*Result*: Quantum-Inspired Adaptive Meta-Heuristic-Machine Learning framework for resilient and energy-efficient task scheduling in multi-cloud ecosystems.
Title:
Quantum-Inspired Adaptive Meta-Heuristic-Machine Learning framework for resilient and energy-efficient task scheduling in multi-cloud ecosystems.
Authors:
Divya N; Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India. n.divya38@gmail.com., Kiranbabu MNV; Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India., Babu GC; Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India.
Source:
Scientific reports [Sci Rep] 2026 Mar 12. Date of Electronic Publication: 2026 Mar 12.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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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. 54, 24–38. https://doi.org/10.1002/spe.3264 (2024).
Kotteswari, K., Dhanaraj, R. K., Balusamy, B., Nayyar, A. & Sharma, A. K. EELB: An energy-efficient load balancing model for cloud environment using Markov decision process. Computing 107, 81. https://doi.org/10.1007/s00607-024-01273-0 (2025).
Rasoulpour Shabestari, E. & Shameli-Sendi, A. An intelligent VM placement method for minimizing energy cost and carbon emission in distributed cloud data centers. J. Grid Comput. 23, 12. https://doi.org/10.1007/s10723-024-09692-x (2025).
Qazi, F., Kwak, D., Khan, F. G., Ali, F. & Khan, S. U. Service level agreement in cloud computing: Taxonomy, prospects, and challenges. Internet Things 25, 101126. https://doi.org/10.1016/j.iot.2023.101126 (2024).
Singh, J. & Walia, N. K. A comprehensive review of cloud computing virtual machine consolidation. IEEE Access 11, 106190–106209. https://doi.org/10.1109/ACCESS.2023.3314502 (2023).
Ma, Z., Ma, D., Lv, M. & Liu, Y. Virtual machine migration techniques for optimizing energy consumption in cloud data centers. IEEE Access 11, 86739–86753. https://doi.org/10.1109/ACCESS.2023.3298654 (2023).
Rahmani, S., Khajehvand, V. & Torabian, M. SPP: Stochastic process-based placement for VM consolidation in cloud environments. Computing 107, 43. https://doi.org/10.1007/s00607-024-01243-6 (2025).
Peng, P. Predicting residential building cooling load with a machine learning random forest approach. Int. J. Interact. Des. Manuf. 19, 3421–3434. https://doi.org/10.1007/s12008-024-01926-5 (2025).
Abdelaziz, V., Santos, M. S., Dias & Mahmoud, A. N. A hybrid model of self-organizing map and deep learning with genetic algorithm for managing energy consumption in public buildings. J. Clean. Prod. 434, 140040. https://doi.org/10.1016/j.jclepro.2023.140040 (2024).
Qasim, M. & Sajid, M. An efficient IoT task scheduling algorithm in cloud environment using modified firefly algorithm. Int. J. Inf. Technol. 17, 179–188. https://doi.org/10.1007/s41870-023-01491-4 (2024).
Aghasi, K., Jamshidi, A., Bohlooli & Javadi, B. A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers. Comput. Netw. 224, 109624. https://doi.org/10.1016/j.comnet.2023.109624 (2023).
Saadi, Y., Jounaidi, S., El Kafhali, S. & Zougagh, H. Reducing energy footprint in cloud computing: A study on the impact of clustering techniques and scheduling algorithms for scientific workflows. Computing 105, 2231–2261. https://doi.org/10.1007/s00607-023-01184-1 (2023).
Tran, H., Bui, T. K. & Pham, T. V. Virtual machine migration policy for multi-tier application in cloud computing based on Q-learning algorithm. Computing 104, 1285–1306. https://doi.org/10.1007/s00607-021-00992-0 (2022).
Qin, Y., Wang, H., Yi, S., Li, X. & Zhai, L. Virtual machine placement based on multi-objective reinforcement learning. Appl. Intell. 50, 2370–2383. https://doi.org/10.1007/s10489-019-01594-0 (2020).
Lin, W. et al. A hardware-aware CPU power measurement based on the power-exponent function model for cloud servers. Inf. Sci. 547, 1045–1065. https://doi.org/10.1016/j.ins.2020.09.040 (2021).
