{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T13:58:42Z","timestamp":1720101522841},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Minufiya University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,6]]},"abstract":"Abstract<\/jats:title>Cloud Computing is playing a huge role in future technology. Further, with the explosive growth of the Internet and cloud computing, several service providers, such as Amazon, Microsoft, IBM, and Google, have expanded their data centers and rapidly deployed data centers in different places around the world to deliver various cloud computing services. However, several challenges are raised with the wide spread use of cloud environment such as power consumption, load balance, reliability, scalability, and security. This paper tackles the power consumption problem and presents an efficient algorithm, called Task Consolidation based Power Minimization (TCPM), to efficiently schedule tasks onto available resources of the cloud environment so as to minimize power consumption. In proposed TCPM algorithm, several benefits of the existing algorithms are enhanced and incorporated into the TCPM algorithm, where the best-fit procedure is used to achieve the best possible resource utilization and avoid wasting energy. The results of the proposed TCPM algorithm are compared with other recent algorithms such as FCFS, WWO, and MCT algorithms using the CloudSim toolkit.<\/jats:p>","DOI":"10.1007\/s11042-022-14009-1","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T05:07:04Z","timestamp":1666760824000},"page":"21385-21413","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Task consolidation based power consumption minimization in cloud computing environment"],"prefix":"10.1007","volume":"82","author":[{"given":"Shaimaa","family":"Badr","sequence":"first","affiliation":[]},{"given":"Ahmed","family":"El Mahalawy","sequence":"additional","affiliation":[]},{"given":"Gamal","family":"Attiya","sequence":"additional","affiliation":[]},{"given":"Aida A.","family":"Nasr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"14009_CR1","doi-asserted-by":"crossref","unstructured":"Afaf Abdelkader Abdelhafiz (2018) \u201cTuples: A New Scheduling Algorithm\u201d, J Comput 13(11):1309\u20131315","DOI":"10.17706\/jcp.13.11.1309-1315"},{"key":"14009_CR2","unstructured":"Aishwarya, Anusha K, Gagana, Megha (n.d.) Survey on Energy Consumption in Cloud Computing. Int J Eng Res Technol 9(4): 2278\u20130181"},{"key":"14009_CR3","doi-asserted-by":"crossref","unstructured":"Amer DA, Attiya G, Ziedan I, Nasr AA (May 2021) A new task scheduling algorithm based on water wave optimization for cloud computing. Int J Comput\u00a0 (0975\u20138887) 183(3):65\u201375","DOI":"10.5120\/ijca2021921320"},{"key":"14009_CR4","doi-asserted-by":"publisher","first-page":"2793","DOI":"10.1007\/s11227-021-03977-0","volume":"78","author":"DA Amer","year":"2021","unstructured":"Amer DA, Attiya G, Zeidan I, Nasr AA (2021) Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing. J Supercomput 78:2793\u20132818","journal-title":"J Supercomput"},{"key":"14009_CR5","unstructured":"Arulkumar V, Bhalaji N (n.d.) Load balancing in cloud computing using water wave algorithm. Article in Concurrency and Computation Practice and Experience, September 2019, \u00a9 2019 John Wiley & Sons, Ltd."