{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:21:58Z","timestamp":1763018518471,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction of virtual machine placement problems. At the same time, the multi-dimensional resources on the cloud should be used in a balanced manner in order to avoid resources waste. In this context, this paper addresses a real-world virtual machine placement problem arising in a Healthcare-Cloud (H-Cloud) of hospitals chain in Saudi Arabia, considering server power consumption and resource utilization. As a part of optimizing both objectives, user service quality has to be taken into account. In fact, user quality of service (QoS) is also considered by measuring the Service-Level Agreement (SLA) violation rate. This problem is modeled as a multi-objective virtual machine placement problem with the objective of minimizing power consumption, resource utilization, and SLA violation rate. To solve this challenging problem, a fuzzy grouping genetic algorithm (FGGA) is proposed. Considering that multiple optimization objectives may have different degrees of influence on the problem, the fitness function of the proposed algorithm is calculated with fuzzy logic-based function. The experimental results show the effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.3390\/a15040128","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T21:44:06Z","timestamp":1649972646000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1900-420X","authenticated-orcid":false,"given":"Nawaf","family":"Alharbe","sequence":"first","affiliation":[{"name":"Applied College, Taibah University, Medina 42353, Saudi Arabia"}]},{"given":"Abeer","family":"Aljohani","sequence":"additional","affiliation":[{"name":"Applied College, Taibah University, Medina 42353, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6627-9161","authenticated-orcid":false,"given":"Mohamed Ali","family":"Rakrouki","sequence":"additional","affiliation":[{"name":"Applied College, Taibah University, Medina 42353, Saudi Arabia"},{"name":"Ecole Sup\u00e9rieure des Sciences Economiques et Commerciales de Tunis, University of Tunis, Montfleury 1089, Tunisia"},{"name":"Business Analytics and DEcision Making Lab. (BADEM), Tunis Business School, University of Tunis, Bir El Kassaa 2059, Tunisia"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"ref_1","unstructured":"Whitney, J., and Delforge, P. (2014). Data Center Efficiency Assessment, Scaling Up Energy Efficiency Across the Data Center Industry: Evaluating Key Drivers and Barriers, Natural Resources Defense Council. Technical Report."},{"key":"ref_2","unstructured":"Shehabi, A., Smith, S., Sartor, D., Brown, R., Herrlin, M., Koomey, J., Masanet, E., Horner, N., Azevedo, I., and Lintner, W. (2014). United States Data Center Energy Usage Report, Berkeley Lab.. Technical Report."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, X.F., Zhan, Z.H., Du, K.J., and Chen, W.N. (2014, January 12\u201316). Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. Proceedings of the 2014 Genetic and Evolutionary Computation Conference, Vancouver, BC, Canada.","DOI":"10.1145\/2576768.2598265"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Le, K., Zhang, J., Meng, J., Bianchini, R., Jaluria, Y., and Nguyen, T.D. (2011, January 12\u201318). Reducing electricity cost through virtual machine placement in high performance computing clouds. Proceedings of the 2011 SC\u2014International Conference for High Performance Computing, Networking, Storage and Analysis, Seatle, WA, USA.","DOI":"10.1145\/2063384.2063413"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pires, F.L., and Bar\u00e1n, B. (2013, January 9\u201312). Multi-objective virtual machine placement with service level agreement: A memetic algorithm approach. Proceedings of the 2013 IEEE\/ACM 6th International Conference on Utility and Cloud Computing, UCC 2013, Dresden, Germany.","DOI":"10.1109\/UCC.2013.44"},{"key":"ref_6","first-page":"29","article-title":"Dynamic virtual machine integration algorithm based on load prediction","volume":"12","author":"Zhiqiang","year":"2015","journal-title":"J. Yangtze Univ. Nat. Ed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.future.2011.04.013","article-title":"Towards a green cluster through dynamic remapping of virtual machines","volume":"28","author":"Liao","year":"2012","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.jcss.2013.02.004","article-title":"A multi-objective ant colony system algorithm for virtual machine placement in cloud computing","volume":"79","author":"Gao","year":"2013","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s13677-016-0067-7","article-title":"Optimizing virtual machine placement for energy and SLA in clouds using utility functions","volume":"5","author":"Mosa","year":"2016","journal-title":"J. Cloud Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kumar, D., and Raza, Z. (2015, January 13\u201314). A PSO based VM resource scheduling model for cloud computing. Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Communication Technology, CICT 2015, Ghaziabad, India.","DOI":"10.1109\/CICT.2015.35"},{"key":"ref_11","first-page":"100374","article-title":"Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters","volume":"28","author":"Farzai","year":"2020","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11277","DOI":"10.1007\/s00500-018-03686-6","article-title":"Optimal VM placement in distributed cloud environment using MOEA\/D","volume":"23","author":"Gopu","year":"2019","journal-title":"Soft Comput."},{"key":"ref_13","unstructured":"Alhammadi, A.S.A., and Vasanthi, V. (2021, January 4\u20135). Multi-Objective Algorithms for Virtual Machine Selection and Placement in Cloud Data Center. Proceedings of the 2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021, Online."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pushpa, R., and Siddappa, M. (2022, January 12\u201313). Adaptive Hybrid Optimization Based Virtual Machine Placement in Cloud Computing. Proceedings of the 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Namakkal, India.","DOI":"10.1109\/ICSSIT53264.2022.9716298"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3448","DOI":"10.1007\/s11227-021-03953-8","article-title":"Optimal machine placement based on improved genetic algorithm in cloud computing","volume":"78","author":"Lu","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e834","DOI":"10.7717\/peerj-cs.834","article-title":"A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization","volume":"8","author":"Mejahed","year":"2022","journal-title":"PeerJ. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1742-6596\/2195\/1\/012012","article-title":"Research on Multi-Objective Optimization Method of Edge Cloud Computing Virtual Machine Placement","volume":"2195","author":"Li","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_18","first-page":"1","article-title":"The role of an ant colony optimisation algorithm in solving the major issues of the cloud computing","volume":"2021","author":"Asghari","year":"2021","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Garey, M.R., and Johnson, D.S. (1981). Approximation Algorithms for Bin Packing Problems: A Survey, Springer.","DOI":"10.1007\/978-3-7091-2748-3_8"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MC.2007.443","article-title":"The case for energy-proportional computing","volume":"40","author":"Barroso","year":"2007","journal-title":"Computer"},{"key":"ref_21","first-page":"62","article-title":"Energy-performance optimisation for the dynamic consolidation of virtual machines in cloud computing","volume":"9","author":"Li","year":"2018","journal-title":"Int. J. Serv. Oper. Inform."},{"key":"ref_22","unstructured":"Mutingi, M., Mbohwa, C., and Musiyarira, H. (2017, January 25\u201327). Grouping genetic algorithms: An exploratory study. Proceedings of the World Congress on Engineering and Computer Science (WCECS 2017), San Francisco, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100796","DOI":"10.1016\/j.swevo.2020.100796","article-title":"Variation Operators for Grouping Genetic Algorithms: A Review","volume":"60","author":"Kharel","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_24","unstructured":"Klir, G.J., and Yuan, B. (1996). Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh, World Scientific Publishing Co., Inc."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Buyya, R., Ranjan, R., and Calheiros, R.N. (2009, January 21\u201324). Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities. Proceedings of the 2009 International Conference on High Performance Computing and Simulation, HPCS 2009, Leipzig, Germany.","DOI":"10.1109\/HPCSIM.2009.5192685"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1613\/jair.2861","article-title":"ParamILS: An automatic algorithm configuration framework","volume":"36","author":"Hutter","year":"2009","journal-title":"J. Artif. Intell. Res."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/4\/128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:53:57Z","timestamp":1760136837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/4\/128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,14]]},"references-count":26,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["a15040128"],"URL":"https:\/\/doi.org\/10.3390\/a15040128","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2022,4,14]]}}}