Deep Reinforcement Learning for Cloud Job Scheduling and Resource Management
A survey of how Deep Reinforcement Learning is applied to job scheduling and resource management in cloud computing. Because cloud workloads and resource availability fluctuate unpredictably, static heuristic and meta-heuristic schedulers adapt poorly; DRL learns adaptive policies from continuous environment observation. The review frames scheduling as a Markov decision process (state, action, reward design), organizes DRL algorithms by methodology (value-based, policy-gradient, actor-critic, multi-agent), compares them on metrics such as makespan, latency, cost, energy, utilization, and fairness across cloud and edge settings, and surveys emerging trends and open problems.
Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An
An algorithm-level survey of Deep Reinforcement Learning (DRL) methods for job scheduling and resource management in cloud computing. It motivates the problem: cloud environments are dynamic and hete…