{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:02:55Z","timestamp":1760241775583,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,6]],"date-time":"2018-09-06T00:00:00Z","timestamp":1536192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.<\/jats:p>","DOI":"10.3390\/fi10090086","type":"journal-article","created":{"date-parts":[[2018,9,6]],"date-time":"2018-09-06T10:38:38Z","timestamp":1536230318000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-7890","authenticated-orcid":false,"given":"Samah","family":"Alshathri","sequence":"first","affiliation":[{"name":"College of Information Technology, Princess Nourah Bint Abdulrahman University, Riyadh 11671 P.O.Box: 84428, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1788-547X","authenticated-orcid":false,"given":"Bogdan","family":"Ghita","sequence":"additional","affiliation":[{"name":"School of Computing, Electronics and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathan","family":"Clarke","sequence":"additional","affiliation":[{"name":"School of Computing, Electronics and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.jnca.2016.09.002","article-title":"A survey on cloud computing security: Issues, threats, and solutions","volume":"75","author":"Singh","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_2","first-page":"103","article-title":"An Introduction to the Special Issue on Advanced Control of Energy Systems","volume":"8","author":"Azzouzi","year":"2013","journal-title":"WSEAS Trans. Power Syst."},{"doi-asserted-by":"crossref","unstructured":"Singh, S., Sharma, P.K., and Moon, S.Y. (2017). EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure. Sustainability, 9.","key":"ref_3","DOI":"10.3390\/su9040673"},{"key":"ref_4","first-page":"1","article-title":"Stochastic Modeling and Analysis with Energy Optimization for Wireless Sensor Networks","volume":"5","author":"Xu","year":"2014","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3844\/jcssp.2016.448.454","article-title":"Optimized Energy Efficient Virtual Machine Placement Algorithm and Techniques for Cloud Data Centers","volume":"12","author":"Patel","year":"2016","journal-title":"J. Comput. Sci."},{"key":"ref_6","first-page":"1126","article-title":"Energy-Efficient Data Center Concepts under the EXPO-2017 Astana","volume":"2","author":"Baiboz","year":"2015","journal-title":"J. Multidiscip. Eng. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.procs.2015.09.064","article-title":"Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments","volume":"65","author":"Awad","year":"2015","journal-title":"Procedia Comput. Sci."},{"doi-asserted-by":"crossref","unstructured":"Mahmood, A., Khan, S.A., and Bahlool, R.A. (2017). Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm. Computers, 6.","key":"ref_8","DOI":"10.3390\/computers6020015"},{"unstructured":"Ma, T., Tang, M., Shen, W., and Jin, Y. (2017). Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing. Information, 5.","key":"ref_9"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.14445\/22312803\/IJCTT-V29P113","article-title":"Study of Scheduling Techniques in Cloud Computing Environment","volume":"29","author":"Yadav","year":"2015","journal-title":"Int. J. Comput. Trends Technol."},{"key":"ref_11","first-page":"462","article-title":"Study of Task Scheduling Algorithms in the Cloud Computing Environment: A Review","volume":"8","author":"Yadav","year":"2017","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10723-015-9334-y","article-title":"An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment","volume":"14","author":"Tang","year":"2015","journal-title":"J. Grid Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.future.2013.06.009","article-title":"A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters","volume":"37","author":"Wu","year":"2014","journal-title":"Future Gener. Comput. Syst."},{"doi-asserted-by":"crossref","unstructured":"Ali, S.A., and Islamia, J.M. (2016, January 14\u201317). A Relative Study of Task Scheduling Algorithms in Cloud Computing Environment. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India.","key":"ref_14","DOI":"10.1109\/IC3I.2016.7917943"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jnca.2015.05.001","article-title":"A new energy-aware task scheduling method for data-intensive applications in the cloud","volume":"59","author":"Zhao","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jpdc.2015.08.004","article-title":"Energy-efficient task scheduling for multi-core platforms with per-core DVFS","volume":"86","author":"Lin","year":"2015","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.future.2015.02.001","article-title":"Energy efficient scheduling of virtual machines in cloud with deadline constraint","volume":"50","author":"Ding","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"doi-asserted-by":"crossref","unstructured":"Pavithra, B., and Ranjana, R. (2016, January 25\u201326). Energy efficient resource provisioning with dynamic VM placement using energy aware load balancer in cloud. Proceedings of the 2016 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, India.","key":"ref_18","DOI":"10.1109\/ICICES.2016.7518919"},{"doi-asserted-by":"crossref","unstructured":"AlIsmail, S.M., and Kurdi, H.A. (2016, January 13\u201315). Green algorithm to reduce the energy consumption in cloud computing data centers. Proceedings of the 2016 SAI Computing Conference (SAI), London, UK.","key":"ref_19","DOI":"10.1109\/SAI.2016.7556035"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13677-015-0031-y","article-title":"Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers","volume":"4","author":"Ziqian","year":"2015","journal-title":"J. Cloud Comput. Adv. Syst. Appl."},{"unstructured":"(2018, July 25). Intel\u2019s Cloud Computing 2015 Vision. Available online: http:\/\/www.intel.com\/content\/www\/us\/en\/cloud-computing\/cloudcomputing-intel-cloud-2015-vision.html.","key":"ref_21"},{"unstructured":"Ismaila, L., and Fardoun, A. (2016, January 23\u201326). EATS: Energy-Aware Tasks Scheduling in Cloud Computing Systems. Proceedings of the 6th International Conference on Sustainable Energy Information Technology (SEIT 2016), Madrid, Spain.","key":"ref_22"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.simpat.2015.06.002","article-title":"Open-Source Simulators for Cloud Computing: Comparative Study and Challenging Issues","volume":"1","author":"Tian","year":"2015","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"337","DOI":"10.14257\/ijgdc.2015.8.5.33","article-title":"Virtual Machine Migration in Cloud Computing","volume":"8","author":"Kaur","year":"2015","journal-title":"Int. J. Grid Distrib. Comput."},{"unstructured":"Alshathri, S. (2016, January 19\u201321). Towards an Energy Optimization Framework for Cloud Computing Data Centers. Proceedings of the Eleventh International Network Conference (INC 2016), Frankfurt am Main, Germany.","key":"ref_25"},{"unstructured":"Beloglazov, A. (February 2013). Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing. [Ph.D. Thesis, The University of Melbourne].","key":"ref_26"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1002\/cpe.1867","article-title":"Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers","volume":"24","author":"Beloglazov","year":"2012","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/1687-1499-2014-64","article-title":"A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing","volume":"2014","author":"Shu","year":"2014","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"doi-asserted-by":"crossref","unstructured":"Han, G., Que, W., Jia, G., and Shu, L. (2016). An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing. Sensors, 16.","key":"ref_29","DOI":"10.3390\/s16020246"},{"unstructured":"(2018, July 25). Github. Available online: https:\/\/github.com\/Cloudslab\/cloudsim\/releases\/tag\/cloudsim-3.0.3.","key":"ref_30"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/10\/9\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:19:11Z","timestamp":1760195951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/10\/9\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,6]]},"references-count":30,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["fi10090086"],"URL":"https:\/\/doi.org\/10.3390\/fi10090086","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2018,9,6]]}}}