{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:25:55Z","timestamp":1772555155854,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,8]],"date-time":"2019-05-08T00:00:00Z","timestamp":1557273600000},"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>Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users\u2019 tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.<\/jats:p>","DOI":"10.3390\/fi11050109","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms"],"prefix":"10.3390","volume":"11","author":[{"given":"Amer","family":"Al-Rahayfeh","sequence":"first","affiliation":[{"name":"Department of Computer Science, Al-Hussein Bin Talal University, Ma\u2019an 71111, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9376-3511","authenticated-orcid":false,"given":"Saleh","family":"Atiewi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al-Hussein Bin Talal University, Ma\u2019an 71111, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1350-6719","authenticated-orcid":false,"given":"Abdullah","family":"Abuhussein","sequence":"additional","affiliation":[{"name":"Department of Information Systems, St. Cloud State University, St. Cloud, MN 56301, USA"}]},{"given":"Muder","family":"Almiani","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Al-Hussein Bin Talal University, Ma\u2019an 71111, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1109\/TCYB.2016.2574766","article-title":"TTSA: An effective scheduling approach for delay bounded tasks in hybrid clouds","volume":"47","author":"Yuan","year":"2017","journal-title":"IEEE Trans. Cybernet."},{"key":"ref_2","first-page":"129","article-title":"Multi-Rumen Anti-Grazing approach of load balancing in cloud network","volume":"9","author":"Sharma","year":"2017","journal-title":"Int. J. Inf. Tech."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Atiewi, S., Yussof, S., Ezanee, M., and Almiani, M. (2016, January 29). A review energy-efficient task scheduling algorithms in cloud computing. Proceedings of the 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, USA.","DOI":"10.1109\/LISAT.2016.7494108"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13677-017-0085-0","article-title":"A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique","volume":"6","author":"Elmougy","year":"2017","journal-title":"J. Cloud Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1504\/IJGUC.2018.095439","article-title":"A power saver scheduling algorithm using DVFS and DNS techniques in cloud computing data centres","volume":"9","author":"Atiewi","year":"2018","journal-title":"Int. J. Grid Util. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13673-017-0109-2","article-title":"A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments","volume":"7","author":"Moon","year":"2017","journal-title":"Hum.-Centric Comp. Inf. Sci."},{"key":"ref_7","first-page":"25","article-title":"Cloud task scheduling for load balancing based on intelligent strategy","volume":"6","author":"Keshk","year":"2014","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gawali, M.B., and Shinde, S.K. (2018). Task scheduling and resource allocation in cloud computing using a heuristic approach. J. Cloud Comput., 7.","DOI":"10.1186\/s13677-018-0105-8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1007\/s11277-016-3481-8","article-title":"A hybrid strategy for resource allocation and load balancing in virtualized data centers using BSO algorithms","volume":"94","author":"Jeyakrishnan","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jana, B., Chakraborty, M., and Mandal, T. (2019). A Task Scheduling Technique Based on Particle Swarm Optimization Algorithm in Cloud Environment. Soft Computing: Theories and Applications, Springer.","DOI":"10.1007\/978-981-13-0589-4_49"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.5121\/ijcnc.2018.10307","article-title":"Proposed Load Balancing Algorithm to Reduce Response Time and Processing Time on Cloud Computing","volume":"10","author":"Phi","year":"2018","journal-title":"Int. J. Comput. Netw. Commun."},{"key":"ref_12","unstructured":"Kherbache, V., Madelaine, E., and Hermenier, F. (2017). Scheduling live migration of virtual machines. IEEE Trans. Cloud Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mousavi, S., Mosavi, A., and Varkonyi-Koczy, A.R. (2017, January 25\u201328). A load balancing algorithm for resource allocation in cloud computing. Proceedings of the International Conference on Global Research and Education, Iasi, Romania.","DOI":"10.1007\/978-3-319-67459-9_36"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Velde, V., and Rama, B. (2017, January 15\u201316). An advanced algorithm for load balancing in cloud computing using fuzzy technique. Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2017.8250624"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/TCC.2016.2543722","article-title":"A dynamical and load-balanced flow scheduling approach for big data centers in clouds","volume":"6","author":"Tang","year":"2016","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_16","unstructured":"Yu, L., Chen, L., Cai, Z., Shen, H., Liang, Y., and Pan, Y. (2016). Stochastic load balancing for virtual resource management in datacenters. IEEE Trans. Cloud Comput."},{"key":"ref_17","unstructured":"Shen, H. (2017). RIAL: Resource intensity aware load balancing in clouds. IEEE Trans. Cloud Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/TASE.2017.2693688","article-title":"Dynamic cloud task scheduling based on a two-stage strategy","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Automation Science Eng."},{"key":"ref_19","unstructured":"Kumar, A.S., and Venkatesan, M. (2018). Task scheduling in a cloud computing environment using HGPSO algorithm. Cluster Comput., 1\u20137."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1007\/s11277-018-5816-0","article-title":"A hybrid approach for task scheduling using the cuckoo and harmony search in cloud computing environment","volume":"101","author":"Pradeep","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_21","first-page":"271","article-title":"OCSA: task scheduling algorithm in cloud computing environment","volume":"11","author":"Krishnadoss","year":"2018","journal-title":"Int. J. Intell. Engin. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1007\/s10586-018-2811-x","article-title":"A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment","volume":"21","author":"Alla","year":"2018","journal-title":"Cluster Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13090","DOI":"10.1109\/ACCESS.2017.2724598","article-title":"A DVFS based energy-efficient tasks scheduling in a data center","volume":"5","author":"Wang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_24","first-page":"1","article-title":"Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems","volume":"2018","author":"Seth","year":"2018","journal-title":"Int. J. Inf. Tech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1007\/s11036-017-0840-y","article-title":"Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia Storage System with QoS Provision","volume":"22","author":"Eswaran","year":"2017","journal-title":"Mobile Netw. Appl."},{"key":"ref_26","first-page":"1","article-title":"Dragonfly optimization and constraint measure-based load balancing in cloud computing","volume":"2017","author":"Polepally","year":"2017","journal-title":"Cluster Comput."},{"key":"ref_27","first-page":"2419","article-title":"Efficient Load Balancing Task Scheduling in Cloud Computing using Raven Roosting Optimization Algorithm","volume":"8","author":"Rani","year":"2017","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"31","DOI":"10.4018\/IJGHPC.2018010103","article-title":"An enhanced task scheduling in cloud computing based on deadline-aware model","volume":"10","author":"Alworafi","year":"2018","journal-title":"Int. Grid High Perform. Comput."},{"key":"ref_29","first-page":"1","article-title":"Modeling and Analyzing Dynamic Fault-Tolerant Strategy for Deadline Constrained Task Scheduling in Cloud Computing","volume":"2017","author":"Fan","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybernet. Syst."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/5\/109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:49:56Z","timestamp":1760186996000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/5\/109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,8]]},"references-count":29,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["fi11050109"],"URL":"https:\/\/doi.org\/10.3390\/fi11050109","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,8]]}}}