu, and G. Fan, ‘Cost-effective approaches for deadline-constrained workflow scheduling in clouds’, J Supercomput.May 2023, doi: 10.1007/s11227-022-04962-x.
V. Singh, I. Gupta, and P. K. Jana, ‘An Energy Efficient Algorithm for Workflow Scheduling in IaaS Cloud’, J Grid Comput, Sep. 2020,doi:10.1007/s10723-019-09490-2.
F. Cao, M. M. Zhu, and C. Q. Wu, ‘Energy-Efficient Resource Management for Scientific Workflows in Clouds’, in 2014 IEEE World Congress on Services, IEEE, 2014. doi: 10.1109/SERVICES.2014.76.
W. Zheng and S. Huang, ‘Deadline Constrained Energy-Efficient Scheduling for Workflows in Clouds’, in 2014 Second International Conference on Advanced Cloud and Big Data, IEEE, 2014
P. Arroba, J. M. Moya, J. L. Ayala, and R. Buyya, ‘Dynamic Voltage and Frequency Scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers’, Concurr Comput, May 2017, doi: 10.1002/CPE.4067.
B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, ‘Systematic literature reviews in software engineering – A systematic literature review’, Inf Softw Technol.Jan. 2009, doi: 10.1016/j.infsof.2008.09.009.
O. Dieste, A. Grimán, and N. Juristo, ‘Developing search strategies for detecting relevant experiments’, Empir Softw Eng, Oct.2009, doi: 10.1007/s10664-008-9091-7.
F. Wu, Q. Wu, and Y. Tan, ‘Workflow scheduling in cloud: a survey’, Journal of Supercomputing, Sep. 2015, doi: 10.1007/S11227-015-1438-4.
M. Masdari, S. ValiKardan, Z. Shahi, and S. I. Azar, ‘Towards workflow scheduling in cloud computing: A comprehensive analysis’, Journal of Network and Computer Applications,doi:10.1016/j.jnca.2016.01.018.
M. A. Rodriguez and R. Buyya, ‘A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments’, Concurr Comput, vol. 29, no. 8, Apr. 2017, doi: 10.1002/CPE.4041.
M. Adhikari, T. Amgoth, and S. N. Srirama, ‘A survey on scheduling strategies for workflows in cloud environment and emerging trends’, ACM Comput Surv, vol. 52, no. 4, Aug. 2019, doi: 10.1145/3325097.
M. Hosseinzzadeh, M. Y. Ghafour, H. K. Hma, Vo Bay, and A. Khoshnevis, ‘Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a ComprehensiveReview’,doi:10.1007/s10723-020-09533-z.
L. Versluis and A. Iosup, ‘A survey of domains in workflow scheduling in computing infrastructures: Community and keyword analysis, emerging trends, and taxonomies’, Future Generation Computer Systems,2021.doi:10.1016/j.future.2021.04.009.
Y. Kumar, S. Kaul, and Y.-C. Hu, ‘Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey’, Sustainable Computing: Informatics and Systems.Dec.2022,doi:10.1016/j.suscom.2022.100780.
R. Medara and R. S. Singh, A Review on Energy-Aware Scheduling Techniques for Workflows in IaaS Clouds,Springer US, 2022. doi: 10.1007/s11277-022-09621-1.
P. Verma, A. K. Maurya, and R. S. Yadav, ‘A survey on energy‐efficient workflow scheduling algorithms in cloud computing’, Softw Pract Exp,May2024.doi: 10.1002/spe.3292.
M. , & K. K. S. Menaka, ‘Workflow scheduling in cloud environment–Challenges, tools, limitations & methodologies: A review’, Measurement: Sensors..
M. Kalra and S. Singh, ‘Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach’, Wirel Pers Commun. doi: 10.1007/s11277-020-07759-4.
R. Medara and R. S. Singh, ‘Energy Efficient and Reliability Aware Workflow Task Scheduling in Cloud Environment’, Wirel Pers Commun.doi: 10.1007/s11277-021-08263-z.
K. K. Chakravarthi, P. Neelakantan, L. Shyamala, and V. Vaidehi, ‘Reliable budget aware workflow scheduling strategy on multi-cloud environment’, Cluster Comput. Apr. 2022, doi: 10.1007/S10586-021-03464-4.
