{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T02:21:23Z","timestamp":1777515683079,"version":"3.51.4"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09666-3","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T15:56:21Z","timestamp":1752767781000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An efficient model to estimate and optimise the cloud migration costs from on-premises web apps"],"prefix":"10.1007","volume":"28","author":[{"given":"Vijay","family":"Prakash","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajay","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohd","family":"Shahid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lalit","family":"Garg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seema","family":"Bawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"9666_CR1","doi-asserted-by":"crossref","unstructured":"Andrade VT, Castro RM, Carneiro W, Bayerlein L, de Oliveira E, Oliveira IS, Oliveira PA. Migration from on-premises to cloud: challenges and opportunities. In: International Conference on Applied Informatics. 2024. pp. 167\u201382.","DOI":"10.1007\/978-3-031-75144-8_12"},{"key":"9666_CR2","unstructured":"Gade KR. Cost optimization in the cloud: a practical guide to ELT integration and data migration strategies. J Comput Innov. 2024;4(1)."},{"key":"9666_CR3","doi-asserted-by":"publisher","unstructured":"Darwish D. Emerging trends in cloud computing analytics, scalability, and service models. 2024. https:\/\/doi.org\/10.4018\/979-8-3693-0900-1.","DOI":"10.4018\/979-8-3693-0900-1"},{"issue":"4","key":"9666_CR4","doi-asserted-by":"publisher","first-page":"2409","DOI":"10.1109\/TCC.2021.3051766","volume":"10","author":"MW Asres","year":"2022","unstructured":"Asres MW, Ardito L, Patti E. Computational cost analysis and Data-Driven predictive modeling of Cloud-Based Online-NILM algorithm. IEEE Trans Cloud Comput. 2022;10(4):2409\u201323. https:\/\/doi.org\/10.1109\/TCC.2021.3051766.","journal-title":"IEEE Trans Cloud Comput"},{"issue":"4","key":"9666_CR5","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1007\/s10586-020-03088-0","volume":"23","author":"A Kumar","year":"2020","unstructured":"Kumar A, Bawa S. DAIS: dynamic access and integration services framework for cloud-oriented storage systems. Cluster Comput. 2020;23(4):3289\u2013308. https:\/\/doi.org\/10.1007\/s10586-020-03088-0.","journal-title":"Cluster Comput"},{"issue":"3","key":"9666_CR6","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1007\/s10586-021-03280-w","volume":"24","author":"G Sharma","year":"2021","unstructured":"Sharma G, Miglani N, Kumar A. PLB: a resilient and adaptive task scheduling scheme based on multi-queues for cloud environment. Cluster Comput. 2021;24(3):2615\u201337. https:\/\/doi.org\/10.1007\/s10586-021-03280-w.","journal-title":"Cluster Comput"},{"key":"9666_CR7","doi-asserted-by":"publisher","first-page":"3362","DOI":"10.1016\/j.procs.2023.10.330","volume":"225","author":"F Olariu","year":"2023","unstructured":"Olariu F, Alboaie L. Challenges in optimizing migration costs from On-Premises to Microsoft Azure. Procedia Comput Sci. 2023;225:3362\u201371. https:\/\/doi.org\/10.1016\/j.procs.2023.10.330.","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"9666_CR8","first-page":"15","volume":"15","author":"BP Bhandari","year":"2025","unstructured":"Bhandari BP, Adhikari S, P., Sharma. Cost-Benefit analysis of cloud migration: evaluating the financial impact of moving from On-Premises to cloud infrastructure. Int J Adv Theor Appl Comput Sci Res Innov Appl. 2025;15(2):15\u201328.","journal-title":"Int J Adv Theor Appl Comput Sci Res Innov Appl"},{"issue":"1","key":"9666_CR9","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s11227-020-03296-w","volume":"77","author":"M Ghobaei-Arani","year":"2021","unstructured":"Ghobaei-Arani M, Shahidinejad A. An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. J Supercomput. 