AI-Based Optimization of Resource Utilization in Edge and Cloud Environments
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V1I6P101Keywords:
Edge Computing, Cloud Computing, Resource Utilization, Autoscaling, Workload Forecasting, Reinforcement Learning, Bayesian Optimization, Digital Twins, Multi-Objective Optimization, Federated Learning, Energy-Aware Scheduling, SLA ComplianceAbstract
This paper presents an AI-driven framework for optimizing resource utilization across heterogeneous edge–cloud infrastructures while meeting latency, cost, and energy objectives. The approach combines short-horizon workload forecasting with multi-objective decision policies that coordinate container placement, autoscaling, and dataflow routing. We fuse sequence models for demand prediction with Bayesian optimization to tune policy knobs online, and a safe reinforcement learning agent to select actions under service-level constraints. A lightweight digital twin provides fast counterfactual evaluations to gate risky actions and accelerate policy updates. The framework integrates with Kubernetes and serverless runtimes, supports hardware heterogeneity (CPU, GPU, TEE), and leverages federated learning to preserve data locality and privacy at the edge. We evaluate the system on microservices, streaming analytics, and ML inference pipelines under diurnal, bursty, and failure-injected regimes. Results show consistent improvements in tail latency at comparable or lower cost, higher packing efficiency without SLA regressions, and measurable energy savings via power-aware placement and elastic right-sizing. Ablations highlight the importance of uncertainty-aware forecasts and safety constraints for robust operation under workload drift. The design is compatible with existing observability stacks and policy engines, enabling incremental adoption. We conclude with deployment guidelines and discuss limitations in highly volatile, multi-tenant settings, outlining directions for adaptive, regulation-aware resource governance
References
[1] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv preprint. https://arxiv.org/abs/1707.06347
[2] Achiam, J., Held, D., Tamar, A., & Abbeel, P. (2017). Constrained Policy Optimization. arXiv preprint. https://arxiv.org/abs/1705.10528
[3] García, J., & Fernández, F. (2015). A Comprehensive Survey on Safe Reinforcement Learning. Journal of Machine Learning Research. https://arxiv.org/abs/1801.08757
[4] Bai, S., Kolter, J. Z., & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (TCN). arXiv preprint. https://arxiv.org/abs/1803.01271
[5] Taylor, S. J., & Letham, B. (2018). Forecasting at Scale (Prophet). The American Statistician. https://arxiv.org/abs/1701.07813
[6] Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. arXiv preprint. https://arxiv.org/abs/1206.2944
[7] Brochu, E., Cora, V. M., & de Freitas, N. (2010). A Tutorial on Bayesian Optimization of Expensive Cost Functions. arXiv preprint. https://arxiv.org/abs/1012.2599
[8] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data (Federated Learning). arXiv preprint. https://arxiv.org/abs/1602.05629
[9] Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018). Split Learning for Health: Distributed Deep Learning without Sharing Raw Patient Data. arXiv preprint. https://arxiv.org/abs/1812.00564
[10] Mirhoseini, A., et al. (2017). Device Placement Optimization with Reinforcement Learning. arXiv preprint. https://arxiv.org/abs/1706.04972
[11] Harchol-Balter, M. (2013). Performance Modeling and Design of Computer Systems: Queueing Theory in Action. Book site. https://www.cs.cmu.edu/~harchol/PerformanceModeling/book.html
[12] Barroso, L. A., & Hölzle, U. (2007). The Case for Energy-Proportional Computing. IEEE Computer. https://static.googleusercontent.com/media/research.google.com/en//archive/energy_proportional_computing.pdf
[13] Koenker, R., & Hallock, K. F. (2001). Quantile Regression. Journal of Economic Perspectives (open copy). https://www.econ.uiuc.edu/~roger/research/ker/chapter2.pdf
