{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:45:15Z","timestamp":1767339915039,"version":"build-2065373602"},"reference-count":82,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Abdulaziz University, Jeddah, Saudi Arabia","award":["G-1561-144-1440"],"award-info":[{"award-number":["G-1561-144-1440"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Cloud computing, a superset of heterogeneous distributed computing, allows sharing of geographically dispersed resources across multiple organizations on a rental basis using virtualization as per demand. In cloud computing, workflow allocation to achieve the optimum schedule has been reported to be NP-hard. This paper proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize makespan in the cloud computing environment. The model has two phases: task prioritization and task allocation. The task prioritization phase begins by dividing workflows into the number of partitions as per the level attribute; after that, upward rank is employed to determine the partition-wise task allocation order. In the allocation phase, the best-suited virtual machine is determined to offer the lowest finish time for each task in partition-wise mapping to minimize the workflow task\u2019s completion time. The model considers the inter-task communication between the cooperative workflow tasks. A comparative performance evaluation of LMHEFT has been conducted with the competitive models from the literature implemented in MATLAB, i.e., heterogeneous earliest finish time (HEFT) and dynamic level scheduling (DLS), on makespan, flowtime, and utilization. The experimental findings indicate that LMHEFT surpasses HEFT and DLS in terms of makespan 15.51% and 85.12% when varying the number of workflows, 41.19% and 86.73% when varying depth levels, and 13.74% and 80.24% when varying virtual machines, respectively. Further statistical analysis has been carried out to confirm the hypothesis developed in the simulation study by using normality tests, homogeneity tests, and the Kruskal\u2013Wallis test.<\/jats:p>","DOI":"10.3390\/a18020099","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T12:12:16Z","timestamp":1739362336000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Levelized Multiple Workflow Heterogeneous Earliest Finish Time Allocation Model for Infrastructure as a Service (IaaS) Cloud Environment"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1873-9121","authenticated-orcid":false,"given":"Farheen","family":"Bano","sequence":"first","affiliation":[{"name":"Industrial Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"given":"Faisal","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Workday Inc., Pleasanton, CA 94588, USA"}]},{"given":"Mohammad","family":"Shahid","sequence":"additional","affiliation":[{"name":"Department of Commerce, Aligarh Muslim University, Aligarh 202002, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0668-9796","authenticated-orcid":false,"given":"Mahfooz","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of MCA, G. L. Bajaj Institute of Technology and Management, Greater Noida 201306, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5666-3813","authenticated-orcid":false,"given":"Faraz","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Gandhi Institute of Technology and Management (GITAM) (Deemed to be University), Hyderabad Campus, Hyderabad 502329, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8822-5332","authenticated-orcid":false,"given":"Mohammad","family":"Sajid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aligarh Muslim University, Aligarh 202002, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"ref_1","first-page":"224","article-title":"Cloud computing","volume":"321","author":"Boss","year":"2007","journal-title":"IBM White Pap."},{"key":"ref_2","first-page":"1","article-title":"A Comprehensive Survey on Cloud Computing\u2014Architecture, Tools, Technologies, and Open Issues","volume":"12","author":"Jawed","year":"2022","journal-title":"Int. J. Cloud Appl. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alam, M., Mustajab, S., Shahid, M., and Ahmad, F. (2023;, January 3\u20134). Cloud Computing: Architecture, Vision, Challenges, Opportunities, and Emerging Trends. Proceedings of the 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS60361.2023.10425507"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.future.2014.10.008","article-title":"Pegasus, a workflow management system for science automation","volume":"46","author":"Deelman","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6477","DOI":"10.4249\/scholarpedia.6477","article-title":"Petri net","volume":"3","author":"Adam","year":"2008","journal-title":"Scholarpedia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"371","DOI":"10.3233\/FUN-2008-86401","article-title":"Towards Building the State Class