Mongia, V. EMaC: Dynamic VM consolidation framework for energy-efficiency and multi-metric SLA compliance in cloud data centers. SN Comput. Sci. 5, 643. https://doi.org/10.1007/s42979-024-02841-4 (2024).
Amahrouch, M., Bouhamidi, Y., Saadi & Kafhali, S. E. An efficient model based on machine learning algorithms for virtual machines classification in cloud computing environment, in Proc. 4th Int. Conf. Innov. Res. Appl. Sci., Eng. Technol. (IRASET), Fez, Morocco, May 2024, pp. 1–6. https://doi.org/10.1109/IRASET60493.2024.10510768(2024).
Srivastava & Kumar, N. An efficient firefly and honeybee based load balancing mechanism in cloud infrastructure. Clust Comput. 27, 2805–2827. https://doi.org/10.1007/s10586-024-04236-3 (2024).
Wang, J. et al. Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform. J. Cloud Comput. 11, 50. https://doi.org/10.1186/s13677-022-00362-1 (2022).
Pourghebleh, A. A., Anvigh, A. R., Ramtin & Mohammadi, B. The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments. Clust Comput. 24, 2673–2696. https://doi.org/10.1007/s10586-021-03282-w (2021).
Hayyolalam, V., Pourghebleh, B., Chehrehzad, M. R. & Kazem, A. A. P. Single-objective service composition methods in cloud manufacturing systems: Recent techniques, classification, and future trends. Concurr. Comput. Pract. Exp. 34, e6698. https://doi.org/10.1002/cpe.6698 (2022).
Shafiq, A., Jhanjhi, N. Z., Abdullah, A. & Alzain, M. A. A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access 9, 41731–41744. https://doi.org/10.1109/ACCESS.2021.3065303 (2021).
Zhang, W.-Z. et al. Secure and optimized load balancing for multitier IoT and edge-cloud computing systems. IEEE Internet Things J. 8, 8119–8132. https://doi.org/10.1109/JIOT.2020.3011723 (2020).
Saeik, F. et al. Task offloading in edge and cloud computing: A survey on mathematical, artificial intelligence and control theory solutions. Comput. Netw. 195, 108177. https://doi.org/10.1016/j.comnet.2021.108177 (2021).
Hayyolalam, V., Pourghebleh, B. & Kazem, A. A. P. Trust management of services (TMoS): Investigating the current mechanisms. Trans. Emerg. Telecommun Technol. 31, e4063. https://doi.org/10.1002/ett.4063 (2020).
Zhang, W., Chen, L., Luo, J. & Liu, J. A two-stage container management in the cloud for optimizing the load balancing and migration cost. Future Gener Comput. Syst. 135, 303–314. https://doi.org/10.1016/j.future.2022.05.008 (2022).
Tawfeeg, T. M. et al. Cloud dynamic load balancing and reactive fault tolerance techniques: A systematic literature review (SLR). IEEE Access. 10, 71853–71873. https://doi.org/10.1109/ACCESS.2022.3189300 (2022).
Kong, L., Mapetu, J. P. B. & Chen, Z. Heuristic load balancing based zero imbalance mechanism in cloud computing. J. Grid Comput. 18, 123–148. https://doi.org/10.1007/s10723-019-09497-y (2020).
Kumar, K. P2BED-C: A novel peer to peer load balancing and energy efficient technique for data-centers over cloud. Wirel. Pers. Commun. 123, 311–324. https://doi.org/10.1007/s11277-021-08992-0 (2022).
Liang, X., Dong, Y., Wang & Zhang, X. A low-power task scheduling algorithm for heterogeneous cloud computing. J. Supercomput. 76, 7290–7314. https://doi.org/10.1007/s11227-019-03076-3 (2020).
Ebrahim, M. & Hafid, A. Resilience and load balancing in fog networks: A multi-criteria decision analysis approach. Microprocess Microsyst. 101, 104893. https://doi.org/10.1016/j.micpro.2023.104893 (2023).
