Optimal Workload Scheduling and Resource Provisioning in Hybrid Cloud Environments Using a Multi-Agent Reinforcement Learning Approach

Abstract

Hybrid cloud environments, which combine the advantages of both public and private clouds, have
gained significant popularity among organizations seeking to optimize their IT infrastructure.
However, the complex nature of hybrid clouds poses challenges in terms of workload scheduling
and resource provisioning, leading to suboptimal performance and increased costs. This research
paper presents a novel multi-agent reinforcement learning approach for optimal workload
scheduling and resource provisioning in hybrid cloud environments. The proposed framework
leverages the power of cooperative and competitive learning among multiple intelligent agents to
make informed decisions based on real-time system dynamics and historical data. The research
methodology involves the design and implementation of a decentralized multi-agent system, where
each agent represents a specific component of the hybrid cloud infrastructure, such as virtual
machines, storage units, and network resources. The agents employ advanced reinforcement
learning algorithms, such as deep Q-networks (DQN) and proximal policy optimization (PPO), to
learn optimal policies for workload scheduling and resource allocation. The agents collaborate and
compete with each other to maximize overall system performance while minimizing costs and
ensuring service level agreements are met. The proposed approach is evaluated through extensive
simulations and real-world case studies, demonstrating significant improvements in resource
utilization, response time, and cost-efficiency compared to traditional rule-based and heuristic
methods. The study presents a detailed analysis of the convergence properties and scalability of the
multi-agent reinforcement learning framework, highlighting its ability to adapt to dynamic
workload patterns and varying resource constraints. The findings of this research have significant
implications for organizations adopting hybrid cloud environments. By leveraging the power of
multi-agent reinforcement learning, the proposed framework enables automated and intelligent
decision-making for workload scheduling and resource provisioning, leading to optimized
performance and reduced operational costs. This research contributes to the advancement of
autonomous and self-adaptive cloud computing systems, paving the way for more efficient and
intelligent management of hybrid cloud infrastructures. [