{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T21:01:01Z","timestamp":1781989261811,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cloud computing is an emerging paradigm that offers flexible and seamless services for users based on their needs, including user budget savings. However, the involvement of a vast number of cloud users has made the scheduling of users\u2019 tasks (i.e., cloudlets) a challenging issue in selecting suitable data centres, servers (hosts), and virtual machines (VMs). Cloudlet scheduling is an NP-complete problem that can be solved using various meta-heuristic algorithms, which are quite popular due to their effectiveness. Massive user tasks and rapid growth in cloud resources have become increasingly complex challenges; therefore, an efficient algorithm is necessary for allocating cloudlets efficiently to attain better execution times, resource utilisation, and waiting times. This paper proposes a cloudlet scheduling, locust inspired algorithm to reduce the average makespan and waiting time and to boost VM and server utilisation. The CloudSim toolkit was used to evaluate our algorithm\u2019s efficiency, and the obtained results revealed that our algorithm outperforms other state-of-the-art nature-inspired algorithms, improving the average makespan, waiting time, and resource utilisation.<\/jats:p>","DOI":"10.3390\/s21217308","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T21:57:49Z","timestamp":1635976669000},"page":"7308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Locust Inspired Algorithm for Cloudlet Scheduling in Cloud Computing Environments"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-7037","authenticated-orcid":false,"given":"Mohammed Alaa","family":"Ala\u2019anzy","sequence":"first","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5124-5759","authenticated-orcid":false,"given":"Mohamed","family":"Othman","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia, Serdang 43400, Malaysia"},{"name":"Laboratory of Computational Science and Mathematical Physics, Institute for Mathematical Research (INSPEM), Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8079-1791","authenticated-orcid":false,"given":"Zurina Mohd","family":"Hanapi","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2385-3287","authenticated-orcid":false,"given":"Mohamed A.","family":"Alrshah","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Networks, Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24309","DOI":"10.1109\/ACCESS.2020.2970475","article-title":"Scheduling scientific workflow using multi-objective algorithm with fuzzy resource utilization in multi-cloud environment","volume":"8","author":"Farid","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6301","DOI":"10.1007\/s11277-017-4839-2","article-title":"Hybrid task scheduling method for cloud computing by genetic and DE algorithms","volume":"97","author":"Kamalinia","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/19393555.2020.1716116","article-title":"Profit maximization based task scheduling in hybrid clouds using whale optimization technique","volume":"29","author":"Ms","year":"2020","journal-title":"Inf. Secur. J. Glob. Perspect."},{"key":"ref_4","first-page":"1","article-title":"Task scheduling in cloud environment: A multi-objective ABC framework","volume":"38","author":"Jena","year":"2017","journal-title":"J. Inf. Optim. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TSC.2017.2679738","article-title":"A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment","volume":"13","author":"Domanal","year":"2017","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, S., Wang, C., Ren, K., and Lou, W. (2010, January 14\u201319). Achieving secure, scalable, and fine-grained data access control in cloud computing. Proceedings of the IEEE INFOCOM, San Diego, CA, USA.","DOI":"10.1109\/INFCOM.2010.5462174"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TNET.2002.803918","article-title":"Resource management with hoses: Point-to-cloud services for virtual private networks","volume":"10","author":"Duffield","year":"2002","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_8","first-page":"012018","article-title":"Range wise busy checking 2-way imbalanced algorithm for cloudlet allocation in cloud environment","volume":"1018","author":"Alanzy","year":"2018","journal-title":"JPhCS"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1109\/TPDS.2012.283","article-title":"Dynamic resource allocation using virtual machines for cloud computing environment","volume":"24","author":"Xiao","year":"2012","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TCC.2014.2328582","article-title":"CIVSched: A communication-aware inter-VM scheduling technique for decreased network latency between co-located VMs","volume":"2","author":"Guan","year":"2014","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/TCC.2014.2306427","article-title":"Dynamic heterogeneity-aware resource provisioning in the cloud","volume":"2","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"141868","DOI":"10.1109\/ACCESS.2019.2944420","article-title":"Load balancing and server consolidation in cloud computing environments: A meta-study","volume":"7","author":"Othman","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ala\u2019anzy, M.A., Othman, M., Hasan, S., Ghaleb, S.M., and Latip, R. (2020, January 14\u201315). Optimising Cloud Servers Utilisation Based on Locust-Inspired Algorithm. Proceedings of the 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), Stockholm, Sweden.","DOI":"10.1109\/ISCMI51676.2020.9311584"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.comnet.2018.07.001","article-title":"Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system","volume":"144","author":"Kumar","year":"2018","journal-title":"Comput. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2014). Nature-Inspired Optimization Algorithms, Elsevier, Mara Conner.","DOI":"10.1016\/B978-0-12-416743-8.00010-5"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35435","DOI":"10.1109\/ACCESS.2018.2839028","article-title":"LACE: A locust-inspired scheduling algorithm to reduce energy consumption in cloud datacenters","volume":"6","author":"Kurdi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.procs.2019.04.152","article-title":"A bio-inspired algorithm for virtual machines allocation in public clouds","volume":"151","author":"Alhassan","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ala\u2019anzy, M.A., and Othman, M. (2021). Mapping and Consolidation of VMs Using Locust-Inspired Algorithms for Green Cloud Computing. Neural Process. Lett., 1\u201317.","DOI":"10.1007\/s11063-021-10637-0"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s10732-012-9197-3","article-title":"Bee colony optimization for scheduling independent tasks to identical processors","volume":"18","author":"Ramljak","year":"2012","journal-title":"J. Heuristics"},{"key":"ref_20","first-page":"4128","article-title":"Load balancing of virtual machine using honey bee galvanizing algorithm in cloud","volume":"6","author":"Rathore","year":"2015","journal-title":"IJCSIT"},{"key":"ref_21","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."