Graph of the TSPN Model","volume":"86","author":"Abdelli","year":"2008","journal-title":"Fundam. Informaticae"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2015.04.019","article-title":"Taxonomies of workflow scheduling problem and techniques in the Cloud","volume":"52","author":"Smanchat","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1007\/s11227-015-1438-4","article-title":"Workflow scheduling in Cloud: A survey","volume":"71","author":"Wu","year":"2015","journal-title":"J. Supercomput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/71.993206","article-title":"Performance-effective and low-complexity task scheduling for heterogeneous computing","volume":"13","author":"Topcuoglu","year":"2002","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_10","unstructured":"Iverson, M., Ozuner, F., and Follen, G. (1995, January 24\u201325). Parallelizing Existing Applications in a Distributed Heterogeneous Environment. Proceedings of the 4th Heterogeneous Computing Workshop (HCW\u201995), Santa Barbara, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1504\/IJGUC.2014.060223","article-title":"Level-based batch scheduling strategies for computational grid","volume":"5","author":"Shahid","year":"2014","journal-title":"Int. J. Grid Util. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, J., Buyya, R., and Ramamohanarao, K. (2008). Workflow scheduling algorithms for grid computing. Studied Computer Intelligence, Springer.","DOI":"10.1007\/978-3-540-69277-5_7"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1016\/j.future.2008.08.004","article-title":"Multi-cost job routing and scheduling in grid networks","volume":"25","author":"Stevens","year":"2009","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/s11227-011-0578-4","article-title":"A market-oriented hierarchical scheduling strategy in cloud workflow systems","volume":"63","author":"Wu","year":"2013","journal-title":"J. Supercomput."},{"key":"ref_15","first-page":"482","article-title":"Scheduling workflow applications with makespan and reliability constraints","volume":"12","author":"Abawajy","year":"2018","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, Q., Yun, D., Lin, X., Gu, Y., Lin, W., and Liu, Y. (2013). On workflow scheduling for end-to-end performance optimization in distributed network environments. Job Scheduling Strategies for Parallel Processing, Springer.","DOI":"10.1007\/978-3-642-35867-8_5"},{"key":"ref_17","unstructured":"Garey, M.R., and Johnson, D.S. (1979). Computers and Intractability, Freeman."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/71.207593","article-title":"A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures","volume":"4","author":"Sih","year":"1993","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s10723-009-9144-1","article-title":"Towards the scheduling of multiple workflows on computational grids","volume":"8","author":"Bittencourt","year":"2010","journal-title":"J. Grid Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Blythe, J., Jain, S., Deelman, E., Gil, Y., Vahi, K., Mandal, A., and Kennedy, K. (2005, January 9\u201312). Task scheduling strategies for workflow-based applications in grids. Proceedings of the Cluster Computing and the Grid, CCGrid 2005, Cardiff, UK. IEEE International Symposium on 2005.","DOI":"10.1109\/CCGRID.2005.1558639"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, M.M., Heymann, E., and Senar, M.A. (2006, January 6\u20139). Analysis of dynamic heuristics for workflow scheduling on grid systems. Proceedings of the Fifth International Symposium on Parallel and Distributed Computing, Timisoara, Romania.","DOI":"10.1109\/ISPDC.2006.9"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1145\/1084805.1084816","article-title":"Scheduling of scientific workflows in the askalon grid environment","volume":"34","author":"Wieczorek","year":"2005","journal-title":"ACM SIGMOD Rec."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.future.2017.09.054","article-title":"A novel cost-efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources","volume":"79","author":"Singh","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.future.2019.04.029","article-title":"Dynamic multi-workflow scheduling: A deadline and cost-aware approach for commercial clouds","volume":"100","author":"Arabnejad","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/978-981-16-9416-5_17","article-title":"Security prioritized heterogeneous earliest finish time workflow allocation algorithm for cloud computing","volume":"Volume 1","author":"Alam","year":"2022","journal-title":"Congress on Intelligent Systems: Proceedings of CIS 2021"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.jss.2015.06.016","article-title":"Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid","volume":"108","author":"Shahid","year":"2015","journal-title":"J. Syst. Softw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/71.80160","article-title":"Hypertool: A programming aid for message-passing systems","volume":"1","author":"Wu","year":"1990","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1109\/71.503776","article-title":"Dynamic critical-path scheduling: An effective technique for allocating task graphs to multiprocessors","volume":"7","author":"Kwok","year":"1996","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TPDS.2013.57","article-title":"List scheduling algorithm for heterogeneous systems by an optimistic cost table","volume":"25","author":"Arabnejad","year":"2013","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.future.2020.05.040","article-title":"A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment","volume":"112","author":"Hassan","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1007\/s11227-022-04729-4","article-title":"A novel technique to optimize quality of service for directed acyclic graph (DAG) scheduling in cloud computing environment using heuristic approach","volume":"79","author":"Rajak","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alam, M., Shahid, M., and Mustajab, S. (2022, January 15\u201317). Security Oriented Deadline Aware Workflow Allocation Strategy for Infrastructure as a Service Clouds. Proceedings of the 2022 3rd International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates.","DOI":"10.1109\/ICCAKM54721.2022.9990406"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shahid, M., Ashraf, Z., Alam, M., Ahmad, F., and Imran, M. (2021, January 19\u201320). A Multi-Objective Workflow Allocation Strategy in IaaS Cloud Environment. Proceedings of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS51004.2021.9397081"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cirou, B., and Jeannot, E. (2001). Triplet: A clustering scheduling algorithm for heterogeneous systems. Proceedings International Conference on Parallel Processing Workshops, Valencia, Spain, 3\u20137 September 2001, IEEE.","DOI":"10.1109\/ICPPW.2001.951956"},{"key":"ref_35","unstructured":"Boeres, C., and Rebello, V.E. (2004, January 27\u201329). A cluster-based strategy for scheduling task on heterogeneous processors. Proceedings of the IEEE in Computer Architecture and High Performance Computing, SBAC-PAD, 16th Symposium, Petr\u00f3polis, Brazil."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/71.722221","article-title":"On exploiting task duplication in parallel program scheduling","volume":"9","author":"Ahmad","year":"1998","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_37","unstructured":"Li, G., Chen, D., Wang, D., and Zhang, D. (2003, January 22\u201326). Task clustering and scheduling to multiprocessors with duplication. Proceedings of the Parallel and Distributed Processing Symposium, Nice, France."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/0020-0190(94)90012-4","article-title":"Two-way dominant sequence clustering for processor scheduling","volume":"49","author":"Kim","year":"1994","journal-title":"Inf. Process. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jedari, B., and Dehghan, M. (2009). Efficient DAG scheduling with resource-aware clustering for heterogeneous systems. Computer and Information Science 2009, Springer.","DOI":"10.1007\/978-3-642-01209-9_23"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1109\/TPDS.2016.2526682","article-title":"Clustering-based task scheduling in a large number of heterogeneous processors","volume":"27","author":"Kanemitsu","year":"2016","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_41","unstructured":"Chung, Y.C., and Ranka, S. (1992, January 16\u201320). Applications and performance analysis of a compile-time optimization approach for list scheduling algorithms on distributed memory multiprocessors. Proceedings of the Supercomputing \u201992, Minneapolis, MN, USA."},{"key":"ref_42","unstructured":"Liou, J.C., and Palis, M.A. (1996, January 23\u201326). An efficient task clustering heuristic for scheduling dags on multiprocessors. Proceedings of the Multiprocessors, Workshop on Resource Management, Symposium of Parallel and Distributed Processing, New Orleans, LA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1504\/IJHPCN.2017.086534","article-title":"Communication-aware task scheduling algorithm for heterogeneous computing","volume":"10","author":"Huang","year":"2017","journal-title":"Int. J. High Perform. Comput. Netw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"173884","DOI":"10.1109\/ACCESS.2019.2956759","article-title":"DBEFT: A Dependency-Ratio Bundling Earliest Finish Time Algorithm for Heterogeneous Computing","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/71.655248","article-title":"Optimal scheduling algorithm for distributed-memorymachines","volume":"9","author":"Darbha","year":"1998","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_46","unstructured":"Park, G.L., Shirazi, B., and Marquis, J. (1997, January 1\u20135). Dfrn: A new approach for duplication-based scheduling for distributed memory multiprocessor systems. Proceedings of the 11th International Parallel Processing Symposium, Geneva, Switzerland."