2021;77(1):711\u201350. https:\/\/doi.org\/10.1007\/s11227-020-03296-w.","journal-title":"J Supercomput"},{"issue":"6","key":"9666_CR10","doi-asserted-by":"publisher","first-page":"4027","DOI":"10.1007\/s10586-022-03634-y","volume":"25","author":"R Rani","year":"2022","unstructured":"Rani R, Khurana M, Kumar A, Kumar N. Big data dimensionality reduction techniques in iot: review, applications and open research challenges. Cluster Comput. 2022;25(6):4027\u201349. https:\/\/doi.org\/10.1007\/s10586-022-03634-y.","journal-title":"Cluster Comput"},{"key":"9666_CR11","doi-asserted-by":"publisher","unstructured":"Kashyap V, Kumar A, Kumar A, Hu YC. A systematic survey on fog and IoT driven healthcare: open challenges and research issues. Electron. 2022;11(17). https:\/\/doi.org\/10.3390\/electronics11172668.","DOI":"10.3390\/electronics11172668"},{"key":"9666_CR12","doi-asserted-by":"publisher","unstructured":"Singh T, Kumar A, Analyzing Security and Privacy issues for Multi-Cloud Service Providers Using Nessus. In: 2023 5th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2023. 2023. https:\/\/doi.org\/10.1109\/ICECCT56650.2023.10179727.","DOI":"10.1109\/ICECCT56650.2023.10179727"},{"key":"9666_CR13","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/978-3-642-30111-7_31","volume":"167","author":"A Kumar","year":"2012","unstructured":"Kumar A, Bawa S. Virtualization of large-scale data storage system to achieve dynamicity and scalability in grid computing. Adv Intell Soft Comput. 2012;167:323\u201331. https:\/\/doi.org\/10.1007\/978-3-642-30111-7_31.","journal-title":"Adv Intell Soft Comput"},{"key":"9666_CR14","doi-asserted-by":"crossref","unstructured":"Udgirkar VN, Surekha N, Nachiappan C, Jadhav B, Vats SB, S., Agme. Development of cloud infrastructure with improved auto scaling and elasticity with real time data analytics. In: Challenges in Information, Communication and Computing Technology. 2025. pp. 65\u201370.","DOI":"10.1201\/9781003559085-12"},{"issue":"1","key":"9666_CR15","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/encyclopedia5010037","volume":"5","author":"K Betti Pillippuge","year":"2025","unstructured":"Betti Pillippuge K, Khan TL, Z., Munir. Horizontal autoscaling of virtual machines in hybrid cloud infrastructures: current status, challenges, and opportunities. Encyclopedia. 2025;5(1):37.","journal-title":"Encyclopedia"},{"key":"9666_CR16","doi-asserted-by":"publisher","unstructured":"Marinho M, Prakash V, Garg L, Savaglio C, Bawa S. Effective cloud resource utilisation in cloud erp decision-making process for industry 4.0 in the united States. Electron. 2021;10(8). https:\/\/doi.org\/10.3390\/electronics10080959.","DOI":"10.3390\/electronics10080959"},{"key":"9666_CR17","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.3390\/electronics10111320","volume":"10","author":"V Prakash","year":"2021","unstructured":"Prakash V, Bawa S, Garg L. Multi-Dependency and time based resource scheduling algorithm for scientific applications in cloud computing. Electronics. 2021;10:1320.","journal-title":"Electronics"},{"issue":"2","key":"9666_CR18","first-page":"1","volume":"10","author":"S Thompson","year":"2024","unstructured":"Thompson S. Optimizing cloud migration: best practices and lessons learned. Int J Sci Eng. 2024;10(2):1\u201317.","journal-title":"Int J Sci Eng"},{"key":"9666_CR19","unstructured":"Bandari V. Optimizing IT modernization through cloud migration: strategies for a secure, efficient and cost-effective transition. Appl Res Artif Intell Cloud Comput. 2022;5(1):66\u201383. https:\/\/researchberg.com\/index.php\/araic\/article\/view\/97."