S. Chitra, ‘Multi Criteria based Resource Score Heuristic for Cloud Workflow Scheduling’, Procedia Comput Sci.doi: 10.1016/j.procs.2020.01.099.
S. Saharawat and M. Kalra, ‘Deadline Constrained Energy-Efficient Workflow Scheduling Heuristic for Cloud’.doi: 10.1007/978-981-15-3020-3_33.
M. Hariri, M. Nouri-Baygi, and S. Abrishami, ‘A hybrid algorithm for scheduling scientific workflows in IaaS cloud with deadline constraint’, J Supercomput, 16975–16996, Oct. 2022, doi: 10.1007/s11227-022-04563-8.
M. Sajid, Z. Raza, and M. Shahid, ‘Hybrid bio-inspired scheduling algorithms for batch of tasks on heterogeneous computing system’, International Journal of Bio-Inspired Computation.2018, doi: 10.1504/IJBIC.2018.091698.
N. Rizvi, R. Dharavath, and D. R. Edla, ‘Cost and makespan aware workflow scheduling in IaaS clouds using hybrid spider monkey optimization’, Simul Model Pract Theory, 2021, doi: 10.1016/j.simpat.2021.102328.
M. Zeedan, G. Attiya, and N. El-Fishawy, ‘Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing’, Computing, 2022, doi: 10.1007/S00607-022-01116-Y/FULLTEXT.HTML.
V. K. Sriperambuduri and N. M, ‘A Hybrid Grey Wolf Optimization and Constriction Factor based PSO Algorithm for Workflow Scheduling in Cloud’, International Journal on Recent and Innovation Trends in Computing and Communication, Aug. 2023, doi: 10.17762/ijritcc.v11i9s.7744.
B. Wang, C. Wang, Y. Song, J. Cao, X. Cui, and L. Zhang, ‘A survey and taxonomy on workload scheduling and resource provisioning in hybrid clouds’, Cluster Comput, vol. 23, no. 4, pp. 2809–2834, Dec. 2020, doi: 10.1007/s10586-020-03048-8.
M. Grami, ‘An energy-aware scheduling of dynamic workflows using big data similarity statistical analysis in cloud computing’, Journal of Supercomputing, Feb. 2022, doi: 10.1007/S11227-021-04016-8.
D. Alsadie, Z. Tari, E. J. Alzahrani, and A. Y. Zomaya, ‘Dynamic resource allocation for an energy efficient VM architecture for cloud computing’, ACM International Conference Proceeding Series, Jan. 2018, doi: 10.1145/3167918.3167952.
S. Xue, Y. Peng, X. Xu, J. Zhang, C. Shen, and F. Ruan, ‘DSM: a dynamic scheduling method for concurrent workflows in cloud environment’, Cluster Comput, Jan. 2019, doi: 10.1007/S10586-017-1189-5.
E. C. da Silva and P. H. R. Gabriel, ‘A comprehensive review of evolutionary algorithms for multiprocessor DAG scheduling’,Computation.Jun. 2020, doi:10.3390/COMPUTATION8020026.
M. Shahid, Z. Raza, and M. Sajid, ‘Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid’, Journal of Systems and Software,2015,doi:10.1016/j.jss.2015.06.016.
Z. Zhu and X. Tang, ‘Deadline-constrained workflow scheduling in IaaS clouds with multi-resource packing’, Future Generation Computer Systems.Dec.2019,doi: 0.1016/j.future.2019.07.043.
M. Hussain, L. F. Wei, A. Rehman, F. Abbas, A. Hussain, and M. Ali, ‘Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers’, Future Generation Computer Systems,Jul.2022. doi: 10.1016/j.future.2022.02.018.
A. K. Maurya and A. K. Tripathi, ‘Deadline-constrained algorithms for scheduling of bag-of-tasks and workflows in cloud computing environments’, ACM International Conference Proceeding Series,Mar.2018.doi:10.1145/3195612.3195618.
J.-S. Vöckler, G. Juve, E. Deelman, M. Rynge, and G. B. Berriman, ‘Robust deadline-constrained resource provisioning and workflow scheduling algorithm for handling performance uncertainty in IaaS clouds’, UCC 2017 Companion - Companion Proceedings of the 10th International Conference on Utility and Cloud Computing, pp. 29–34, Dec. 2017, doi: 10.1145/3147234.3148110.
