TL;DR
A structured taxonomy is presented that classifies algorithms into traditional, heuristic, meta-heuristic, and modern learning-based approaches, with a particular emphasis on the increasing adoption of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) for dynamic and adaptive scheduling.
The proliferation of connected devices and latency-sensitive applications in mobile edge computing (MEC) and fifth-generation (5G) and beyond (B5G) networks has made intelligent load balancing a cornerstone of next-generation distributed systems. Efficient workload distribution across edge servers is essential for mini...
A deep reinforcement learning framework for intelligent cloud resource allocation that jointly optimizes resource utilization, Service Level Agreement compliance, infrastructure cost, and energy efficiency, and adapts to workload distribution shifts within 200 episodes without manual retuning is presented.
The fast evolution of cloud environments has created more dynamic, diverse, and latency-sensitive workloads that require greater sophistication in resource management. Traditional, reactive heuristic scheduling methods tend to overlook shifting workloads, resulting in poor resource management, higher energy consumption...
HRL-TaskOpt is presented, a novel Hierarchical Reinforcement Learning-based task scheduling framework that combines high-level global task offloading with millisecond-granularity local scheduling policies in an edge–cloud scenario and offers a scalable and intelligent solution for adaptive task scheduling.
A hybrid model, O-MCTSALP, which is optimized to schedule tasks and balance their loads in cloud computing systems and has the lowest makespan of all the workloads, is presented.
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