},{"key":"ref_47","unstructured":"Chen, H., Shirazi, B., and Marquis, J. (1993, January 20\u201322). Performance evaluation of a novel scheduling method: Linear clustering with task duplication. Proceedings of the 2nd International Conference on Parallel and Distributed Systems, San Diego, CA, USA."},{"key":"ref_48","first-page":"8","article-title":"Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach","volume":"47","author":"Wang","year":"1997","journal-title":"IEEE Trans Parallel Distrib Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/71.265940","article-title":"A genetic algorithm for multiprocessor scheduling","volume":"5","author":"Hou","year":"1994","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1006\/jpdc.1997.1395","article-title":"Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm","volume":"47","author":"Kwok","year":"1997","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TPDS.2004.38","article-title":"An incremental genetic algorithm approach to multiprocessor scheduling","volume":"15","author":"Wu","year":"2004","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1109\/71.790598","article-title":"Genetic scheduling for parallel processor systems: Comparative studies and performance issues","volume":"10","author":"Zomaya","year":"1999","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.future.2019.03.005","article-title":"A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud\u2013edge environment","volume":"97","author":"Xie","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1007\/s00500-017-2897-8","article-title":"Smart PSO-based secured scheduling approaches for scientific workflows in cloud computing","volume":"23","author":"Sujana","year":"2019","journal-title":"Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Vinothina, V., and Sridaran, R. (2018). An Approach for Workflow Scheduling in Cloud Using ACO. Big Data Analytics, Springer.","DOI":"10.1007\/978-981-10-6620-7_50"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1007\/s11227-021-03978-z","article-title":"An ACO-based multi-objective optimization for cooperating VM placement in cloud data center","volume":"78","author":"Karmakar","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2150035","DOI":"10.1142\/S0219649221500350","article-title":"SWSA: A Hybrid Scientific Workflow Scheduling Algorithm Based on Metaheuristic Approach in Cloud Computing Environment","volume":"20","author":"Abbasi","year":"2021","journal-title":"J. Inf. Knowl. Manag."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e4746","DOI":"10.1002\/dac.4746","article-title":"Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm","volume":"34","author":"Ramathilagam","year":"2021","journal-title":"Int. J. Commun. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Hejji, D.J., Nassif, A.B., Nasir, Q., and AbuTalib, M. (2020, January 3\u20135). Systematic Literature Review: Metaheuristics-based Approach for Workflow Scheduling in Cloud. Proceedings of the 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), Sharjah, United Arab Emirates.","DOI":"10.1109\/CCCI49893.2020.9256692"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"106411","DOI":"10.1016\/j.asoc.2020.106411","article-title":"Multi-objective scheduling strategy for scientific workflows in cloud environment: A Firefly-based approach","volume":"93","author":"Adhikari","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1007\/s10489-020-01875-1","article-title":"Cost-effective workflow scheduling approach on Cloud under deadline constraint using firefly algorithm","volume":"51","author":"Chakravarthi","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.procs.2016.09.032","article-title":"A budget-constrained time and reliability optimization bat algorithm for scheduling workflow applications in clouds","volume":"98","author":"Kaur","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.future.2020.06.031","article-title":"Energy-aware workflow scheduling and optimization in clouds using bat algorithm","volume":"113","author":"Gu","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.future.2018.01.005","article-title":"A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing","volume":"83","author":"Choudhary","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"122009","DOI":"10.1016\/j.eswa.2023.122009","article-title":"A two-stage preference driven multi-objective evolutionary algorithm for workflow scheduling in the Cloud","volume":"238","author":"Xie","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Saovapakhiran, B., Michailidis, G., and Devetsikiotis, M. (2011, January 5\u20139). Aggregated-DAG scheduling for job flow maximization in heterogeneous cloud computing. Proceedings of the 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, Houston, TX, USA.","DOI":"10.1109\/GLOCOM.2011.6133611"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s10723-012-9215-6","article-title":"Multiple workflow scheduling strategies with user run time estimates on a grid","volume":"10","author":"Tchernykh","year":"2012","journal-title":"J. Grid Comput."