Self-Adaptive Multi-Cloud Job Scheduling Using Deep Reinforcement Learning and Workload Forecasting
By: Ashutosh Bhatt, Ambika Aggarwal and Khushboo Jain
References
- Y. Gu, Z. Liu, S. Dai, C. Liu, Y. Wang, S. Wang, G. Theodoropoulos, L. Cheng, “Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review”, arXiv:2501.01007, 2025, Available at:
https://arxiv.org/abs/2501.01007 (Accessed on date: 17 October 2026). - A. Naik, K. Sooda. “Efficient Job Scheduling in Cloud Environments using Reinforcement Learning Actor-Critic Models”, Engineering, Technology & Applied Science Research, 14 (5), pp. 16559–16564, 2024.
- X. Yu, J. Mi, L. Tang, L. Long, X. Qin. “Dynamic Multi-Objective Task Scheduling in Cloud Computing using Reinforcement Learning for Energy and Cost Optimization”, Scientific Reports, 2025.
- J. Pan, Y. Wei. “A Deep Reinforcement Learning-Based Scheduling Framework for Real-Time Workflows in the Cloud Environment”, Expert Systems with Applications, 255, p. 124845, 2024.
- S. Mangalampalli, G.R. Karri, M.V. Ratnamani, S.N. Mohanty, B.A. Jabr, Y.A. Ali, S. Ali, B.S. Abdullaeva. “Efficient Deep Reinforcement Learning Based Task Scheduler in Multi Cloud Environment”, Scientific Reports, 14 (1), p. 21850, 2024.
- J.K. Park, W.K. Jung, E.S. Suh. “Human-AI Dynamic Resource Reallocation for Disruption-Prone Engineering Workflows”, Journal of Computing and Information Science in Engineering, pp. 1–54, 2026.
- S. Chandrasiri, D. Meedeniya. “Energy-Efficient Dynamic Workflow Scheduling in Cloud Environments using Deep Learning”, Sensors, 25 (5), p. 1428, 2025.
- J. Dogani, F. Khunjush, M.R. Mahmoudi, M. Seydali. “Multivariate Workload and Resource Prediction in Cloud Computing using CNN and GRU by Attention Mechanism”, The Journal of Supercomputing, 79 (3), pp. 3437–3470, 2023.
- G. Cui, T. Hu, W. Zhang, H. Bao. “MMTransformer: A Multivariate Time-Series Resource Forecasting Model for Multi-Component Applications”, Scientific Reports, 15 (1), p. 29740, 2025.
- A. Rossi, A. Visentin, D. Carraro, S. Prestwich, K.N. Brown. “Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Domain Generalization”, 2024.
- S. Simaiya, U.K. Lilhore, Y.K. Sharma, K.B. Rao, V.V.R. Maheswara Rao, A. Baliyan, A. Bijalwan, R. Alroobaea. “A Hybrid Cloud Load Balancing and Host Utilization Prediction Method using Deep Learning and Optimization Techniques”, Scientific Reports, 14 (1), p. 1337, 2024.
- A. Aggarwal, P. Dimri, A. Agarwal, A. Bhatt. “Self Adaptive Fruit Fly Algorithm for Multiple Workflow Scheduling in Cloud Computing Environment”, Kybernetes, 50 (6), pp. 1704–1730, 2021. doi:
https://doi.org/10.1108/K-11-2019-0757 . - J. Chen, H. Ye, F. Jiang, X. He, T. Zhang, J. Chen, X. Gao, “Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services”, arXiv:2507.12908, 2025, Available at:
https://arxiv.org/abs/2507.12908 (Accessed on date: 17 October 2026). - K. Jain, M. Gupta, A. Abraham. “A Review on Privacy and Security Assessment of Cloud Computing”, Journal of Information Assurance and Security, 16, pp. 161–168, 2021.
- B. Everman, N. Rajendran, X. Li, Z. Zong. “Improving the Cost Efficiency of Large-Scale Cloud Systems Running Hybrid Workloads – A Case Study of Alibaba Cluster Traces”, Sustainable Computing: Informatics and Systems, 30, p. 100528, 2021.
