{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:22:44Z","timestamp":1743070964306,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819980819"},{"type":"electronic","value":"9789819980826"}],"license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8082-6_13","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T16:08:09Z","timestamp":1699978089000},"page":"166-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Task Scheduling with\u00a0Multi-strategy Improved Sparrow Search Algorithm in\u00a0Cloud Datacenters"],"prefix":"10.1007","author":[{"given":"Yao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyue","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jss.2016.07.006","volume":"124","author":"B Keshanchi","year":"2017","unstructured":"Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1\u201321 (2017)","journal-title":"J. Syst. Softw."},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Gupta, A., Garg, R.: Load balancing based task scheduling with ACO in cloud computing. In: 2017 International Conference on Computer and Applications (ICCA), pp. 174\u2013179. IEEE (2017)","DOI":"10.1109\/COMAPP.2017.8079781"},{"key":"13_CR3","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s11277-019-06566-w","volume":"109","author":"T Prem Jacob","year":"2019","unstructured":"Prem Jacob, T., Pradeep, K.: A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization. Wirel. Pers. Commun. 109, 315\u2013331 (2019)","journal-title":"Wirel. Pers. Commun."},{"issue":"1","key":"13_CR4","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","volume":"8","author":"J Xue","year":"2020","unstructured":"Xue, J., Shen, B.: A novel swarm intelligence optimization approach: sparrow search algorithm. Syst. Sci. Control Eng. 8(1), 22\u201334 (2020)","journal-title":"Syst. Sci. Control Eng."},{"issue":"3","key":"13_CR5","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1007\/s11227-021-03989-w","volume":"78","author":"OY Abdulhammed","year":"2022","unstructured":"Abdulhammed, O.Y.: Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm. J. Supercomput. 78(3), 3266\u20133287 (2022)","journal-title":"J. Supercomput."},{"issue":"14","key":"13_CR6","doi-asserted-by":"publisher","first-page":"5425","DOI":"10.3390\/s22145425","volume":"22","author":"S Qiu","year":"2022","unstructured":"Qiu, S., Li, A.: Application of chaos mutation adaptive sparrow search algorithm in edge data compression. Sensors 22(14), 5425 (2022)","journal-title":"Sensors"},{"key":"13_CR7","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.future.2018.09.014","volume":"91","author":"AR Arunarani","year":"2019","unstructured":"Arunarani, A.R., Manjula, D., Sugumaran, V.: Task scheduling techniques in cloud computing: a literature survey. Futur. Gener. Comput. Syst. 91, 407\u2013415 (2019)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3103\/S0146411619010024","volume":"53","author":"RM Alguliyev","year":"2019","unstructured":"Alguliyev, R.M., Imamverdiyev, Y.N., Abdullayeva, F.J.: PSO-based load balancing method in cloud computing. Autom. Control. Comput. Sci. 53, 45\u201355 (2019)","journal-title":"Autom. Control. Comput. Sci."},{"issue":"3","key":"13_CR9","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/TEVC.2008.2009032","volume":"13","author":"YG Woldesenbet","year":"2009","unstructured":"Woldesenbet, Y.G., Yen, G.G., Tessema, B.G.: Constraint handling in multiobjective evolutionary optimization. IEEE Trans. Evol. Comput. 13(3), 514\u2013525 (2009)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"13_CR10","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s40436-021-00366-x","volume":"10","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., He, R., Yang, K.: A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm. Adv. Manuf. 10(1), 114\u2013130 (2022)","journal-title":"Adv. Manuf."},{"key":"13_CR11","doi-asserted-by":"publisher","first-page":"69307","DOI":"10.1109\/ACCESS.2021.3075547","volume":"9","author":"W Tuerxun","year":"2021","unstructured":"Tuerxun, W., Chang, X., Hongyu, G., Zhijie, J., Huajian, Z.: Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm. IEEE Access 9, 69307\u201369315 (2021)","journal-title":"IEEE Access"},{"issue":"4","key":"13_CR12","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1002\/ima.22559","volume":"31","author":"T Liu","year":"2021","unstructured":"Liu, T., Yuan, Z., Wu, L., Badami, B.: Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm. Int. J. Imaging Syst. Technol. 31(4), 1921\u20131935 (2021)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1007\/s11071-018-4251-9","volume":"93","author":"Y Luo","year":"2018","unstructured":"Luo, Y., Zhou, R., Liu, J., Cao, Y., Ding, X.: A parallel image encryption algorithm based on the piecewise linear chaotic map and hyper-chaotic map. Nonlinear Dyn. 93, 1165\u20131181 (2018)","journal-title":"Nonlinear Dyn."},{"key":"13_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113377","volume":"152","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi, A., Heidarinejad, M., Mirjalili, S., Gandomi, A.H.: Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst. Appl. 152, 113377 (2020)","journal-title":"Expert Syst. Appl."},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Yan, S., Yang, P., Zhu, D., Zheng, W., Wu, F.: Improved sparrow search algorithm based on iterative local search. Comput. Intell. Neurosci. 2021 (2021)","DOI":"10.1155\/2021\/6860503"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Wang, Z., Huang, X., Zhu, D.: A multistrategy-integrated learning sparrow search algorithm and optimization of engineering problems. Comput. Intell. Neurosci. 2022 (2022)","DOI":"10.1155\/2022\/2475460"},{"issue":"1","key":"13_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1002\/spe.995","volume":"41","author":"RN Calheiros","year":"2011","unstructured":"Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23\u201350 (2011)","journal-title":"Softw. Pract. Exp."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8082-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:06:58Z","timestamp":1710349618000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8082-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"ISBN":["9789819980819","9789819980826"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8082-6_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"15 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}