{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T18:41:53Z","timestamp":1781376113016,"version":"3.54.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Halic University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In this paper, we address the multi-objective task scheduling problem in cloud computing environments for IoT-generated tasks, focusing on minimizing makespan, load imbalance, energy consumption, and CO<jats:sub>2<\/jats:sub> emissions. We propose a novel load imbalance metric tailored for this specific context and introduce an enhanced Equilibrium Optimizer (EO) algorithm, termed CEEO. The CEEO integrates a Coulomb operator inspired by Coulomb\u2019s law to improve exploration and escape local optima in multimodal optimization problems. The algorithm\u2019s performance is evaluated using standard benchmark functions from CEC2017, where it demonstrates significant improvements over existing optimization algorithms in terms of convergence speed and solution quality. In addition, the CEEO is applied to a simulated cloud computing task scheduling environment, considering varying numbers of tasks and virtual machines. The results reveal that CEEO consistently outperforms other scheduling algorithms, reducing makespan and improving load balancing, energy consumption, and CO<jats:sub>2<\/jats:sub> emissions. Statistical analysis using the Friedman test confirms the statistical significance of these improvements. This research provides an efficient and robust solution for multi-objective task scheduling in cloud computing, offering substantial improvements in operational costs, execution time, and overall service quality.<\/jats:p>","DOI":"10.1007\/s00607-025-01495-y","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T06:02:08Z","timestamp":1748239328000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["CEEO: an innovative Coulomb-enhanced Equilibrium Optimizer for task scheduling of cloud services in IoT environments"],"prefix":"10.1007","volume":"107","author":[{"given":"Sepehr","family":"Ebrahimi Mood","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Souri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nihat","family":"\u0130nan\u00e7","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan-Ching","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"issue":"4","key":"1495_CR1","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TPDS.2019.2953745","volume":"31","author":"Z Chen","year":"2019","unstructured":"Chen Z, Hu J, Min G, Zomaya AY, El-Ghazawi T (2019) Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning. IEEE Trans Parallel Distrib Syst 31(4):923\u2013934","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"1495_CR2","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhang J, Min G, Ning Z, Li J (2024) Traffic-aware lightweight hierarchical offloading towards adaptive slicing-enabled Sagin. IEEE J Sel Areas Commun","DOI":"10.1109\/JSAC.2024.3459020"},{"key":"1495_CR3","doi-asserted-by":"crossref","unstructured":"Chen Z, Huang S, Min G, Ning Z, Li J, Zhang Y (2025) Mobility-aware seamless service migration and resource allocation in Multi-edge IoV systems. IEEE Trans Mob Comput","DOI":"10.1109\/TMC.2025.3540407"},{"key":"1495_CR4","doi-asserted-by":"crossref","unstructured":"Hasan MK, Weichen Z, Safie N, Ahmed FRA, Ghazal TM (2024) A survey on key agreement and authentication protocol for internet of things application. IEEE Access","DOI":"10.1109\/ACCESS.2024.3393567"},{"issue":"4","key":"1495_CR5","doi-asserted-by":"publisher","first-page":"e4329","DOI":"10.1002\/ett.4329","volume":"35","author":"A Dhar Dwivedi","year":"2024","unstructured":"Dhar Dwivedi A, Singh R, Kaushik K, Mukkamala RR, Alnumay WS (2024) Blockchain and artificial intelligence for 5G-enabled internet of things: challenges, opportunities, and solutions. Trans Emerg Telecommunications Technol 35(4):e4329","journal-title":"Trans Emerg Telecommunications Technol"},{"key":"1495_CR6","doi-asserted-by":"crossref","unstructured":"Singh R, Mishra R, Aher RN, Ghosal A, Chakraborty N (2023) A survey on cloud computing approaches, in 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI): IEEE, pp. 282\u2013285","DOI":"10.1109\/ICCSAI59793.2023.10421324"},{"key":"1495_CR7","doi-asserted-by":"crossref","unstructured":"Cao F, Zhu MM Energy-aware workflow job scheduling for green clouds, in (2013) IEEE international conference on green computing and communications and IEEE internet of things and IEEE cyber, physical and social computing, 2013: IEEE, pp. 232\u2013239","DOI":"10.1109\/GreenCom-iThings-CPSCom.2013.58"},{"key":"1495_CR8","doi-asserted-by":"publisher","first-page":"106804","DOI":"10.1016\/j.eneco.2023.106804","volume":"125","author":"X Yu","year":"2023","unstructured":"Yu X, Hu Y, Zhou D, Wang Q, Sang X, Huang K (2023) Carbon emission reduction analysis for cloud computing industry: can carbon emissions trading and technology innovation help? Energy Econ 125:106804","journal-title":"Energy Econ"},{"key":"1495_CR9","doi-asserted-by":"crossref","unstructured":"Chen Z, Liang