- H. Mao, M. Alizadeh, I. Menache, S. Kandula. “Resource Management with Deep Reinforcement Learning”. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks (HotNets), pp. 50–56, 2016.
- H. Mao, M. Schwarzkopf, S.B. Venkatakrishnan, Z. Meng, M. Alizadeh. “Learning Scheduling Algorithms for Data Processing Clusters”. In Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM), pp. 270–288, 2019.
- M. Jaderberg, W.M. Czarnecki, I. Dunning, L. Marris, G. Lever, A.G. Castaneda, C. Beattie, N.C. Rabinowitz, A.S. Morcos, A. Ruderman, N. Sonnerat, et al. “Human-Level Performance in 3D Multiplayer Games with Population-Based Reinforcement Learning”, Science, 364 (6443), pp. 859–865, 2019.
- V. Mnih, A.P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, K. Kavukcuoglu. “Asynchronous Methods for Deep Reinforcement Learning”. In Proceedings of the 33rd International Conference on Machine Learning (ICML), PMLR 48, pp. 1928–1937, 2016.
- J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, “Proximal Policy Optimization Algorithms”, arXiv:1707.06347, 2017, Available at:
https://arxiv.org/abs/1707.06347 (Accessed on date: 17 October 2026). - W. Zhang, H. Ou. “Reinforcement Learning Based Multi-Objective Task Scheduling for Energy Efficient and Cost Effective Cloud Edge Computing”, Scientific Reports, 15 (1), p. 41716, 2025.
- J. Wang, S. Li, X. Zhang, F. Wu, C. Xie. “Deep Reinforcement Learning Task Scheduling Method Based on Server Real-Time Performance”, PeerJ Computer Science, 10, p. e2120, 2024.
- A.K. Sharma, J. Kunkel. “Grapheon RL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems”. In Proceedings of the 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 489–494, 2025.
- S.A.M. Mostafa, A. Mohan, J. Wang, “SLA-MORL: SLA-Aware Multi-Objective Reinforcement Learning for HPC Resource Optimization”, arXiv:2508.03509, 2025, Available at:
https://arxiv.org/abs/2508.03509 (Accessed on date: 17 October 2026). - G.E.P. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung. Time Series Analysis: Forecasting and Control, 5th ed., John Wiley & Sons, Hoboken, NJ, 2015.
- S. Hochreiter, J. Schmidhuber. “Long Short-Term Memory”, Neural Computation, 9 (8), pp. 1735–1780, 1997.
- K. Cho, B. Van Merriënboer, Ç. Gulçehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio. “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation”. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734, 2014.
- A. Aggarwal, K. Jain, A. Bhatt. “A Hybrid Approach to Deepfake Detection Using Vision Transformers with Contrastive Learning”. In Proceedings of the 2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–6, 2025.
- P.B. Guruge, Y.H.P.P. Priyadarshana. “Time Series Forecasting-Based Kubernetes Autoscaling Using Facebook Prophet and Long Short-Term Memory”, Frontiers in Computer Science, 7, p. 1509165, 2025.
- Z. Ahamed, M. Khemakhem, F. Eassa, F. Alsolami, A. Basuhail, K. Jambi. “Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments”, Sensors, 23 (15), p. 6911, 2023.
- H. Shingne, D. Ghusse, C. Dadiyala, R. Welekar. “Federated Deep Learning-Driven Decentralized and Cost-Aware Cloud Resource Management for Load Balancing and SLA Optimizations”, Discover Computing, 29 (1), p. 105, 2026.
- K. Jain, A. Aggarwal. “Edge-Cloud Hybrid Task Scheduling Using Federated Reinforcement Learning and Adaptive Swarm Intelligence”, Journal of Information Assurance and Security, 20 (4), p. 198, 2025.
- A.Y. Ng, D. Harada, S. Russell. “Policy Invariance under Reward Transformations: Theory and Application to Reward Shaping”. In Proceedings of the 16th International Conference on Machine Learning (ICML), pp. 278–287, 1999.
