🤖 AI Summary
Dynamic workloads in cloud environments often lead to resource over-provisioning, creating a challenging trade-off between cost and response latency. This work proposes a novel approach that integrates LSTM-based predictive auto-scaling with a game-theoretic heuristic for task scheduling, uniquely unifying time-series load forecasting and game-driven real-time decision-making within a single framework. The method achieves substantial reductions in resource costs while maintaining response times comparable to those of conventional heuristic algorithms, matching the performance of purely machine learning–based solutions. By synergistically combining predictive analytics with strategic scheduling, the proposed approach simultaneously optimizes both cost efficiency and service quality, offering a practical and effective solution for dynamic cloud resource management.
📝 Abstract
Cloud computing allows scalable resource provisioning, but dynamic workload changes often lead to higher costs due to over-provisioning. Machine learning (ML) approaches, such as Long Short-Term Memory (LSTM) networks, are effective for predicting workload patterns at a higher level, but they can introduce delays during sudden traffic spikes. In contrast, mathematical heuristics like Game Theory provide fast and reliable scheduling decisions, but they do not account for future workload changes. To address this trade-off, this paper proposes a hybrid orchestration framework that combines LSTM-based predictive scaling with heuristic task allocation. The results show that this approach reduces infrastructure costs close to ML-based models while maintaining fast response times similar to heuristic methods. This work presents a practical approach for improving cost efficiency in cloud resource management.