},{"key":"9666_CR20","doi-asserted-by":"publisher","unstructured":"Prakash V, Odedina O, Kumar A, Garg L, Bawa S. A secure framework for the internet of things anomalies using machine learning. Discov Internet Things. 2024;4(1). https:\/\/doi.org\/10.1007\/s43926-024-00088-z.","DOI":"10.1007\/s43926-024-00088-z"},{"issue":"1","key":"9666_CR21","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1002\/spe.3248","volume":"54","author":"R Buyya","year":"2024","unstructured":"Buyya R, Ilager S, Arroba P. Energy-efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads. Softw - Pract Exp. 2024;54(1):24\u201338. https:\/\/doi.org\/10.1002\/spe.3248.","journal-title":"Softw - Pract Exp"},{"key":"9666_CR22","doi-asserted-by":"publisher","unstructured":"Olariu F. Overcoming Challenges in Migrating Modular Monolith from On-Premises to AWS Cloud. In: Proceedings - RoEduNet IEEE International Conference. 2023. https:\/\/doi.org\/10.1109\/RoEduNet60162.2023.10274946.","DOI":"10.1109\/RoEduNet60162.2023.10274946"},{"key":"9666_CR23","doi-asserted-by":"publisher","unstructured":"Sen R, Sharma A. Optimization of cost: Storage over cloud versus on premises storage. In: Proceedings\u2013\u20092020 IEEE 9th International Conference on Communication Systems and Network Technologies, CSNT 2020. 2020. pp. 179\u201381. https:\/\/doi.org\/10.1109\/CSNT48778.2020.9115736.","DOI":"10.1109\/CSNT48778.2020.9115736"},{"issue":"12","key":"9666_CR24","doi-asserted-by":"publisher","first-page":"3045","DOI":"10.1016\/j.cor.2013.06.012","volume":"40","author":"JT Tsai","year":"2013","unstructured":"Tsai JT, Fang JC, Chou JH. Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res. 2013;40(12):3045\u201355. https:\/\/doi.org\/10.1016\/j.cor.2013.06.012.","journal-title":"Comput Oper Res"},{"issue":"2","key":"9666_CR25","first-page":"1","volume":"15","author":"BP Poudel","year":"2025","unstructured":"Poudel BP, Sharma A, K., Sharma. Predictive modeling for cloud migration costs: A machine learning approach to estimating total cost of ownership for enterprises. Int J Adv Theor Appl Comput Sci Res Innov Appl. 2025;15(2):1\u201314.","journal-title":"Int J Adv Theor Appl Comput Sci Res Innov Appl"},{"key":"9666_CR26","doi-asserted-by":"publisher","unstructured":"Ma Y, Xie S, Zhong H, Lee L, Lv K. HiEngine: how to architect a Cloud-Native Memory-Optimized database engine. Proc ACM SIGMOD Int Conf Manag Data. 2022;2177\u201390. https:\/\/doi.org\/10.1145\/3514221.3526043.","DOI":"10.1145\/3514221.3526043"},{"key":"9666_CR27","doi-asserted-by":"publisher","unstructured":"Liu M, Pan L, Liu S. Cost optimization for cloud storage from user perspectives: recent advances, taxonomy, and survey. ACM Comput Surv. 2023;55(13s). https:\/\/doi.org\/10.1145\/3582883.","DOI":"10.1145\/3582883"},{"issue":"3","key":"9666_CR28","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1109\/TCC.2020.3015769","volume":"10","author":"P Osypanka","year":"2022","unstructured":"Osypanka P, Nawrocki P. Resource usage cost optimization in cloud computing using machine learning. IEEE Trans Cloud Comput. 2022;10(3):2079\u201389. https:\/\/doi.org\/10.1109\/TCC.2020.3015769.","journal-title":"IEEE Trans Cloud Comput"},{"key":"9666_CR29","doi-asserted-by":"publisher","unstructured":"Maas W, Rossi F, Caggiani M, Navaux P, Lorenzon A. Investigating cloud instances to achieve optimal trade-offs between performance-cost efficiency. Computing. 2025;107(3). https:\/\/doi.org\/10.1007\/s00607-025-01444-9.","DOI":"10.1007\/s00607-025-01444-9"},{"key":"9666_CR30","doi-asserted-by":"publisher","unstructured":"Abdulqader AF et al. Optimizing IoT performance through edge computing: reducing latency, enhancing bandwidth efficiency, and strengthening security for 2025 applications. In: Conference of Open Innovation Association, FRUCT. 