V. Arabnejad, K. Bubendorfer, and B. Ng, ‘Deadline Distribution Strategies for Scientific Workflow Scheduling in Commercial Clouds’, Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing, 2016, doi: 10.1145/3127404.
X. Tang, X. Li, and Z. Fu, ‘Budget-constraint stochastic task scheduling on heterogeneous cloud systems’, Concurr Comput, vol. 29, no. 19, Oct. 2017, doi: 10.1002/CPE.4210.
H. Li, D. Wang, G. Xu, Y. Yuan, and Y. Xia, ‘Improved swarm search algorithm for scheduling budget-constrained workflows in the cloud’, Soft comput.Apr. 2022, doi: 10.1007/s00500-022-06782-w.
Y. Qin, H. Wang, S. Yi, X. Li, and L. Zhai, ‘An energy-aware scheduling algorithm for budget-constrained scientific workflows based on multi-objective reinforcement learning’, Journal of Supercomputing. Jan. 2020, doi: 10.1007/S11227-019-03033-Y.
N. Rizvi and D. Ramesh, ‘Fair budget constrained workflow scheduling approach for heterogeneous clouds’, Cluster Comput, 2020.doi: 10.1007/s10586-020-03079-1.
W. Ahmad, B. Alam, and A. Atman, ‘An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment’, J Supercomput.Oct. 2021, doi: 10.1007/s11227-021-03733-4.
R. Garg, M. Mittal, and L. H. Son, ‘Reliability and energy efficient workflow scheduling in cloud environment’, Cluster Comput.Dec. 2019, doi: 10.1007/s10586-019-02911-7.
H. A. Hassan, S. A. Salem, and E. M. Saad, ‘A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment’, Future Generation Computer Systems, vol. 112, pp. 431–448, Nov. 2020, doi: 10.1016/j.future.2020.05.040.
M. I. Khaleel, ‘PPR-RM: Performance-to-Power Ratio, Reliability and Makespan — aware scientific workflow scheduling based on a coalitional game in the cloud’, Journal of Network and Computer Applications. Nov. 2022, doi: 10.1016/j.jnca.2022.103478.
T. Dong, F. Xue, H. Tang, and C. Xiao, ‘Deep reinforcement learning for fault-tolerant workflow scheduling in cloud environment’, Applied Intelligence, 2022, doi: 10.1007/S10489-022-03963-W.
P. Guo and Z. Xue, ‘Cost-effective fault-tolerant scheduling algorithm for real-time tasks in cloud systems’, in 2017 IEEE 17th International Conference on Communication Technology (ICCT).Oct. 2017, doi: 10.1109/ICCT.2017.8359968.
Z. Li, J. Ge, H. Hu, W. Song, H. Hu, and B. Luo, ‘Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds’, IEEE Trans Serv Comput.Jul. 2018, doi: 10.1109/TSC.2015.2466545.
H. Ji, W. Bao, and X. Zhu, ‘Adaptive workflow scheduling for diverse objectives in cloud environments’, Transactions on Emerging Telecommunications Technologies, Feb. 2017, doi: 10.1002/ett.2941.
X. Xu, W. Dou, X. Zhang, and J. Chen, ‘EnReal: An Energy-Aware Resource Allocation Method for Scientific Workflow Executions in Cloud Environment’, IEEE Transactions on Cloud Computing, Apr. 2016, doi: 10.1109/TCC.2015.2453966.
P. Kaur and S. Mehta, ‘Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm’, J Parallel Distrib Comput.Mar.2017,doi:10.1016/j.jpdc.2016.11.003.
G. Yao, Y. Ding, Y. Jin, and K. Hao, ‘Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system’, Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 21, no. 15, pp. 4309–4322, Aug. 2017, doi: 10.1007/S00500-016-2063-8.
S. J. Nirmala and S. M. S. Bhanu, ‘Catfish-PSO based scheduling of scientific workflows in IaaS cloud’, Computing, vol. 98, no. 11, pp. 1091–1109, 2016, doi: 10.1007/s00607-016-0494-9.