},{"key":"ref_68","unstructured":"Zhao, H., and Sakellariou, R. (2006, January 25\u201329). Scheduling multiple dags onto heterogeneous systems. Proceedings of the Parallel and Distributed Processing Symposium, 20th International, IPDPS\u201906, Rhodes Island, Greece."},{"key":"ref_69","first-page":"22","article-title":"Multiple DAGs reliability model and fault-tolerant scheduling algorithm in cloud computing system","volume":"18","author":"Jing","year":"2014","journal-title":"Comput. Model. New Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s11227-014-1361-0","article-title":"Adaptive multiple-workflow scheduling with task rearrangement","volume":"71","author":"Chen","year":"2015","journal-title":"J. Supercomput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1016\/j.procs.2015.05.442","article-title":"A clustering-based approach to static scheduling of multiple workflows with soft deadlines in heterogeneous distributed systems","volume":"51","author":"Bochenina","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.energy.2017.02.069","article-title":"Energy-aware stochastic scheduler for batch of precedence-constrained jobs on heterogeneous computing system","volume":"125","author":"Sajid","year":"2017","journal-title":"Energy"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Shahid, M., and Raza, Z. (2014, January 11\u201313). Performance evaluation of Static Level based Batch Scheduling Strategy (SLBBS) for computational grid. Proceedings of the 2014 International Conference on Parallel, Distributed and Grid Computing, Solan, India.","DOI":"10.1109\/PDGC.2014.7030736"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Deng, F., Lai, M., and Geng, J. (2019, January 12\u201315). Multi-workflow scheduling based on genetic algorithm. Proceedings of the 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China.","DOI":"10.1109\/ICCCBDA.2019.8725731"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"101916","DOI":"10.1016\/j.sysarc.2020.101916","article-title":"TOPSIS inspired budget and deadline aware multi-workflow scheduling for cloud computing","volume":"114","author":"Chakravarthi","year":"2021","journal-title":"J. Syst. Archit."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"4002","DOI":"10.1109\/TNSM.2021.3125395","article-title":"Real-time multiple-workflow scheduling in cloud environments","volume":"18","author":"Ma","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s10586-022-03819-5","article-title":"Security prioritized multiple workflow allocation model under precedence constraints in cloud computing environment","volume":"27","author":"Alam","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e8337","DOI":"10.1002\/cpe.8337","article-title":"A Multi-Workflow Scheduling Approach With Explicit Evolutionary Multi-Objective Multi-Task Optimization Algorithm in Cloud Environment","volume":"37","author":"Zhang","year":"2025","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"120401","DOI":"10.1016\/j.eswa.2023.120401","article-title":"Efficient, economical and energy-saving multi-workflow scheduling in hybrid Cloud","volume":"228","author":"Sun","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_80","unstructured":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics, SAGE Publications. [4th ed.]."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Iantovics, L.B., Dehmer, M., and Emmert-Streib, F. (2018). MetrIntSimil\u2014An accurate and robust metric for comparison of similarity in intelligence of any number of cooperative multiagent systems. Symmetry, 10.","DOI":"10.3390\/sym10020048"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"21575","DOI":"10.1109\/ACCESS.2023.3248800","article-title":"A Generalized Multiobjective Reliability Redundancy Allocation With Uncertainties","volume":"11","author":"Ashraf","year":"2023","journal-title":"IEEE Access"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/2\/99\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:30:28Z","timestamp":1760027428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/2\/99"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":82,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["a18020099"],"URL":"https:\/\/doi.org\/10.3390\/a18020099","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,2,10]]}}}