Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach
Efficient task scheduling in large-scale distributed systems presents significant challenges due to dynamic workloads, heterogeneous resources, and competing quality-of-service requirements. Traditional centralized approaches face scalability limitations and single points of failure, while classical heuristics lack adaptability to changing conditions. This paper proposes a decentralized multi-agent deep reinforcement learning (DRL-MADRL) framework for task scheduling in heterogeneous distributed systems. We formulate the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and develop a lightweight actor-critic architecture implemented using only NumPy, enab...
| Authors | Daniel Benniah John |
|---|---|
| Primary category | cs.DC |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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