A Scalable Machine Learning Strategy for Resource Allocation Database

Efficiently responding to dynamic application demands in cloud environments is crucial for meeting service level agreements (SLAs) and optimizing resource costs. Traditional auto-scaling approaches often struggle with predefined rules, making it challenging to devise optimal adaptation strategies. This paper introduces a proactive strategy that leverages the robust capabilities of long short-term memory (LSTM) for precise request prediction, complemented by the intelligent decision-making power of multi-agent reinforcement learning (MARL) to determine optimal actions for scaling virtual machines. In this proposed methodology, the LSTM accurately predicts the number of requests in the next time step, effectively adapting to dynamic traffic changes. The integration of MARL enhances the adaptability and efficiency of the auto-scaling process by enabling virtual machines to make informed decisions based on real time states. This study asserts that applying MARL as a fundamental component of the auto-scaling strategy is a promising and effective solution. The synergy between LSTM and MARL based Ape-X not only enhances predictive accuracy but also empowers virtual machines to make proactive decisions, making it a valuable approach for meeting SLAs and optimizing resource utilization in dynamic cloud environments.