Kumar, Y., Kaul, S. & Hu, Y.-C. Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey. Sustain. Comput. Informatics Syst. 36, 100780. https://doi.org/10.1016/j.suscom.2022.100780 (2022).
Neelakantan, P. & Yadav, N. S. An optimized load balancing strategy for an enhancement of cloud computing environment. Wirel. Pers. Commun. 131, 1745–1765. https://doi.org/10.1007/s11277-023-10618-6 (2023).
Guo, X.-Q. et al. Edge–cloud co-evolutionary algorithms for distributed data-driven optimization problems. IEEE Trans. Cybern. 53, 6598–6611. https://doi.org/10.1109/TCYB.2021.3111102 (2022).
Kannan, K., Sunitha, G., Deepa, S., Babu, D. V. & Avanija, J. A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms. Comput. Electr. Eng. 102, 108225. https://doi.org/10.1016/j.compeleceng.2022.108225 (2022).
Sefati, S. S. et al. A Probabilistic Approach to Load Balancing in Multi-Cloud Environments via Machine Learning and Optimization Algorithms. J. Grid Comput. 23, 16 (2025).
Kim, D. Y., Lee, D. E., Kim, J. W. & Lee, H. S. Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud–Edge–Terminal IoT Networks Using Federated Reinforcement Learning. IEEE Internet Things J. 11, 10133 (2024).
Cheng, Z. et al. Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy. Future Gener. Comput. Syst. https://doi.org/10.1016/j.future.2024.06.035 (2024).
Li, X., Zhao, H. & Deng, W. IOFL: Intelligent-optimization-based federated learning for non-IID data. IEEE Internet Things J. https://doi.org/10.1109/jiot.2024.3354942 (2024).
Jiang, Q., Guo, Y., Yang, Z. & Zhou, X. A Parallel Whale Optimization Algorithm and Its Implementation on FPGA, Proceedings of the IEEE Congress on Evolutionary Computation (CEC), (2020).
Meng, Q., He, Y., Hussain, S., Lu, J. & Guerrero, J. M. Day-ahead economic dispatch of wind-integrated microgrids using coordinated energy storage and hybrid demand response strategies. Sci. Rep. 15(1), 26579. https://doi.org/10.1038/s41598-025-11561-2 (2025).
Zhang, B., Sang, H., Meng, L., Jiang, X. & Lu, C. Knowledge- and data-driven hybrid method for lot streaming scheduling in hybrid flowshop with dynamic order arrivals. Comput. Oper. Res. 184, 107244. https://doi.org/10.1016/j.cor.2025.107244 (2025).
Chen, P. et al. QoS-oriented Task Offloading in NOMA-based Multi-UAV Cooperative MEC Systems. IEEE Trans. Wireless Commun. https://doi.org/10.1109/TWC.2025.3593884 (2025).
Long, X., Chen, J., Yang, L. & Huang, H. An emergency scheduling method based on AutoML for space maneuver objective tracking. Expert Syst. Appl. 298, 129759. https://doi.org/10.1016/j.eswa.2025.129759 (2026).
Shao, S., Tian, Y., Zhang, Y. & Zhang, X. Knowledge learning-based dimensionality reduction for solving large-scale sparse multiobjective optimization problems. IEEE Trans. Cybernetics 55(7), 3471–3484. https://doi.org/10.1109/TCYB.2025.3558354 (2025).
Zhou, D. et al. Mission-driven resource scheduling in satellite-terrestrial networks: From perspective of collaboration and reconfiguration. IEEE Trans. Commun. 73(8), 6705–6719. https://doi.org/10.1109/TCOMM.2025.3529250 (2025).
Xu, W. et al. Blockchain-based verifiable decentralized identity for intelligent flexible manufacturing. IEEE Internet Things J. 12(16), 32366–32378. https://doi.org/10.1109/JIOT.2025.3576735 (2025).
Zhang, F. et al. Breaking the edge: Enabling efficient neural network inference on integrated edge devices. IEEE Trans. Cloud Comput. 13(2), 694–710. https://doi.org/10.1109/TCC.2025.3559346 (2025).
de Lima, E. C., Rossi, F. D., Luizelli, M. C., Calheiros, R. N. & Lorenzon, A. F. A neural network framework for optimizing parallel computing in cloud servers. J. Syst. Archit. https://doi.org/10.1016/j.sysarc.2024.103131 (2024).