J. Jiang, Y. Lin, G. Xie, L. Fu, and J. Yang, ‘Time and Energy Optimization Algorithms for the Static Scheduling of Multiple Workflows in Heterogeneous Computing System’, J Grid Comput, vol. 15, no. 4, pp. 435–456, Dec. 2017, doi: 10.1007/S10723-017-9391-5.
N. Rehani and R. Garg, ‘Meta-heuristic based reliable and green workflow scheduling in cloud computing’, International Journal of System Assurance Engineering and Management, vol. 9, no. 4, pp. 811–820, Aug. 2018, doi: 10.1007/s13198-017-0659-8.
S. Elsherbiny, E. Eldaydamony, M. Alrahmawy, and A. E. Reyad, ‘2017 Shyamaa An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment _ Elsevier Enhanced Reader’.
A. Rehman, S. S. Hussain, Z. ur Rehman, S. Zia, and S. Shamshirband, ‘Multi-objective approach of energy efficient workflow scheduling in cloud environments’, Concurr Comput, vol. 31, no. 8, pp. 1–20, 2019, doi: 10.1002/cpe.4949.
M. Safari and R. Khorsand, ‘Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment’, Simul Model Pract Theory, vol. 87, pp. 311–326, Sep. 2018, doi: 10.1016/J.SIMPAT.2018.07.006.
X. Qu, P. Xiao, and L. Huang, ‘Improving the energy efficiency and performance of data-intensive workflows in virtualized clouds’, Journal of Supercomputing, Jul. 2018, doi: 10.1007/S11227-018-2344-3/FULLTEXT.HTML.
N. Anwar and H. Deng, ‘A hybrid metaheuristic for multi-objective scientific workflow scheduling in a cloud environment’, Applied Sciences (Switzerland), vol. 8, no. 4, Mar. 2018, doi: 10.3390/APP8040538.
A. M. Manasrah and H. B. Ali, ‘Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing’, Wirel Commun Mob Comput, vol. 2018, 2018, doi: 10.1155/2018/1934784.
B. Qureshi, ‘Profile-based power-aware workflow scheduling framework for energy-efficient data centers’, Future Generation Computer Systems, May 2019, doi: 10.1016/j.future.2018.11.010.
Y. Gao, S. Zhang, and J. Zhou, ‘A Hybrid Algorithm for Multi-Objective Scientific Workflow Scheduling in IaaS Cloud’, IEEE Access,2019,doi:10.1109/ACCESS.2019.2939294.
Y. Li, W. Tang, and G. Liu, ‘HPEFT for hierarchical heterogeneous multi-DAG in a multigroup scan UPA system’, Electronics (Switzerland),May 2019, doi: 10.3390/ELECTRONICS8050498.
S. Gupta, I. Agarwal, and R. S. Singh, ‘Workflow scheduling using Jaya algorithm in cloud’, Concurr Comput, vol. 31, no. 17, 2019, doi: 10.1002/cpe.5251.
A. Mohammadzadeh, M. Masdari, F. S. Gharehchopogh, and A. Jafarian, ‘A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling’, Cluster Comput, Jun. 2021, doi: 10.1007/S10586-020-03205-Z.
L. Zhang, L. Wang, Z. Wen, M. Xiao, and J. Man, ‘Minimizing Energy Consumption Scheduling Algorithm of Workflows With Cost Budget Constraint on Heterogeneous Cloud Computing Systems’, IEEE Access.2020.
M. K. S. & F. Y. Ali Asghari, ‘Online scheduling of dependent tasks of cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents’, 2020.
R. Ranjan, I. S. Thakur, G. S. Aujla, N. Kumar, and A. Y. Zomaya, ‘Energy-Efficient Workflow Scheduling Using Container-Based Virtualization in Software-Defined Data Centers’, IEEE Trans Industr Inform, Dec. 2020, doi: 10.1109/TII.2020.2985030.
M. Alaei, R. Khorsand, and M. Ramezanpour, ‘An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud’, Appl Soft Comput. Feb. 2021, doi: 10.1016/J.ASOC.2020.106895.