2024. pp. 145\u201358. https:\/\/doi.org\/10.23919\/FRUCT64283.2024.10749858.","DOI":"10.23919\/FRUCT64283.2024.10749858"},{"key":"9666_CR31","doi-asserted-by":"publisher","unstructured":"Prakash V, Bawa S, Garg L. Multi-dependency and time based resource scheduling algorithm for scientific applications in cloud computing. Electron. 2021;10(11). https:\/\/doi.org\/10.3390\/electronics10111320.","DOI":"10.3390\/electronics10111320"},{"issue":"7","key":"9666_CR32","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1109\/TPDS.2020.2968913","volume":"31","author":"B Wan","year":"2020","unstructured":"Wan B, Dang J, Li Z, Gong H, Zhang F, Oh S. Modeling analysis and cost-performance ratio optimization of virtual machine scheduling in cloud computing. IEEE Trans Parallel Distrib Syst. 2020;31(7):1518\u201332. https:\/\/doi.org\/10.1109\/TPDS.2020.2968913.","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"9666_CR33","doi-asserted-by":"publisher","unstructured":"Khazaei H, Mi\u0161i\u0107 J, Mi\u0161i\u0107 VB. Modelling of cloud computing centers using M\/G\/m queues. In: Proceedings - International Conference on Distributed Computing Systems. 2011. pp. 87\u201392. https:\/\/doi.org\/10.1109\/ICDCSW.2011.13.","DOI":"10.1109\/ICDCSW.2011.13"},{"issue":"3","key":"9666_CR34","doi-asserted-by":"publisher","first-page":"2367","DOI":"10.1007\/s10586-021-03269-5","volume":"24","author":"JK Valappil Thekkepuryil","year":"2021","unstructured":"Valappil Thekkepuryil JK, Suseelan DP, Keerikkattil PM. An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment. Cluster Comput. 2021;24(3):2367\u201384. https:\/\/doi.org\/10.1007\/s10586-021-03269-5.","journal-title":"Cluster Comput"},{"key":"9666_CR35","doi-asserted-by":"publisher","unstructured":"Shishira SR, Kandasamy A, Chandrasekaran K. Survey on meta heuristic optimization techniques in cloud computing. In: 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016. 2016. pp. 1434\u2013440. https:\/\/doi.org\/10.1109\/ICACCI.2016.7732249.","DOI":"10.1109\/ICACCI.2016.7732249"},{"issue":"1","key":"9666_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40064-015-0962-2","volume":"4","author":"R Rai","year":"2015","unstructured":"Rai R, Sahoo G, Mehfuz S. Exploring the factors influencing the cloud computing adoption: a systematic study on cloud migration. Springerplus. 2015;4(1):1\u201312. https:\/\/doi.org\/10.1186\/s40064-015-0962-2.","journal-title":"Springerplus"},{"issue":"4","key":"9666_CR37","first-page":"405","volume":"41","author":"A Khajeh-Hosseini","year":"2011","unstructured":"Khajeh-Hosseini A, Greenwood D, Sommerville I. The cloud adoption toolkit: supporting cloud adoption decisions in the enterprise. Softw Pract Exp. 2011;41(4):405\u201330.","journal-title":"Softw Pract Exp"},{"issue":"11","key":"9666_CR38","doi-asserted-by":"publisher","first-page":"2408","DOI":"10.1109\/TPDS.2019.2917900","volume":"30","author":"L Wang","year":"2019","unstructured":"Wang L. Architecture-Based Reliability-Sensitive criticality measure for Fault-Tolerance cloud applications. IEEE Trans Parallel Distrib Syst. 2019;30(11):2408\u201321. https:\/\/doi.org\/10.1109\/TPDS.2019.2917900.","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"9666_CR39","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.suscom.2018.02.001","volume":"17","author":"MH Malekloo","year":"2018","unstructured":"Malekloo MH, Kara N, Barachi ME. An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain Comput Inf Syst. 2018;17:9\u201324. https:\/\/doi.org\/10.1016\/j.suscom.2018.02.001.","journal-title":"Sustain Comput Inf Syst"},{"issue":"5","key":"9666_CR40","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1109\/TFUZZ.2018.2879789","volume":"27","author":"B