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. 54, 24–38. https://doi.org/10.1002/spe.3264 (2024).
Kotteswari, K., Dhanaraj, R. K., Balusamy, B., Nayyar, A. & Sharma, A. K. EELB: An energy-efficient load balancing model for cloud environment using Markov decision process. Computing 107, 81. https://doi.org/10.1007/s00607-024-01273-0 (2025).
Rasoulpour Shabestari, E. & Shameli-Sendi, A. An intelligent VM placement method for minimizing energy cost and carbon emission in distributed cloud data centers. J. Grid Comput. 23, 12. https://doi.org/10.1007/s10723-024-09692-x (2025).
Qazi, F., Kwak, D., Khan, F. G., Ali, F. & Khan, S. U. Service level agreement in cloud computing: Taxonomy, prospects, and challenges. Internet Things 25, 101126. https://doi.org/10.1016/j.iot.2023.101126 (2024).
Singh, J. & Walia, N. K. A comprehensive review of cloud computing virtual machine consolidation. IEEE Access 11, 106190–106209. https://doi.org/10.1109/ACCESS.2023.3314502 (2023).
Ma, Z., Ma, D., Lv, M. & Liu, Y. Virtual machine migration techniques for optimizing energy consumption in cloud data centers. IEEE Access 11, 86739–86753. https://doi.org/10.1109/ACCESS.2023.3298654 (2023).
Rahmani, S., Khajehvand, V. & Torabian, M. SPP: Stochastic process-based placement for VM consolidation in cloud environments. Computing 107, 43. https://doi.org/10.1007/s00607-024-01243-6 (2025).
Peng, P. Predicting residential building cooling load with a machine learning random forest approach. Int. J. Interact. Des. Manuf. 19, 3421–3434. https://doi.org/10.1007/s12008-024-01926-5 (2025).
Abdelaziz, V., Santos, M. S., Dias & Mahmoud, A. N. A hybrid model of self-organizing map and deep learning with genetic algorithm for managing energy consumption in public buildings. J. Clean. Prod. 434, 140040. https://doi.org/10.1016/j.jclepro.2023.140040 (2024).
Qasim, M. & Sajid, M. An efficient IoT task scheduling algorithm in cloud environment using modified firefly algorithm. Int. J. Inf. Technol. 17, 179–188. https://doi.org/10.1007/s41870-023-01491-4 (2024).
Aghasi, K., Jamshidi, A., Bohlooli & Javadi, B. A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers. Comput. Netw. 224, 109624. https://doi.org/10.1016/j.comnet.2023.109624 (2023).
Saadi, Y., Jounaidi, S., El Kafhali, S. & Zougagh, H. Reducing energy footprint in cloud computing: A study on the impact of clustering techniques and scheduling algorithms for scientific workflows. Computing 105, 2231–2261. https://doi.org/10.1007/s00607-023-01184-1 (2023).
Tran, H., Bui, T. K. & Pham, T. V. Virtual machine migration policy for multi-tier application in cloud computing based on Q-learning algorithm. Computing 104, 1285–1306. https://doi.org/10.1007/s00607-021-00992-0 (2022).
Qin, Y., Wang, H., Yi, S., Li, X. & Zhai, L. Virtual machine placement based on multi-objective reinforcement learning. Appl. Intell. 50, 2370–2383. https://doi.org/10.1007/s10489-019-01594-0 (2020).
Lin, W. et al. A hardware-aware CPU power measurement based on the power-exponent function model for cloud servers. Inf. Sci. 547, 1045–1065. https://doi.org/10.1016/j.ins.2020.09.040 (2021).
Mongia, V. EMaC: Dynamic VM consolidation framework for energy-efficiency and multi-metric SLA compliance in cloud data centers. SN Comput. Sci. 5, 643. https://doi.org/10.1007/s42979-024-02841-4 (2024).