S. Saeedi, R. Khorsand, S. Ghandi Bidgoli, and M. Ramezanpour, ‘Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing’, Comput Ind Eng. Sep. 2020, doi: 10.1016/j.cie.2020.106649.
Y. Qin, H. Wang, S. Yi, X. Li, and L. Zhai, ‘A multi-objective reinforcement learning algorithm for deadline constrained scientific workflow scheduling in clouds’, Front Comput Sci, Oct. 2021, doi: 10.1007/s11704-020-9273-z.
A. Mohammadzadeh, M. Masdari, F. S. Gharehchopogh, and A. Jafarian, ‘Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing’, Evol Intell, Dec. 2021, doi: 10.1007/s12065-020-00479-5.
A. Ramathilagam and K. Vijayalakshmi, ‘Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm’, International Journal of Communication Systems, Mar. 2021. doi: 10.1002/dac.4746.
A. Belgacem and K. Beghdad-Bey, ‘Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost’, Cluster Comput, vol. 25, no. 1, pp. 579–595, Feb. 2022, doi: 10.1007/s10586-021-03432-y.
J. Kakkottakath Valappil Thekkepuryil, D. P. Suseelan, and P. M. Keerikkattil, ‘An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment’, Cluster Comput, doi: 10.1007/s10586-021-03269-5.
M. A. Khan, ‘A cost-effective power-aware approach for scheduling cloudlets in cloud computing environments’, Journal of Supercomputing, Jan. 2022, doi: 10.1007/s11227-021-03894-2.
S. Danthuluri and S. Chitnis, ‘Energy and cost optimization mechanism for workflow scheduling in the cloud’, Mater Today Proc, Jul.2021, doi: 10.1016/j.matpr.2021.07.168.
L. Zhang, L. Wang, M. Xiao, Z. Wen, and C. Peng, ‘EM_WOA: A budget-constrained energy consumption optimization approach for workflow scheduling in clouds’, Peer Peer Netw Appl.doi: 10.1007/s12083-021-01267-3.
G.R.Garg,Neha.Neeraj,Raj,Manish.Gupta,Indrajeet.Kumar,Vinay.Sinha, ‘Energy-aware scientific workflow scheduling in cloud environment’, Cluster Comput, doi: 10.1007/s10586-022-03613-3.
A. Taghinezhad-Niar, S. Pashazadeh, and J. Taheri, ‘Energy-efficient workflow scheduling with budget-deadline constraints for cloud’, Computing, Mar. 2022, doi: 10.1007/s00607-021-01030-9.
H. Li, G. Xu, D. Wang, M. Zhou, Y. Yuan, and A. Alabdulwahab, ‘Chaotic-Nondominated-Sorting Owl Search Algorithm for Energy-Aware Multi-Workflow Scheduling in Hybrid Clouds’, IEEE Transactions on Sustainable Computing, Jul. 2022, doi: 10.1109/TSUSC.2022.3144357.
A. Choudhary, M. C. Govil, G. Singh, L. K. Awasthi, and E. S. Pilli, ‘Energy-aware scientific workflow scheduling in cloud environment’, Cluster Comput, 2022, doi: 10.1007/S10586-022-03613-3.
M. Cao, Y. Li, X. Wen, Y. Zhao, and J. Zhu, ‘Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing’, Egyptian Informatics Journal, Jul.2023, doi: 0.1016/j.eij.2023.04.002.
Z. Sun, H. Huang, Z. Li, C. Gu, R. Xie, and B. Qian, ‘Efficient, economical and energy-saving multi-workflow scheduling in hybrid cloud’, Expert Syst Appl, Oct. 2023, doi: 10.1016/j.eswa.2023.120401.
H. Li, X. Li, J. Xu, and L. Chen, ‘Entropy based swarm intelligent searching for scheduling deadline constrained workflows in hybrid cloud’, International Journal of Machine Learning and Cybernetics, Apr. 2024, doi: 10.1007/s13042-023-01962-y.
M. I. Khaleel, M. Safran, S. Alfarhood, and M. Zhu, ‘Energy-latency trade-off analysis for scientific workflow in cloud environments: The role of processor utilization ratio and mean grey wolf optimizer’, Engineering Science and Technology, an International Journal, Feb. 2024, doi: 10.1016/j.jestch.2023.101611.
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