Wang","year":"2019","unstructured":"Wang B, Xie H, Xia X, Zhang X. A NSGA-II algorithm hybridizing local Simulated-Annealing operators for a Bi-Criteria robust Job-Shop scheduling problem under scenarios. IEEE Trans Fuzzy Syst. 2019;27(5):1075\u201384. https:\/\/doi.org\/10.1109\/TFUZZ.2018.2879789.","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"9666_CR41","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TSC.2017.2711009","volume":"11","author":"Y Al-Dhuraibi","year":"2018","unstructured":"Al-Dhuraibi Y, Paraiso F, Djarallah N, Merle P. Elasticity in cloud computing: state of the Art and research challenges. IEEE Trans Serv Comput. 2018;11(2):430\u201347. https:\/\/doi.org\/10.1109\/TSC.2017.2711009.","journal-title":"IEEE Trans Serv Comput"},{"key":"9666_CR42","doi-asserted-by":"publisher","unstructured":"Rawat PS, Kumar N. Cost optimization model for cloud using machine learning and artificial intelligence. Adv Comput Tech Optim Cloud. 2024;170\u20139. https:\/\/doi.org\/10.1201\/9781003457152-9.","DOI":"10.1201\/9781003457152-9"},{"key":"9666_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-024-02224-1","author":"S Singhal","year":"2024","unstructured":"Singhal S, Sharma A. An energy-aware migration framework using metaheuristic algorithm in cloud computing. Knowl Inf Syst. 2024. https:\/\/doi.org\/10.1007\/s10115-024-02224-1.","journal-title":"Knowl Inf Syst"},{"key":"9666_CR44","doi-asserted-by":"publisher","unstructured":"Long G, Wang S, Lv C. QoS-aware resource management in cloud computing based on fuzzy meta-heuristic method. Cluster Comput. 2025;28(4). https:\/\/doi.org\/10.1007\/s10586-024-05021-1.","DOI":"10.1007\/s10586-024-05021-1"},{"key":"9666_CR45","doi-asserted-by":"publisher","unstructured":"Mohammad Hasani Zade B, Mansouri N, Javidi MM. An efficient task scheduling algorithm leveraging fuzzy quantum atom search optimizer with innovative migration for cloud computing. Cluster Comput. 2025;28(4). https:\/\/doi.org\/10.1007\/s10586-024-04953-y.","DOI":"10.1007\/s10586-024-04953-y"},{"key":"9666_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-024-01949-0","author":"N Madyavanhu","year":"2024","unstructured":"Madyavanhu N, Kumar V. Utilizing multi-population ant colony system and exponential grey prediction model for multi-objective virtual machine consolidation in cloud data centers. Int J Inf Technol. 2024. https:\/\/doi.org\/10.1007\/s41870-024-01949-0.","journal-title":"Int J Inf Technol"},{"key":"9666_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08151-7","author":"S Alangaram","year":"2023","unstructured":"Alangaram S, Balakannan SP. Optimization of cloud data centre resources using meta-heuristic approaches. Soft Comput. 2023. https:\/\/doi.org\/10.1007\/s00500-023-08151-7.","journal-title":"Soft Comput"},{"key":"9666_CR48","doi-asserted-by":"publisher","unstructured":"Zhou J, et al. Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing. J Cloud Comput. 2023;12(1). https:\/\/doi.org\/10.1186\/s13677-023-00453-3.","DOI":"10.1186\/s13677-023-00453-3"},{"issue":"4","key":"9666_CR49","doi-asserted-by":"publisher","first-page":"983","DOI":"10.3233\/IDT-230264","volume":"17","author":"HN Infantia","year":"2023","unstructured":"Infantia HN, Anbuananth C, Kalarani S. An effective process of VM migration with hybrid heuristic-assisted encryption technique for secured data transmission in cloud environment. Intell Decis Technol. 2023;17(4):983\u20131006. https:\/\/doi.org\/10.3233\/IDT-230264.","journal-title":"Intell Decis Technol"},{"key":"9666_CR50","doi-asserted-by":"publisher","unstructured":"Khan MSA, Santhosh R. Hybrid optimization algorithm for VM migration in cloud computing. Comput Electr Eng. 2022;102. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108152.","DOI":"10.1016\/j.compeleceng.2022.108152"},{"issue":"3","key":"9666_CR51","doi-asserted-by":"publisher","first-page":"69","DOI":"10.4018\/IJSI.2020070105","volume":"8","author":"N Chawla","year":"2020","unstructured":"Chawla N, Kumar D, Sharma DK. Improving cost for data migration in cloud computing using genetic algorithm. Int J Softw Innov. 2020;8(3):69\u201381. https:\/\/doi.org\/10.4018\/IJSI.2020070105.","journal-title":"Int J Softw Innov"},{"key":"9666_CR52","doi-asserted-by":"publisher","unstructured":"Sha J, Ebadi AG, Mavaluru D, Alshehri M, Alfarraj O, Rajabion L. A method for virtual machine migration in cloud computing using a collective behavior-based metaheuristics algorithm. Concurr Comput Pract Exp. 2020;32(2). https:\/\/doi.org\/10.1002\/cpe.5441.","DOI":"10.1002\/cpe.5441"},{"key":"9666_CR53","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-981-19-0924-5_6","volume":"108","author":"FH Yusoff","year":"2022","unstructured":"Yusoff FH, Kamarudin SNK, Harun N. Big Data-Based image Handling\u2014A review of implementation using Amazon web services. Stud Big Data. 2022;108:95\u2013106. https:\/\/doi.org\/10.1007\/978-981-19-0924-5_6.","journal-title":"Stud Big Data"},{"key":"9666_CR54","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/978-3-031-52272-7_8","volume":"F2564","author":"RF Shabina","year":"2024","unstructured":"Shabina RF, Ali H, Jahankhani Y, Siddiqi, Hassan B. Ensuring Securing PII data in the AWS cloud: A comprehensive guide to PCI DSS compliance. Adv Sci Technol Secur Appl. 2024;F2564:185\u2013216. https:\/\/doi.org\/10.1007\/978-3-031-52272-7_8. Part.","journal-title":"Adv Sci Technol Secur Appl"},{"key":"9666_CR55","doi-asserted-by":"publisher","unstructured":"Uthej K, Keerthan NKS, Musunuru NK, Beena BM. Cloud-Infused AWS Services: Automobile Database Management System. In: Proceedings - ICNEWS 2024: 2nd International Conference on Networking, Embedded and Wireless Systems: Wireless Technology - Building a Digital World. 2024. https:\/\/doi.org\/10.1109\/ICNEWS60873.2024.10731084.","DOI":"10.1109\/ICNEWS60873.2024.10731084"},{"key":"9666_CR56","doi-asserted-by":"publisher","unstructured":"Murugesan GK. Cloud cost factors and AWS cost optimization techniques. In: 12th International Symposium on Digital Forensics and Security, ISDFS 2024, 2024. https:\/\/doi.org\/10.1109\/ISDFS60797.2024.10527314.","DOI":"10.1109\/ISDFS60797.2024.10527314"},{"issue":"3","key":"9666_CR57","doi-asserted-by":"publisher","first-page":"592","DOI":"10.3390\/iot5030026","volume":"5","author":"V Ajith","year":"2024","unstructured":"Ajith V, Cyriac T, Chavda C, Kiyani AT, Chennareddy V, Ali K. Analyzing docker vulnerabilities through static and dynamic methods and enhancing IoT security with AWS IoT core, cloudwatch, and guardduty. IoT. 2024;5(3):592\u2013607. https:\/\/doi.org\/10.3390\/iot5030026.","journal-title":"IoT"},{"key":"9666_CR58","doi-asserted-by":"publisher","unstructured":"Gawankar Y, Naik S. Anticipating the evolution of data accountability through technological advancements and regulatory landscape changes. Cloud Secur Concepts Appl Pract. 2024;143\u201359. https:\/\/doi.org\/10.1201\/9781003455448-8.","DOI":"10.1201\/9781003455448-8"},{"key":"9666_CR59","doi-asserted-by":"crossref","unstructured":"Mirjalili S, S., Mirjalili. Genetic algorithm. Evolutionary algorithms and neural networks. Theory Appl. 2019;33\u201355.","DOI":"10.1007\/978-3-319-93025-1_4"},{"key":"9666_CR60","doi-asserted-by":"publisher","unstructured":"Nikravesh AY, Ajila SA, Lung CH. Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker. J Cloud Comput. 