[1] Rana, N., Jeribi, F., Khan, Z., Alrawagfeh, W., Ben Dhaou, I., Haseebuddin, M., & Uddin, M. (2024). A systematic literature review on contemporary and future trends in virtual machine scheduling techniques in cloud and multi-access computing. Frontiers in Computer Science, 6, 1288552. https://doi.org/10.3389/fcomp.2024.1288552
[2] Sun, W. (2020). An optimal resource allocation scheme for virtual machine placement. AIMS Mathematics, 5(3), 2290-2306. https://doi.org/10.3934/math.2020256
[3] Abdel-Basset, M., Mohamed, R., Abouhawwash, M., Chakrabortty, R. K., & Ryan, M. J. (2021). EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis. Expert systems with applications, 173, 114699. https://doi.org/10.1016/j.eswa.2021.114699
[4] Barbalho, H., Kovaleski, P., Li, B., Marshall, L., Molinaro, M., Pan, A. & Menache, I. (2023). Virtual machine allocation with lifetime predictions. Proceedings of Machine Learning and Systems, 5, 232-253.
[5] Gu, H., Wang, J., Yu, J., Wang, D., Li, B., He, X., & Yin, X. (2023). Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform. PeerJ Computer Science, 9, e1675. https://doi.org/10.7717/peerj-cs.1675
[6] Li, J., & Yang, C. (2023). Hybrid machine learning and optimization approach for resource allocation in edge computing. Journal of Cloud Computing: Theory and Applications, 12(1), 28-45.
[7] Kumar, H., Garg, A., Gupta, A., & Agarwal, Y. (2025). A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management. arXiv preprint arXiv:2511.16075.
[8] Oyekanmi, E. O., Oladoja, I. P., & Omotehinwa, T. O. (2021). Improved RASA for load balancing in cloud computing environments. Asian Journal of Research in Computer Science, 12(4), 52-66. https://doi.org/10.9734/ajrcos/2021/v12i430294
[9] Singh, J., & Walia, N. K. (2023). A comprehensive review of cloud computing virtual machine consolidation. IEEE Access, 11, 106190-106209. https://doi.org/10.1109/ACCESS.2023.3314613
[10] Hosseini, H., & Schierreich, Š. (2025). The Algorithmic Landscape of Fair and Efficient Distribution of Delivery Orders in the Gig Economy. arXiv preprint arXiv:2503.16002.
[11] Kumar, S., Goswami, A., Gupta, R., Singh, S. P., & Lay-Ekuakille, A. (2022). A cost-effective and QoS-aware user allocation approach for edge computing enabled IoT. IEEE Internet of Things Journal, 10(2), 1696-1710. https://doi.org/10.1109/JIOT.2022.3210835
[12] Hamzeh, H. (2021). Fairness for resource allocation in cloud computing (Doctoral dissertation, Bournemouth University).
[13] Li, S., Huang, J., & Cheng, B. (2021). Resource pricing and demand allocation for revenue maximization in IaaS clouds: A market-oriented approach. IEEE Transactions on Network and Service Management, 18(3), 3460-3475. https://doi.org/10.1109/TNSM.2021.3085519
[14] Salami, B., Noori, H., & Naghibzadeh, M. (2020). Fairness-aware energy efficient scheduling on heterogeneous multi-core processors. IEEE Transactions on Computers, 70(1), 72-82. https://doi.org/10.1109/TC.2020.2984607
[15] Raeisi-Varzaneh, M., Dakkak, O., Fazea, Y., & Kaosar, M. G. (2024). Advanced cost-aware Max-Min workflow tasks allocation and scheduling in cloud computing systems. Cluster computing, 27(9), 13407-13419. https://doi.org/10.1007/s10586-024-04594-1
[16] Zhang, X., Li, J., Li, G., & Li, W. (2022). Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources. Cluster Computing, 25(5), 3389-3403. https://doi.org/10.1007/s10586-022-03548-9
[17] Tao, M., Hao, F., Wei, L., Zhi, H., Kuznetsov, S. O., & Min, G. (2024). Fairness-aware maximal cliques identification in attributed social networks with concept-cognitive learning. IEEE Transactions on Computational Social Systems. https://doi.org/10.1109/TCSS.2024.3445721
[18] Buyya, R., Yeo, C. S., & Venugopal, S. (2008). Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. In 2008 10th IEEE international conference on high performance computing and communications (pp. 5-13). Ieee. https://doi.org/10.1109/HPCC.2008.172
[19] Islam, S., Keung, J., Lee, K., & Liu, A. (2012). Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems, 28(1), 155-162. https://doi.org/10.1016/j.future.2011.05.027
[20] Singh, A. R., Sujatha, M. S., Kadu, A. D., Bajaj, M., Addis, H., & Sarada, K. (2025). A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems. Scientific Reports, 15.