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.icte.2017.08.001","article-title":"A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments","volume":"4","author":"Dordaie","year":"2018","journal-title":"ICT Express"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1109\/ACCESS.2015.2508940","article-title":"A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing","volume":"3","author":"Zuo","year":"2015","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1080\/13658816.2011.635594","article-title":"A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas","volume":"26","author":"Liu","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s00521-014-1804-9","article-title":"A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing","volume":"26","author":"Cho","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, W., Ji, Z., Sun, J., Zhang, N., and Hu, Y. (2015, January 26\u201328). SAACO: A self adaptive ant colony optimization in cloud computing. Proceedings of the 2015 IEEE Fifth International Conference on Big Data and Cloud Computing, Dalian, China.","DOI":"10.1109\/BDCloud.2015.53"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s10586-019-02915-3","article-title":"Topsis\u2013pso inspired non-preemptive tasks scheduling algorithm in cloud environment","volume":"22","author":"Panwar","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_28","unstructured":"Chakravarthi, K.K., Shyamala, L., and Vaidehi, V. TOPSIS inspired cost-efficient concurrent workflow scheduling algorithm in cloud. J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1002\/spe.2819","article-title":"Self-adaptive brainstorming for jobshop scheduling in multicloud environment","volume":"50","author":"Bhatt","year":"2020","journal-title":"Softw. Pract. Exp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.swevo.2017.05.001","article-title":"Global-best brain storm optimization algorithm","volume":"37","year":"2017","journal-title":"Swarm Evol. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shi, Y. (2011). Brain storm optimization algorithm. International Conference in Swarm Intelligence, Springer.","DOI":"10.1007\/978-3-642-21515-5_36"},{"key":"ref_32","first-page":"891","article-title":"Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere","volume":"23","author":"Sanaj","year":"2020","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5901","DOI":"10.1007\/s00521-019-04067-2","article-title":"Amelioration of task scheduling in cloud computing using crow search algorithm","volume":"32","author":"Kumar","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s10586-014-0420-x","article-title":"FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method","volume":"18","author":"Shojafar","year":"2015","journal-title":"Clust. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.future.2015.08.006","article-title":"Symbiotic Organism Search optimization based task scheduling in cloud computing environment","volume":"56","author":"Abdullahi","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Changtian, Y., and Jiong, Y. (2012, January 20\u201323). Energy-aware genetic algorithms for task scheduling in cloud computing. Proceedings of the 2012 Seventh ChinaGrid Annual Conference, Beijing, China.","DOI":"10.1109\/ChinaGrid.2012.15"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dai, Y., Lou, Y., and Lu, X. (2015, January 26\u201327). A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. Proceedings of the 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2015.186"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.asoc.2006.10.012","article-title":"Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment","volume":"8","author":"Lee","year":"2008","journal-title":"Appl. Soft Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s10586-019-02951-z","article-title":"Priority-based task scheduling method over cloudlet using a swarm intelligence algorithm","volume":"23","author":"Milan","year":"2020","journal-title":"Clust. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10586-018-1823-x","article-title":"Chaotic social spider algorithm for load balance aware task scheduling in cloud computing","volume":"22","author":"Xavier","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1007\/s11277-020-07306-1","article-title":"A QoS Aware Resource Placement Approach Inspired on the Behavior of the Social Spider Mating Strategy in the Cloud Environment","volume":"113","author":"Abrol","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1111\/j.1461-0248.2012.01840.x","article-title":"Cannibalism can drive the evolution of behavioural phase polyphenism in locusts","volume":"15","author":"Guttal","year":"2012","journal-title":"Ecol. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ariel, G., and Ayali, A. (2015). Locust collective motion and its modeling. PLoS Comput. Biol., 11.","DOI":"10.1371\/journal.pcbi.1004522"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1140\/epjst\/e2008-00633-y","article-title":"A model for rolling swarms of locusts","volume":"157","author":"Topaz","year":"2008","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1002\/cpe.710","article-title":"Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing","volume":"14","author":"Buyya","year":"2002","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1002\/spe.2163","article-title":"Bandwidth-aware divisible task scheduling for cloud computing","volume":"44","author":"Lin","year":"2014","journal-title":"Softw. Pract. Exp."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1002\/spe.995","article-title":"CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms","volume":"41","author":"Calheiros","year":"2011","journal-title":"Softw. Pract. Exp."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"245","DOI":"10.14257\/ijgdc.2015.8.5.24","article-title":"Task scheduling using PSO algorithm in cloud computing environments","volume":"8","author":"Omara","year":"2015","journal-title":"Int. J. Grid Distrib. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.future.2020.08.036","article-title":"A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers","volume":"115","author":"Alboaneen","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"10769","DOI":"10.1007\/s10586-017-1174-z","article-title":"A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment","volume":"22","author":"Muthulakshmi","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tawfeek, M.A., El-Sisi, A., Keshk, A.E., and Torkey, F.A. (2013, January 26\u201328). Cloud task scheduling based on ant colony optimization. Proceedings of the 2013 8th International Conference on Computer Engineering & Systems (ICCES), Cairo, Egypt.","DOI":"10.1109\/ICCES.2013.6707172"},{"key":"ref_52","first-page":"1203","article-title":"Study on cloud computing task schedule strategy based on MACO algorithm","volume":"5","author":"Yonggui","year":"2011","journal-title":"Comput. Meas. Control."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7308\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:25:00Z","timestamp":1760167500000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":52,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217308"],"URL":"https:\/\/doi.org\/10.3390\/s21217308","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,3]]}}}