An advanced parallel hybrid metaheuristic approach for multi-objective optimization in cloud task scheduling (original) (raw)

• Kumar R et al (2023) A user-priorities-based strategy for three-phase intelligent recommendation and negotiating agents for cloud services, IEEE Access, vol. 11, no. January, pp. 26932–26944. https://doi.org/10.1109/ACCESS.2023.3254552
• De la Prieta F, Rodríguez-González S, Chamoso P, Corchado JM, Bajo J (2019) Survey of agent-based cloud computing applications. Future Generation Comput Syst 100:223–236. https://doi.org/10.1016/j.future.2019.04.037
  Article Google Scholar
• Savaglio C, Ganzha M, Paprzycki M, Bădică C, Ivanović M, Fortino G (2020) Agent-based internet of things: state-of-the-art and research challenges. Future Generation Comput Syst 102:1038–1053. https://doi.org/10.1016/j.future.2019.09.016
  Article Google Scholar
• Rimol M (2021) Gartner says four trends are shaping the future of public cloud, Gartner. https://www.gartner.com/en/newsroom/press-releases/2021-08-02-gartner-says-four-trends-are-shaping-the-future-of-public-cloud. Accessed 15 Mar 2025
• Poess M, Nambiar RO (2008) Energy cost, the key challenge of today’s data centers: a power consumption analysis of TPC-C results, Proceedings of the VLDB Endowment, vol. 1, no. 2, pp. 1229–1240. https://doi.org/10.14778/1454159.1454162
• Sun J, Cho H (2022) A lightweight optimal scheduling algorithm for energy-efficient and real-time cloud services, IEEE Access, vol. 10, no. February, pp. 5697–5714. https://doi.org/10.1109/ACCESS.2022.3141086
• Liu Y, Wei X, Xiao J, Liu Z, Xu Y, Tian Y (2020) Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers. Global Energy Interconnect 3(3):272–282. https://doi.org/10.1016/j.gloei.2020.07.008
  Article Google Scholar
• Naeem M, Rehman S, Javaid N, Rasheed S, Hassan K (2018) Min-min scheduling algorithm for efficient resource distribution using cloud and fog in smart buildings, pp. 15–27
• Barut C, Yildirim G, Tatar Y (2024) An intelligent and interpretable rule-based metaheuristic approach to task scheduling in cloud systems. Knowl Based Syst 284. https://doi.org/10.1016/j.knosys.2023.111241
• Khan MSA, Santhosh R (2022) Task scheduling in cloud computing using hybrid optimization algorithm. Soft Comput 26(23):13069–13079. https://doi.org/10.1007/s00500-021-06488-5
  Article Google Scholar
• Pradhan P, Behera PK, Ray BNB (2016) Modified round robin algorithm for resource allocation in cloud computing. Procedia Comput Sci 85(Cms): 878–890. https://doi.org/10.1016/j.procs.2016.05.278
• Duan R, Prodan R, Li X (2014) Multi-objective game theoretic scheduling of bag-of-tasks workflows on hybrid clouds. IEEE Trans Cloud Comput 2(1):29–42. https://doi.org/10.1109/TCC.2014.2303077
  Article Google Scholar
• Yang J, Jiang B, Lv Z, Choo K-KR (Apr. 2020) A task scheduling algorithm considering game theory designed for energy management in cloud computing. Future Generation Comput Syst 105:985–992. https://doi.org/10.1016/j.future.2017.03.024
• Sheng S, Chen P, Chen Z, Wu L, Yao Y (2021) Deep reinforcement learning-based task scheduling in IoT edge computing, Sensors, vol. 21, no. 5, p. 1666, Feb. https://doi.org/10.3390/s21051666
• Song P et al (2021) A deep reinforcement Learning-based task scheduling algorithm for energy efficiency in data centers. Proc - Int Conf Comput Commun Networks ICCCN 2021–July:1–9. https://doi.org/10.1109/ICCCN52240.2021.9522309
  Article Google Scholar
• Nabi S, Ahmad M, Ibrahim M, Hamam H (2022) AdPSO: adaptive PSO-Based task scheduling approach for cloud computing. Sensors 22(3):1–22. https://doi.org/10.3390/s22030920
  Article Google Scholar
• Chen X et al (2020) A WOA-Based optimization approach for task scheduling in cloud computing systems. IEEE Syst J 14(3):3117–3128. https://doi.org/10.1109/JSYST.2019.2960088
  Article Google Scholar
• Bürkük M, Yıldırım G (2022) Cloneable jellyfish search optimizer based task scheduling in cloud environments, Türk Doğa ve Fen Dergisi 11(3): 35–43. https://doi.org/10.46810/tdfd.1123962
• Kaur S, Verma A (2012) An efficient approach to genetic algorithm for task scheduling in cloud computing environment. Int J Inform Technol Comput Sci 4(10):74–79. https://doi.org/10.5815/ijitcs.2012.10.09
  Article Google Scholar
• Zhu QH, Tang H, Huang JJ, Hou Y, Constraints (2021) IEEE/CAA J Automatica Sinica 8(4):848–865. https://doi.org/10.1109/JAS.2021.1003934
  Article MathSciNet Google Scholar
• Tsai JT, Fang JC, Chou JH (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055. https://doi.org/10.1016/j.cor.2013.06.012