https://doi.org/10.1038/s41598-025-02649-w
[21] Singhal, S., Gupta, N., Berwal, P., Naveed, Q. N., Lasisi, A., & Wodajo, A. W. (2023). Energy efficient resource allocation in cloud environment using metaheuristic algorithm. Ieee Access, 11, 126135-126146. https://doi.org/10.1109/ACCESS.2023.3330434
[22] Feng, H., Deng, Y., Zhou, Y., & Min, G. (2021). Towards heat-recirculation-aware virtual machine placement in data centers. IEEE Transactions on Network and Service Management, 19(1), 256-270. https://doi.org/10.1109/TNSM.2021.3120295
[23] Hormozi, E., Hu, S., Ding, Z., Tian, Y. C., Wang, Y. G., Yu, Z. G., & Zhang, W. (2022). Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation. Energy, 252, 123884. https://doi.org/10.1016/j.energy.2022.123884
[24] Mukhopadhyay, N., & Tewari, B. P. (2024). Performance Analysis of a Virtualized Cloud Data Center through Efficient VM Migrations. In Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing (pp. 357-365). https://doi.org/10.1145/3675888.3676071
[25] Maiyza, A. I., Hassan, H. A., Sheta, W. M., Banawan, K., & Korany, N. O. (2025). VTGAN based proactive VM consolidation in cloud data centers using value and trend approaches. Scientific Reports, 15(1), 20133. https://doi.org/10.1038/s41598-025-04757-z
[26] Çağlar, İ., & Altılar, D. T. (2022). Look-ahead energy efficient VM allocation approach for data centers. Journal of Cloud Computing, 11(1), 11. https://doi.org/10.1186/s13677-022-00281-x
[27] 27--Ramamurthy, A., Pantula, P. D., Gharote, M. S., Mitra, K., & Lodha, S. (2021). Multi-objective Optimization for Virtual Machine Allocation in Computational Scientific Workflow under Uncertainty. In CLOSER (pp. 240-247). https://doi.org/10.5220/0010453302400247
[28] Alsadie, D. (2021). A metaheuristic framework for dynamic virtual machine allocation with optimized task scheduling in cloud data centers. IEEE Access, 9, 74218-74233. https://doi.org/10.1109/ACCESS.2021.3077901
[29] Alboaneen, D., Tianfield, H., Zhang, Y., & Pranggono, B. (2021). A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers. Future Generation Computer Systems, 115, 201-212. https://doi.org/10.1016/j.future.2020.08.036
[30] Singh, H., Tyagi, S., Kumar, P., Gill, S. S., & Buyya, R. (2021). Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions. Simulation Modelling Practice and Theory, 111, 102353. https://doi.org/10.1016/j.simpat.2021.102353
[31] Panwar, S. S., Rauthan, M. M. S., & Barthwal, V. (2022). A systematic review on effective energy utilization management strategies in cloud data centers. Journal of Cloud Computing, 11(1), 95. https://doi.org/10.1186/s13677-022-00368-5
[32] Ishola, O. M., & Oyedele, O. O. (2022). Fairness-aware resource allocation in cloud computing. Journal of Computer Science and Its Applications, 9(2), 1-8.
[33] Cohen, M. C., Miao, S., & Wang, Y. (2025). Dynamic Pricing with Fairness Constraints. Operational Research, 73, 3027-3043.
https://doi.org/10.1287/opre.2023.0123
[34] Wang, W., Li, B., & Liang, B. (2014). Dominant resource fairness in cloud computing systems with heterogeneous servers. In IEEE INFOCOM 2014-IEEE Conference on Computer Communications (pp. 583-591). IEEE. https://doi.org/10.1109/INFOCOM.2014.6847983