},{"key":"14009_CR6","doi-asserted-by":"crossref","unstructured":"Badr S, El Mahalawy A, Attiya G, Nasr AA (n.d.) A Review on Task Consolidation for Cloud Computing Environment. ICEEM2021, \u00a92021 IEEE","DOI":"10.1109\/ICEEM52022.2021.9480385"},{"issue":"1","key":"14009_CR7","first-page":"200","volume":"3","author":"A Bharathi","year":"2014","unstructured":"Bharathi A, Mohana RS, Ushapriya A (January 2014) Reducing energy consumption and increasing profit with task consolidation in clouds. Int J Eng Sci Innov Technol (IJESIT) 3(1):200\u2013207","journal-title":"Int J Eng Sci Innov Technol (IJESIT)"},{"key":"14009_CR8","unstructured":"Elzeki OM, Rashad MZ, Elsoud MA (July 2012) Overview of scheduling tasks in distributed computing systems. Int J Soft Comput Eng (IJSCE) ISSN: 2231\u20132307 2(3)"},{"key":"14009_CR9","doi-asserted-by":"crossref","unstructured":"Hsu C, Chen S, Lee C, Chang H, Lai K, Li K, Rong C, Optimizing Energy Consumption with Task Consolidation in Clouds. Inf Sci\u00a0 258:452-462","DOI":"10.1016\/j.ins.2012.10.041"},{"key":"14009_CR10","doi-asserted-by":"crossref","unstructured":"Hussain M, Wei L-F, Lakhan A, Wali S, Ali S, Hussain A (2021), Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing. Sustain Comput-Infor 30:100517","DOI":"10.1016\/j.suscom.2021.100517"},{"key":"14009_CR11","doi-asserted-by":"crossref","unstructured":"Kaur A, Rupinderkaur, Jain P (August 2013) Algorithms for Task Consolidation Problem in a Cloud Computing Environment. Int J Comput Appl (0975\u20138887) 75(4):17\u201322","DOI":"10.5120\/13099-0397"},{"key":"14009_CR12","unstructured":"Khurma RA, Al Harahsheh H, Sharieh A (September 2018) Task scheduling algorithm in cloud computing based on modified round robin algorithm. J Theor Appl Inf Technol 96(17)"},{"key":"14009_CR13","doi-asserted-by":"crossref","unstructured":"Koot M, Wijnhoven F (2021), Usage impact on data center electricity needs: a system dynamic forecasting model. Appl Energy 291:116798","DOI":"10.1016\/j.apenergy.2021.116798"},{"key":"14009_CR14","doi-asserted-by":"crossref","unstructured":"Lee, Zomaya A (2012) Energy-efficient utilization of resources in cloud computing systems. J Supercomput 60:268\u2013280","DOI":"10.1007\/s11227-010-0421-3"},{"key":"14009_CR15","doi-asserted-by":"publisher","unstructured":"Madni SHH, Latiff MSA, Abdullahi M, Abdulhamid S\u2019i M, Usman MJ (2017) Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS One 12(5): e0176321. https:\/\/doi.org\/10.1371\/journal.pone.0176321","DOI":"10.1371\/journal.pone.0176321"},{"key":"14009_CR16","doi-asserted-by":"crossref","unstructured":"Medara R, Singh RS, Selva Kumar U, Barfa S (n.d.) Energy Efficient Virtual Machine ConsolidationUsing Water Wave Optimization. \u00a92020 IEEE","DOI":"10.1109\/CEC48606.2020.9185865"},{"key":"14009_CR17","doi-asserted-by":"crossref","unstructured":"Mehdi NA, Mamat A, Amer A, Abdul-Mehdi ZT (December 2011) Minimum Completion Time for Power-Aware Scheduling in Cloud Computing. Article","DOI":"10.1109\/DeSE.2011.30"},{"key":"14009_CR18","doi-asserted-by":"crossref","unstructured":"Mekala MS, Viswanathan P (July 2021) CTRV: resource based task consolidation approach in cloud for green computing. Distributed and Parallel Databases, Springer","DOI":"10.1007\/s10619-021-07348-9"},{"key":"14009_CR19","doi-asserted-by":"publisher","first-page":"178825","DOI":"10.1109\/ACCESS.2020.3026875","volume":"8","author":"SK Mishra","year":"2020","unstructured":"Mishra SK et al (2020) Energy-aware task allocation for multi-cloud networks. IEEE Access 8:178825\u2013178834","journal-title":"IEEE Access"},{"key":"14009_CR20","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s10586-018-2858-8","volume":"22","author":"SK Panda","year":"2019","unstructured":"Panda SK, Jana PK (2019) An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Clust Comput 22:509\u2013527","journal-title":"Clust Comput"},{"key":"14009_CR21","doi-asserted-by":"crossref","unstructured":"Panda SK, Jana PK (n.d.) An Efficient Energy Saving Task Consolidation Algorithm for Cloud Computing Systems. 