  Article Google Scholar
• Al-Maamari A, Omara FA (2015) Task scheduling using PSO algorithm in cloud computing environments. Int J Grid Distrib Comput 8(5):245–256. https://doi.org/10.14257/ijgdc.2015.8.5.24
  Article Google Scholar
• Ge Y, Wei G (2010) GA-based task scheduler for the cloud computing systems. Proc – 2010 Int Conf Web Inform Syst Min WISM 2010 2:181–186. https://doi.org/10.1109/WISM.2010.87
  Article Google Scholar
• Yildirim G (2023) A novel hybrid multi-thread metaheuristic approach for fake news detection in social media. Appl Intell 53(9):11182–11202. https://doi.org/10.1007/s10489-022-03972-9
  Article Google Scholar
• Sathya Sofia A, GaneshKumar P (2018) Multi-objective task scheduling to minimize energy consumption and Makespan of cloud computing using NSGA-II. J Netw Syst Manage 26(2):463–485. https://doi.org/10.1007/s10922-017-9425-0
  Article Google Scholar
• Xiu X, Li J, Long Y, Wu W (2023) MRLCC: an adaptive cloud task scheduling method based on meta reinforcement learning. J Cloud Comput 12(1):75. https://doi.org/10.1186/s13677-023-00440-8
  Article Google Scholar
• Choppara P, Mangalampalli S (2024) An efficient deep reinforcement learning based task scheduler in cloud-fog environment. Cluster Comput 28(1):67. https://doi.org/10.1007/s10586-024-04712-z
  Article Google Scholar
• Wang Z et al (2023) Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing. J Cloud Comput 12(1):174. https://doi.org/10.1186/s13677-023-00553-0
  Article Google Scholar
• Sing R, Bhoi SK, Panigrahi N, Sahoo KS, Bilal M, Shah SC (2022) EMCS: An energy-efficient makespan cost-aware scheduling algorithm using evolutionary learning approach for cloud-fog-based IoT applications, Sustainability, vol. 14, no. 22. https://doi.org/10.3390/su142215096
• Alsamarai NA, Uçan ON (2024) Improved performance and cost algorithm for scheduling IoT tasks in Fog–Cloud environment using Gray Wolf optimization algorithm. 14(4). Applied Sciences10.3390/app14041670
• Lahza H, R SB, Lahza HFM (2024) Adaptive Multi-Objective resource allocation for Edge-Cloud workflow optimization using deep reinforcement learning. Modelling 5(3):1298–1313. https://doi.org/10.3390/modelling5030067
  Article Google Scholar
• Khaledian N, Khamforoosh K, Akraminejad R, Abualigah L, Javaheri D (2024) An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment. Computing 106(1):109–137. https://doi.org/10.1007/s00607-023-01215-4
  Article Google Scholar
• Gu Y, Budati C (2020) Energy-aware workflow scheduling and optimization in clouds using bat algorithm. Future Generation Comput Syst 113:106–112. https://doi.org/10.1016/j.future.2020.06.031
  Article Google Scholar
• Li C, Chen L (2024) Optimization for energy-aware design of task scheduling in heterogeneous distributed systems: a meta-heuristic based approach. Computing 106:2007–2031. https://doi.org/10.1007/s00607-024-01282-1
  Article Google Scholar
• Abdel-Basset M, El-Shahat D, Elhoseny M, Song H (2021) Energy-Aware metaheuristic algorithm for Industrial-Internet-of-Things task scheduling problems in fog computing applications. IEEE Internet Things J 8(16):12638–12649. https://doi.org/10.1109/JIOT.2020.3012617
  Article Google Scholar
• Deng Z, Yan Z, Huang H, Shen H (2020) Energy-Aware task scheduling on heterogeneous computing systems with time constraint. IEEE Access 8:23936–23950. https://doi.org/10.1109/ACCESS.2020.2970166
  Article Google Scholar
• Saif F, Latip R, Zurina MH, Shafinah K (Jan. 2023) Multi-Objective grey Wolf optimizer algorithm for task scheduling in Cloud-Fog computing. IEEE Access 11:20635–20646. https://doi.org/10.1109/ACCESS.2023.3241240
• Aslan Ş (Feb. 2025) Energy-aware scheduling in flow shops: a novel artificial neural network-driven multi-objective optimization. Eng Optim 57(2):333–360. https://doi.org/10.1080/0305215X.2024.2420738
• Khan HOA, Tahir MU, Ahmad A, Arsh N Reduction in weighted average cost of generation by utilizing tou pricing models: a study from Pakistan, in (2022) 11th International Conference on Renewable Energy Research and Application (ICRERA), 2022, pp. 119–124. https://doi.org/10.1109/ICRERA55966.2022.9922789
• Felloussi M, Delorme X, Gianessi P (2025) Minimizing energy cost in a job-shop scheduling problem under tou pricing: a new method based on a period-indexed MILP, in 14th International Conference on Operations Research and Enterprise Systems, pp. 320–327
• de Sá Ferreira R, Barroso LA, Lino PR, Carvalho MM, Valenzuela P (2013) Time-of-Use tariff design under uncertainty in Price-Elasticities of electricity demand: a stochastic optimization approach. IEEE Trans Smart Grid 4(4):2285–2295. https://doi.org/10.1109/TSG.2013.2241087
