Optimization of Task Scheduling in Volunteer Grid Computing Using Reinforcement Learning Techniques
Keywords:
Volunteer Grid Computing, Reinforcement Learning, Q-Learning, Task Scheduling, Resource Optimization, Distributed SystemsAbstract
Volunteer Grid Computing (VGC) harnesses idle computational resources contributed by volunteers, offering a cost-effective and scalable platform for large-scale computing tasks. However, effective task scheduling remains a fundamental challenge due to the dynamic and unreliable nature of volunteer nodes. This paper explores the use of Reinforcement Learning (RL) to optimize task scheduling in VGC. A Q-learning-based scheduler was designed to adaptively assign tasks based on node reliability, availability, and performance history. Results from simulation demonstrate improved throughput and fault tolerance compared to traditional heuristics. This integration offers a promising pathway to autonomously and intelligently manage volunteer grids at scale.
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