Amahrouch, M., Bouhamidi, Y., Saadi & Kafhali, S. E. An efficient model based on machine learning algorithms for virtual machines classification in cloud computing environment, in Proc. 4th Int. Conf. Innov. Res. Appl. Sci., Eng. Technol. (IRASET), Fez, Morocco, May 2024, pp. 1–6. https://doi.org/10.1109/IRASET60493.2024.10510768(2024).
Srivastava & Kumar, N. An efficient firefly and honeybee based load balancing mechanism in cloud infrastructure. Clust Comput. 27, 2805–2827. https://doi.org/10.1007/s10586-024-04236-3 (2024).
Wang, J. et al. Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform. J. Cloud Comput. 11, 50. https://doi.org/10.1186/s13677-022-00362-1 (2022).
Pourghebleh, A. A., Anvigh, A. R., Ramtin & Mohammadi, B. The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments. Clust Comput. 24, 2673–2696. https://doi.org/10.1007/s10586-021-03282-w (2021).
Hayyolalam, V., Pourghebleh, B., Chehrehzad, M. R. & Kazem, A. A. P. Single-objective service composition methods in cloud manufacturing systems: Recent techniques, classification, and future trends. Concurr. Comput. Pract. Exp. 34, e6698. https://doi.org/10.1002/cpe.6698 (2022).
Shafiq, A., Jhanjhi, N. Z., Abdullah, A. & Alzain, M. A. A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access 9, 41731–41744. https://doi.org/10.1109/ACCESS.2021.3065303 (2021).
Zhang, W.-Z. et al. Secure and optimized load balancing for multitier IoT and edge-cloud computing systems. IEEE Internet Things J. 8, 8119–8132. https://doi.org/10.1109/JIOT.2020.3011723 (2020).
Saeik, F. et al. Task offloading in edge and cloud computing: A survey on mathematical, artificial intelligence and control theory solutions. Comput. Netw. 195, 108177. https://doi.org/10.1016/j.comnet.2021.108177 (2021).
Hayyolalam, V., Pourghebleh, B. & Kazem, A. A. P. Trust management of services (TMoS): Investigating the current mechanisms. Trans. Emerg. Telecommun Technol. 31, e4063. https://doi.org/10.1002/ett.4063 (2020).
Zhang, W., Chen, L., Luo, J. & Liu, J. A two-stage container management in the cloud for optimizing the load balancing and migration cost. Future Gener Comput. Syst. 135, 303–314. https://doi.org/10.1016/j.future.2022.05.008 (2022).
Tawfeeg, T. M. et al. Cloud dynamic load balancing and reactive fault tolerance techniques: A systematic literature review (SLR). IEEE Access. 10, 71853–71873. https://doi.org/10.1109/ACCESS.2022.3189300 (2022).
Kong, L., Mapetu, J. P. B. & Chen, Z. Heuristic load balancing based zero imbalance mechanism in cloud computing. J. Grid Comput. 18, 123–148. https://doi.org/10.1007/s10723-019-09497-y (2020).
Kumar, K. P2BED-C: A novel peer to peer load balancing and energy efficient technique for data-centers over cloud. Wirel. Pers. Commun. 123, 311–324. https://doi.org/10.1007/s11277-021-08992-0 (2022).
Liang, X., Dong, Y., Wang & Zhang, X. A low-power task scheduling algorithm for heterogeneous cloud computing. J. Supercomput. 76, 7290–7314. https://doi.org/10.1007/s11227-019-03076-3 (2020).
Ebrahim, M. & Hafid, A. Resilience and load balancing in fog networks: A multi-criteria decision analysis approach. Microprocess Microsyst. 101, 104893. https://doi.org/10.1016/j.micpro.2023.104893 (2023).
Kumar, Y., Kaul, S. & Hu, Y.-C. Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey. Sustain. Comput. Informatics Syst. 36, 100780. https://doi.org/10.1016/j.suscom.2022.100780 (2022).
Neelakantan, P. & Yadav, N. S. An optimized load balancing strategy for an enhancement of cloud computing environment. Wirel. Pers. Commun. 131, 1745–1765. https://doi.org/10.1007/s11277-023-10618-6 (2023).