  Article Google Scholar
• Ding J-Y, Song S, Zhang R, Chiong R, Wu C (2016) Parallel machine scheduling under Time-of-Use electricity prices: new models and optimization approaches. IEEE Trans Autom Sci Eng 13(2):1138–1154. https://doi.org/10.1109/TASE.2015.2495328
  Article Google Scholar
• Chen L, Zhang J, Chen Z, Demeulemeester E (2025) Integrated hybrid energy and time-of-use electricity tariffs for the resource-constrained project scheduling problem. Available SSRN 5222269
• Luo D, Zhao D, Cao Z, Wu M, Liu L, Ma H (2023) M3AN: multitask multirange multisubgraph attention network for Condition-Aware traffic prediction. IEEE Trans Intell Transp Syst 24(1):218–232. https://doi.org/10.1109/TITS.2022.3216678
  Article Google Scholar
• Shen T, Sun L (2024) Multi-Dimensional evaluation of the operational benefits of integrated energy systems in Zero-Carbon parks using game theory and fuzzy comprehensive evaluation. https://doi.org/10.56578/jgelcd030203
• Lim D-K, Woo D-K, Yeo H-K, Jung S-Y, Ro J, Jung H-K (2015) A novel Surrogate-Assisted Multi-Objective optimization algorithm for an electromagnetic machine design. IEEE Trans Magn 51(3):1–4. https://doi.org/10.1109/TMAG.2014.2359452
  Article Google Scholar
• Li J, Luo G, Yuan Q, Li J, C-V2X Resource allocation and power control for cooperative perception through game theory, in (2024) IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI), 2024, pp. 529–534. https://doi.org/10.1109/DTPI61353.2024.10778907
• Ala A, Goli A (2024) Incorporating machine learning and optimization techniques for assigning patients to operating rooms by considering fairness policies. Eng Appl Artif Intell 136:108980. https://doi.org/10.1016/j.engappai.2024.108980
  Article Google Scholar
• Saghafi Oskuei E, Yousefi Nejad Attari M, Ala A, Simic V, Pamucar D (2025) Combining preventative maintenance planning and production scheduling with sequence dependent setups for decomposing items. Eng Appl Artif Intell 157:111550. https://doi.org/10.1016/j.engappai.2025.111550
  Article Google Scholar
• Ala A, Simic V, Pamucar D, Bacanin N (2024) Enhancing patient information performance in internet of things-based smart healthcare system: hybrid artificial intelligence and optimization approaches. Eng Appl Artif Intell 131:107889. https://doi.org/10.1016/j.engappai.2024.107889
  Article Google Scholar
• Stewart RH, Palmer TS, DuPont B (2021) A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers. Prog Nucl Energy 138:103830. https://doi.org/10.1016/j.pnucene.2021.103830
  Article Google Scholar
• Taha K (2020) Methods that optimize multi-objective problems: a survey and experimental evaluation. IEEE Access 8(1):80855–80878. https://doi.org/10.1109/ACCESS.2020.2989219
  Article Google Scholar
• Luo Q, Wu G, Ji B, Wang L, Suganthan PN (2022) Hybrid Multi-objective optimization approach with Pareto local search for collaborative Truck-Drone routing problems considering flexible time windows. IEEE Trans Intell Transp Syst 23(8):13011–13025. https://doi.org/10.1109/TITS.2021.3119080
  Article Google Scholar
• Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017
  Article Google Scholar
• Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput. https://doi.org/10.1109/4235.797969
  Article Google Scholar
• Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength Pareto evolutionary algorithm. TIK report 103. https://doi.org/10.3929/ethz-a-004284029
• Mason K, Duggan J, Howley E (2017) Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants. Neurocomputing 270:188–197. https://doi.org/10.1016/j.neucom.2017.03.086
  Article Google Scholar
• Coello Coello CA, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization, in Proceedings of the Evolutionary Computation on 2002. CEC ’02. Proceedings of the 2002 Congress - Volume 02, in CEC ’02. USA: IEEE Computer Society, pp. 1051–1056
• Lalwani S, Singhal S, Kumar R, Gupta N (2013) A comprehensive survey: Multi-Objective Particle Swarm Optimization (MOPSO) algorithm: variants and applications, Transactions on Combinatorics, vol. 2, pp. 89–101, Aug
• Alaya I, Solnon C, Ghedira K (2007) Ant Colony Optimization for Multi-objective Optimization Problems, in 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Patras, Greece: IEEE Computer Society, Oct. pp. 450–457. [Online]. Available: https://hal.science/hal-01502167
• Hussain A, Aleem M (2018) GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures. Data (Basel). https://doi.org/10.3390/data3040038