Intelligent Resource Allocation and Scheduling for Cloud Environments

Abstract

Cloud platforms often rely on reactive, threshold-based auto-scaling, which can lead to both over-provisioning (wasted cost) and under-provisioning (performance degradation) under dynamic workloads. We present a fully integrated framework that forecasts short-term resource demands using hybrid time-series models (LSTM neural networks + ARIMA) and drives proactive scaling decisions via a dual-stage optimizer combining Deep Q-Learning (DQN) and Genetic Algorithms (GA). Deployed on a local Kubernetes testbed, our solution achieves over 90 % forecasting accuracy (RMSE < 0.05), reduces operational cost by ~25 %, and improves average CPU utilization from 60 % to 85 %, while maintaining sub-200 ms scaling latencies. This hybrid approach also yields an estimated 15%energy savings by minimizing idle resources—demonstrating a practical path toward cost- and energy-efficient cloud resource management.

Introduction

Cloud computing relies on elastic resource allocation, but traditional auto-scaling approaches—used in systems like Kubernetes HPA and AWS Auto Scaling—react only after thresholds are exceeded. This leads to costly over-provisioning or performance degradation and SLA violations. Predictive methods can forecast demand but are rarely integrated with intelligent schedulers that jointly optimize cost, energy, and performance.

This research introduces an intelligent resource allocation framework combining hybrid workload forecasting and dual-stage optimization.

• A Hybrid ARIMA–LSTM model, trained on real and synthetic traces, achieves over 90% forecasting accuracy by leveraging ARIMA for linear trends and LSTM for nonlinear patterns.

• A scheduling optimizer pairs Deep Q-Network (DQN) for rapid short-term decisions with a Genetic Algorithm (GA) for multi-objective long-term policy refinement (cost, latency, energy).

• Deployment on Kubernetes/Minikube shows ~25% cost savings, 15% energy reduction, and sub-200 ms decision latency.

The literature review highlights gaps in existing work: reactive autoscalers suffer lag, standalone forecasting lacks integrated scheduling, and few studies test hybrid solutions in real container environments. ARIMA, LSTM, RL-based DQN, and GA methods each contribute strengths but have limitations individually.

The proposed system architecture integrates Prometheus-based metric collection, preprocessing, hybrid prediction, and intelligent scheduling within a fully containerized setup using Docker, Kubernetes, Flask APIs, and automated scaling via the Kubernetes Python client. During live execution, a closed feedback loop continuously forecasts workloads and adjusts pod counts accordingly.

Experimental evaluation confirms substantial performance gains: LSTM achieves RMSE 0.05 (92% accuracy) vs. ARIMA’s RMSE 0.12; CPU utilization improves from 60% to 85%, and scaling actions remain within 200 ms. However, limitations include restricted dataset diversity, limited model transferability, focus on CPU-only scaling, potential lag during micro-bursts, and approximate energy estimation.

Overall, the hybrid forecasting and optimization framework significantly enhances cloud auto-scaling efficiency compared to traditional reactive methods, offering a promising direction for intelligent, cost-effective, and energy-aware cloud resource management.

Conclusion

This work presents an end-to-end intelligent resource allocation framework that integrates hybrid time-series forecasting with DQN + GA–based scheduling to proactively scale cloud resources. Implemented on a Kubernetes testbed, the solution demonstrates high forecasting accuracy (>90%), ~25% reduction in operational cost, ~15% energy savings, and sub-200 ms scheduling latency—indicating strong practical potential for real-world cloud deployments. Looking ahead, several enhancements can further strengthen the system: 1) Multi-Cloud & Edge Integration: Extending orchestration across AWS, Azure, GCP, and distributed edge nodes would enable geographically adaptive and latency-optimized deployments. 2) Automated Hyperparameter Tuning: Bayesian optimization can be integrated to dynamically tune model hyperparameters, reducing manual configuration effort and increasing model robustness. 3) Container-Level Predictive Scheduling: Future iterations may leverage Kubernetes Operators for fine-grained per-pod scaling rather than deployment-level scaling[10]. 4) Energy-Aware SLAs: Incorporating constraints such as real-time energy cost fluctuations and carbon-intensity signals into the fitness objective could promote greener and sustainability-aligned cloud operations. Collectively, these future advancements can unlock a more autonomous, eco-efficient, and globally-adaptive cloud autoscaling ecosystem, building upon the strong foundation established by this work.

References

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Copyright

Copyright © 2025 Vaishali Patil, Palak Mundada, Shravi Magdum, Radha Kulkarni, Parth Shinge, Vivek Shirsath, Nrusinha Mane. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.