J, Yu Z, Cheng H, Min G, Li J (2024) Resilient collaborative caching for multi-edge systems with robust federated deep learning. IEEE\/ACM Trans Networking","DOI":"10.1109\/TNET.2024.3497958"},{"key":"1495_CR10","doi-asserted-by":"crossref","unstructured":"Ahmadabadi JZ, Mood SE, Souri A (2023) Star-quake: A new operator in multi-objective gravitational search algorithm for task scheduling in IoT based cloud-fog computing system. IEEE Trans Consum Electron","DOI":"10.1109\/TCE.2023.3321708"},{"key":"1495_CR11","doi-asserted-by":"crossref","unstructured":"Chen Z, Jiang Q, Chen L, Chen X, Li J, Min G (2025) MC-2PF: a Multi-edge cooperative universal framework for load prediction with personalized federated deep learning. IEEE Trans Mob Comput","DOI":"10.1109\/TMC.2025.3528404"},{"key":"1495_CR12","doi-asserted-by":"crossref","unstructured":"Dhabliya D et al (2024) New proposed policies and strategies for dynamic load balancing in cloud computing. Emerging trends in cloud computing analytics, scalability, and service models. IGI Global, pp 135\u2013143","DOI":"10.4018\/979-8-3693-0900-1.ch006"},{"key":"1495_CR13","doi-asserted-by":"crossref","unstructured":"Chen Z, Xiong B, Chen X, Min G, Li J (2024) Joint computation offloading and resource allocation in multi-edge smart communities with personalized federated deep reinforcement learning. IEEE Trans Mob Comput","DOI":"10.1109\/TMC.2024.3396511"},{"issue":"5","key":"1495_CR14","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1007\/s00500-023-09381-5","volume":"28","author":"F BahraniPour","year":"2024","unstructured":"BahraniPour F, Ebrahimi Mood S, Farshi M (2024) Energy-delay aware request scheduling in hybrid cloud and fog computing using improved multi-objective CS algorithm. Soft Comput 28(5):4037\u20134050","journal-title":"Soft Comput"},{"key":"1495_CR15","doi-asserted-by":"publisher","first-page":"105190","DOI":"10.1016\/j.knosys.2019.105190","volume":"191","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: A novel optimization algorithm. Knowl Based Syst 191:105190","journal-title":"Knowl Based Syst"},{"issue":"22","key":"1495_CR16","doi-asserted-by":"publisher","first-page":"15557","DOI":"10.1007\/s00521-021-06178-1","volume":"33","author":"S Ebrahimi Mood","year":"2021","unstructured":"Ebrahimi Mood S, Ding M, Lin Z, Javidi MM (2021) Performance optimization of UAV-based IoT communications using a novel constrained gravitational search algorithm. Neural Comput Appl 33(22):15557\u201315568","journal-title":"Neural Comput Appl"},{"key":"1495_CR17","doi-asserted-by":"publisher","first-page":"104618","DOI":"10.1016\/j.chemolab.2022.104618","volume":"228","author":"ZA Varzaneh","year":"2022","unstructured":"Varzaneh ZA, Hossein S, Mood SE, Javidi MM (2022) A new hybrid feature selection based on improved equilibrium optimization. Chemometr Intell Lab Syst 228:104618","journal-title":"Chemometr Intell Lab Syst"},{"issue":"8","key":"1495_CR18","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1109\/TPDS.2021.3132422","volume":"33","author":"Z Chen","year":"2021","unstructured":"Chen Z, Hu J, Min G, Luo C, El-Ghazawi T (2021) Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning. IEEE Trans Parallel Distrib Syst 33(8):1911\u20131923","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"12","key":"1495_CR19","doi-asserted-by":"publisher","first-page":"21325","DOI":"10.1109\/JIOT.2023.3331722","volume":"11","author":"Z Chen","year":"2023","unstructured":"Chen Z, Zhang J, Zheng X, Min G, Li J, Rong C (2023) Profit-aware cooperative offloading in uav-enabled mec systems using lightweight deep reinforcement learning. IEEE Internet Things J 11(12):21325\u201321336","journal-title":"IEEE Internet Things J"},{"key":"1495_CR20","doi-asserted-by":"crossref","unstructured":"Chen Z, Huang Z, Zhang J, Cheng H, Li J (2024) Resource allocation and collaborative offloading in Multi-UAV-Assisted IoV with federated deep reinforcement learning. IEEE Internet Things J","DOI":"10.1109\/JIOT.2024.3516838"},{"key":"1495_CR21","doi-asserted-by":"crossref","unstructured":"Bahranipour F, Mood SE, Souri A, Farshi M (2024) An Energy-Aware task scheduling method using the Meta-Heuristic algorithm in IoT environments. Intelligent computing on IoT 2.0, big data analytics, and block chain technology. Chapman and Hall\/CRC, pp 277\u2013291","DOI":"10.1201\/9781003326236-15"},{"key":"1495_CR22","doi-asserted-by":"publisher","first-page":"100832","DOI":"10.1016\/j.iot.2023.100832","volume":"23","author":"E Al-Masri","year":"2023","unstructured":"Al-Masri E, Souri A, Mohamed H, Yang W, Olmsted J, Kotevska O (2023) Energy-efficient cooperative resource allocation and task scheduling for internet of things environments. Internet Things 23:100832","journal-title":"Internet Things"},{"key":"1495_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.aej.2024.01.040","volume":"89","author":"FS Alsubaei","year":"2024","unstructured":"Alsubaei FS, Hamed AY, Hassan MR, Mohery M, Elnahary MK (2024) Machine learning approach to optimal task scheduling in cloud communication. Alexandria