2018;7(1). https:\/\/doi.org\/10.1186\/s13677-018-0122-7.","DOI":"10.1186\/s13677-018-0122-7"},{"key":"9666_CR61","doi-asserted-by":"publisher","unstructured":"Guo L, Zhao S, Shen S, Jiang C. Task scheduling optimization in cloud computing based on heuristic Algorithm. J Networks. 2012;7(3):547\u201353. https:\/\/doi.org\/10.4304\/jnw.7.3.547-553.","DOI":"10.4304\/jnw.7.3.547-553"},{"key":"9666_CR62","doi-asserted-by":"publisher","unstructured":"Madni SHH, Abd Latiff MS, Abdullahi M, Abdulhamid SM, Usman MJ. Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE. 2017;12(5). https:\/\/doi.org\/10.1371\/journal.pone.0176321.","DOI":"10.1371\/journal.pone.0176321"},{"key":"9666_CR63","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1002\/mcda.1486","volume":"20","author":"V Mattila","year":"2013","unstructured":"Mattila V, Virtanen K, H\u00e4m\u00e4l\u00e4inen RP. A simulated annealing algorithm for noisy multiobjective optimization. J Multi-Criteria Decis Anal. 2013;20:5\u20136. https:\/\/doi.org\/10.1002\/mcda.1486.","journal-title":"J Multi-Criteria Decis Anal"},{"issue":"1","key":"9666_CR64","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1109\/3477.826960","volume":"30","author":"B Li","year":"2000","unstructured":"Li B, Jiang W. A novel stochastic optimization algorithm. IEEE Trans Syst Man Cybern Part B Cybern. 2000;30(1):193\u20138. https:\/\/doi.org\/10.1109\/3477.826960.","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"key":"9666_CR65","doi-asserted-by":"publisher","unstructured":"M. D\u0130R\u0130K, Comparison of recent Meta-Heuristic optimization algorithms using different benchmark functions. J Math Sci Model, 5, 3, pp. 113\u201324, 2022, https:\/\/doi.org\/10.33187\/jmsm.1115792","DOI":"10.33187\/jmsm.1115792"},{"issue":"3","key":"9666_CR66","doi-asserted-by":"publisher","first-page":"508","DOI":"10.33793\/acperpro.02.03.41","volume":"2","author":"F Ar\u0131c\u0131","year":"2019","unstructured":"Ar\u0131c\u0131 F, Kaya E. Comparison of Meta-heuristic algorithms on benchmark functions. Acad Perspect Procedia. 2019;2(3):508\u201317. https:\/\/doi.org\/10.33793\/acperpro.02.03.41.","journal-title":"Acad Perspect Procedia"},{"issue":"11","key":"9666_CR67","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1007\/s00607-018-0674-x","volume":"101","author":"A Kumar","year":"2019","unstructured":"Kumar A, Bawa S. Generalized ant colony optimizer: swarm-based meta-heuristic algorithm for cloud services execution. Computing. 2019;101(11):1609\u201332. https:\/\/doi.org\/10.1007\/s00607-018-0674-x.","journal-title":"Computing"},{"issue":"3","key":"9666_CR68","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim. 2007;39(3):459\u201371. https:\/\/doi.org\/10.1007\/s10898-007-9149-x.","journal-title":"J Glob Optim"},{"key":"9666_CR69","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/978-3-540-72950-1_77","volume":"4529 LNAI","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2007;4529 LNAI:789\u201398. https:\/\/doi.org\/10.1007\/978-3-540-72950-1_77.","journal-title":"Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics)"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09666-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09666-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09666-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T13:15:18Z","timestamp":1757250918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09666-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9666"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09666-3","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]},"assertion":[{"value":"12 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with animals performed by any of the authors. This article contains no studies with human participants or animals performed by any authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"151"}}