2014 International Conference on Parallel, Distributed and Grid Computing, pp. 262\u2013267","DOI":"10.1109\/PDGC.2014.7030753"},{"key":"14009_CR22","unstructured":"Panigrahi P, Panda SK, Tripathy CR (October 2015) Energy efficient task consolidation algorithms for cloud computing systems. J Inf Process 94:34\u201345"},{"key":"14009_CR23","doi-asserted-by":"crossref","unstructured":"Reda NM, Tawfik A, Marzok MA, Khamis SM (2015) Sort-Mid tasks scheduling algorithm in grid computing\u201d, Cairo University. J Adv Res","DOI":"10.1016\/j.jare.2014.11.010"},{"key":"14009_CR24","unstructured":"Singh P, Sengupta J, Suri PK (2020) CPU and memory requirement based task consolidation for reducing energy consumption in cloud computing. J Crit Rev 7(09)"},{"key":"14009_CR25","unstructured":"Singhn P, Jain EA (April 2014) Survey Paper on Cloud Computing. Int J Eng Technol Innov 3(4):2319"},{"issue":"4","key":"14009_CR26","first-page":"31","volume":"9","author":"M Siva","year":"2016","unstructured":"Siva M, Balamurugan R, Lakshminarasimman L (2016) Water Wave Optimization Algorithm for Solving Economic Dispatch Problems with Generator Constraints. Int J Intell Eng Syst 9(4):31\u201340","journal-title":"Int J Intell Eng Syst"},{"key":"14009_CR27","unstructured":"Carolan J, Gaede S (2009) Introduction to Cloud Computing Architecture, Sun Microsystems Inc. White Paper. Sun Microsystems Inc., Santa Clara, 2009."},{"issue":"4","key":"14009_CR28","first-page":"617","volume":"7","author":"S Taherian Dehkordi","year":"2019","unstructured":"Taherian Dehkordi S, Khatibi Bardsiri A, Zahedi MH (2019) Prediction and diagnosis of diabetes mellitus using a water wave optimization algorithm. J AI Data Mining 7(4):617\u2013630","journal-title":"J AI Data Mining"},{"key":"14009_CR29","doi-asserted-by":"crossref","unstructured":"Wu X, Zhou Y, Lu Y (n.d.) Elite Opposition-Based Water Wave Optimization Algorithm for Global Optimization. Research Article, Hindawi, Mathematical Problems in Engineering, Volume 2017, Article ID 3498363, 25 pages","DOI":"10.1155\/2017\/3498363"},{"key":"14009_CR30","doi-asserted-by":"publisher","first-page":"32415","DOI":"10.1007\/s11042-020-09664-1","volume":"79","author":"Z Yan","year":"2020","unstructured":"Yan Z, Zhang J, Tang J (2020) Modified water wave optimization algorithm for underwater multilevel thresholding image segmentation\u201d, Springer. Multimed Tools Appl 79:32415\u201332448","journal-title":"Multimed Tools Appl"},{"key":"14009_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cor.2014.10.008","volume":"55","author":"Y-J Zheng","year":"2015","unstructured":"Zheng Y-J (March 2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1\u201311","journal-title":"Comput Oper Res"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14009-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-14009-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14009-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T09:15:40Z","timestamp":1685006140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-14009-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":31,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["14009"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-14009-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,26]]},"assertion":[{"value":"30 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The Dataset are generated randomly to cloudsim simulation. The length of the tasks is from 1000 to 10,000 Million instructions. The data center Specifications are given in Table InternalRef removed and the host Specifications are given in Table InternalRef removed while the VMs Specifications are given in Table InternalRef removed.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"The DA statement"}}]}}