Eng J 89:1\u201330","journal-title":"Alexandria Eng J"},{"key":"1495_CR24","doi-asserted-by":"crossref","unstructured":"Kaur R et al (2023) An Advanced Job Scheduling Algorithmic Architecture to Reduce Energy Consumption and CO2 Emissions in Multi-Cloud. Electronics 12(8):1810","DOI":"10.3390\/electronics12081810"},{"key":"1495_CR25","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1504\/IJCC.2023.130927","volume":"12","author":"MA Razaak","year":"2023","unstructured":"Razaak MA, Ansari GA (2023) Greedy-based task scheduling algorithm for minimising energy and power consumption for virtual machines in cloud environment. Int J Cloud Comput 12:2\u20134","journal-title":"Int J Cloud Comput"},{"issue":"1","key":"1495_CR26","first-page":"447","volume":"17","author":"B Nagalakshmi","year":"2025","unstructured":"Nagalakshmi B, Subramanian S (2025) Multi-objective energy aware task scheduling using orthogonal learning particle swarm optimization on cloud environment. Int J Inform Technol 17(1):447\u2013454","journal-title":"Int J Inform Technol"},{"issue":"1","key":"1495_CR27","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s10586-022-03807-9","volume":"27","author":"SM Kak","year":"2024","unstructured":"Kak SM, Agarwal P, Alam MA, Siddiqui F (2024) A hybridized approach for minimizing energy in cloud computing. Cluster Comput 27(1):53\u201370","journal-title":"Cluster Comput"},{"key":"1495_CR28","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.future.2016.12.017","volume":"73","author":"Z Li","year":"2017","unstructured":"Li Z et al (2017) Energy cost minimization with job security guarantee in internet data center. Future Generation Comput Syst 73:63\u201378","journal-title":"Future Generation Comput Syst"},{"issue":"10","key":"1495_CR29","doi-asserted-by":"publisher","first-page":"29617","DOI":"10.1007\/s11042-023-16764-1","volume":"83","author":"MA Al-Betar","year":"2024","unstructured":"Al-Betar MA, Abu Doush I, Makhadmeh SN, Al-Naymat G, Alomari OA, Awadallah MA (2024) Equilibrium optimizer: a comprehensive survey. Multimedia Tools Appl 83(10):29617\u201329666","journal-title":"Multimedia Tools Appl"},{"key":"1495_CR30","doi-asserted-by":"crossref","unstructured":"Liang J-J, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization, in Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005.: IEEE, pp. 68\u201375","DOI":"10.1109\/SIS.2005.1501604"},{"issue":"13","key":"1495_CR31","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232\u20132248","journal-title":"Inf Sci"},{"key":"1495_CR32","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"1495_CR33","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris Hawks optimization: algorithm and applications. Future Generation Comput Syst 97:849\u2013872","journal-title":"Future Generation Comput Syst"},{"key":"1495_CR34","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s00521-015-1870-7","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495\u2013513","journal-title":"Neural Comput Appl"},{"issue":"5","key":"1495_CR35","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s12559-019-09665-9","volume":"11","author":"S Ebrahimi Mood","year":"2019","unstructured":"Ebrahimi Mood S, Javidi MM (2019) Rank-based gravitational search algorithm: a novel nature-inspired optimization algorithm for wireless sensor networks clustering. Cogn Comput 11(5):719\u2013734","journal-title":"Cogn Comput"},{"key":"1495_CR36","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fdez I, Canosa A, Mucientes M, Bugar\u00edn A STAC: a web platform for the comparison of algorithms using statistical tests, in (2015) IEEE international conference on fuzzy systems (FUZZ-IEEE), 2015: IEEE, pp. 1\u20138","DOI":"10.1109\/FUZZ-IEEE.2015.7337889"},{"issue":"4","key":"1495_CR37","first-page":"43","volume":"10","author":"S Azizi","year":"2022","unstructured":"Azizi S (2022) A Multi-objective model for task scheduling optimization in Fog-Cloud computing environments. J Soft Comput Inform Technol 10(4):43\u201352","journal-title":"J Soft Comput Inform Technol"},{"issue":"2","key":"1495_CR38","first-page":"472","volume":"8","author":"S Bansal","year":"2011","unstructured":"Bansal S, Kothari B, Hota C (2011) Dynamic task-scheduling in grid computing using prioritized round robin algorithm. Int J Comput Sci Issues (IJCSI) 8(2):472","journal-title":"Int J Comput Sci Issues (IJCSI)"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-025-01495-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-025-01495-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-025-01495-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T05:47:51Z","timestamp":1751003271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-025-01495-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,26]]},"references-count":38,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1495"],"URL":"https:\/\/doi.org\/10.1007\/s00607-025-01495-y","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,26]]},"assertion":[{"value":"4 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"138"}}