Guo, X.-Q. et al. Edge–cloud co-evolutionary algorithms for distributed data-driven optimization problems. IEEE Trans. Cybern. 53, 6598–6611. https://doi.org/10.1109/TCYB.2021.3111102 (2022).
Kannan, K., Sunitha, G., Deepa, S., Babu, D. V. & Avanija, J. A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms. Comput. Electr. Eng. 102, 108225. https://doi.org/10.1016/j.compeleceng.2022.108225 (2022).
Sefati, S. S. et al. A Probabilistic Approach to Load Balancing in Multi-Cloud Environments via Machine Learning and Optimization Algorithms. J. Grid Comput. 23, 16 (2025).
Kim, D. Y., Lee, D. E., Kim, J. W. & Lee, H. S. Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud–Edge–Terminal IoT Networks Using Federated Reinforcement Learning. IEEE Internet Things J. 11, 10133 (2024).
Cheng, Z. et al. Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy. Future Gener. Comput. Syst. https://doi.org/10.1016/j.future.2024.06.035 (2024).
Li, X., Zhao, H. & Deng, W. IOFL: Intelligent-optimization-based federated learning for non-IID data. IEEE Internet Things J. https://doi.org/10.1109/jiot.2024.3354942 (2024).
Jiang, Q., Guo, Y., Yang, Z. & Zhou, X. A Parallel Whale Optimization Algorithm and Its Implementation on FPGA, Proceedings of the IEEE Congress on Evolutionary Computation (CEC), (2020).
Meng, Q., He, Y., Hussain, S., Lu, J. & Guerrero, J. M. Day-ahead economic dispatch of wind-integrated microgrids using coordinated energy storage and hybrid demand response strategies. Sci. Rep. 15(1), 26579. https://doi.org/10.1038/s41598-025-11561-2 (2025).
Zhang, B., Sang, H., Meng, L., Jiang, X. & Lu, C. Knowledge- and data-driven hybrid method for lot streaming scheduling in hybrid flowshop with dynamic order arrivals. Comput. Oper. Res. 184, 107244. https://doi.org/10.1016/j.cor.2025.107244 (2025).
Chen, P. et al. QoS-oriented Task Offloading in NOMA-based Multi-UAV Cooperative MEC Systems. IEEE Trans. Wireless Commun. https://doi.org/10.1109/TWC.2025.3593884 (2025).
Long, X., Chen, J., Yang, L. & Huang, H. An emergency scheduling method based on AutoML for space maneuver objective tracking. Expert Syst. Appl. 298, 129759. https://doi.org/10.1016/j.eswa.2025.129759 (2026).
Shao, S., Tian, Y., Zhang, Y. & Zhang, X. Knowledge learning-based dimensionality reduction for solving large-scale sparse multiobjective optimization problems. IEEE Trans. Cybernetics 55(7), 3471–3484. https://doi.org/10.1109/TCYB.2025.3558354 (2025).
Zhou, D. et al. Mission-driven resource scheduling in satellite-terrestrial networks: From perspective of collaboration and reconfiguration. IEEE Trans. Commun. 73(8), 6705–6719. https://doi.org/10.1109/TCOMM.2025.3529250 (2025).
Xu, W. et al. Blockchain-based verifiable decentralized identity for intelligent flexible manufacturing. IEEE Internet Things J. 12(16), 32366–32378. https://doi.org/10.1109/JIOT.2025.3576735 (2025).
Zhang, F. et al. Breaking the edge: Enabling efficient neural network inference on integrated edge devices. IEEE Trans. Cloud Comput. 13(2), 694–710. https://doi.org/10.1109/TCC.2025.3559346 (2025).
Contributed Indexing:
Keywords: Energy efficiency; Fault tolerance; Federated learning; Green cloud computing; Meta-heuristics; Multi-cloud scheduling; Quantum-inspired optimization; Resilience; Task placement; Whale optimization algorithm
Entry Date(s):
Date Created: 20260313 Latest Revision: 20260313
Update Code:
20260313
DOI:
10.1038/s41598-026-43125-3
PMID:
41820483
Database:
MEDLINE
*Further Information*
*Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: No participation of humans takes place in this implementation process.*