Adaptive Resource Allocation in Cloud Computing (ARAC)

Abstract

This paper presents Adaptive Resource Allocation in Cloud Computing (ARAC), a novel approach to cloud management that leverages reinforcement learning for dynamic resource allocation. Traditional cloud platforms often rely on manual configuration and pre-defined rules, leading to suboptimal resource utilization and potentially degraded user experience. ARAC addresses this limitation by employing a reinforcement learning-based resource scheduling algorithm. This algorithm continuously learns and adapts to changing conditions, optimizing the allocation of virtual machines, storage, and bandwidth based on user requests, resource utilization rates, and system load. The core claim of ARAC is to design a cloud platform capable of automatically adjusting resource allocations in response to evolving demands. The system's mechanism involves a dynamic adjustment of resources, aiming for optimal utilization and a superior user experience. This paper outlines the architecture, the reinforcement learning framework, and the key components of ARAC, demonstrating its potential to significantly improve cloud computing efficiency and responsiveness. ---