- D.N. Ng’ang’a, W.K. Cheruiyot, D. Njagi, “A Machine Learning Framework for Predicting Failures in Cloud Data Centers – A Case of Google Cluster, Azure Clouds and Alibaba Clouds”, SSRN 4404569, 2024, Available at:
https://ssrn.com/abstract=4404569 (Accessed on date: 17 October 2026). - J. Schulman, P. Moritz, S. Levine, M. Jordan, P. Abbeel, “High-Dimensional Continuous Control Using Generalized Advantage Estimation”, arXiv:1506.02438, 2015, Available at:
https://arxiv.org/abs/1506.02438 (Accessed on date: 17 October 2026). - L. Ruan, Y. Bai, S. Li, J. Lv, T. Zhang, L. Xiao, H. Fang, C. Wang, Y. Xue. “Cloud Workload Turning Points Prediction via Cloud Feature-Enhanced Deep Learning”, IEEE Transactions on Cloud Computing, 11 (2), pp. 1719–1732, 2022.
- M. Nashaat, W. Moussa, R. Rizk, W. Saber. “Dynamic Machine Learning Approach for Workload Prediction in Cloud Environments”, Scientific Reports, 16 (1), p. 10983, 2026.
- J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, A. Bouchachia. “A Survey on Concept Drift Adaptation”, ACM Computing Surveys, 46 (4), pp. 1–37, 2014.
- C. Reiss, A. Tumanov, G.R. Ganger, R.H. Katz, M.A. Kozuch. “Heterogeneity and Dynamicity of Clouds at Scale: Google Trace Analysis”. In Proceedings of the Third ACM Symposium on Cloud Computing (SoCC), pp. 1–13, 2012.
- A. Verma, L. Pedrosa, M. Korupolu, D. Oppenheimer, E. Tune, J. Wilkes. “Large-Scale Cluster Management at Google with Borg”. In Proceedings of the Tenth European Conference on Computer Systems (EuroSys), pp. 1–17, 2015.
- H. Chawla, S. Fallon, E. Fallon. “Energy-Aware Job Profiling and Mediation for Cloud and 6G Orchestration”. In Proceedings of the 2025 4th International Conference on 6G Networking (6GNet), pp. 121–125, 2025.
- Y. Cheng, Z. Chai, A. Anwar. “Characterizing Co-located Datacenter Workloads: An Alibaba Case Study”. In Proceedings of the 9th Asia-Pacific Workshop on Systems (APSys), pp. 1–3, 2018.
- M. Xie. “Towards Efficient, Predictable, and Scalable Ephemeral Storage Systems”, PhD Thesis, University of California, Santa Cruz, 2025.
- R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F. De Rose, R. Buyya. “CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms”, Software: Practice and Experience, 41 (1), pp. 23–50, 2011.
- F.X. Diebold, R.S. Mariano. “Comparing Predictive Accuracy”, Journal of Business & Economic Statistics, 13 (3), pp. 253–263, 1995.
- Q. Weng, W. Xiao, Y. Yu, W. Wang, C. Wang, J. He, Y. Li, L. Zhang, W. Lin, Y. Ding. “MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters”. In Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI), pp. 945–960, 2022.
- R.S. Sutton, A.G. Barto. Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
- A. Bhatt, P. Dimri, A. Aggarwal. “Self-Adaptive Brainstorming for Jobshop Scheduling in Multicloud Environment”, Software: Practice and Experience, 50, pp. 1381–1398, 2020. doi:
https://doi.org/10.1002/spe.2819 . - H. Topcuoglu, S. Hariri, M.Y. Wu. “Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing”, IEEE Transactions on Parallel and Distributed Systems, 13 (3), pp. 260–274, 2002.
- Y. Bao, Y. Peng, C. Wu. “Deep Learning-Based Job Placement in Distributed Machine Learning Clusters”. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM), pp. 489–513, 2019.
Language: English
Page range: 501 - 526
Published on: Oct 27, 2026
Published by: Cerebration Science Publishing Co., Limited
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
Related subjects:
© 2026 Ashutosh Bhatt, Ambika Aggarwal, Khushboo Jain, published by Cerebration Science Publishing Co., Limited
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.