Decentralized Task Scheduling in Distributed Systems: A Lightweight Multi-Agent Deep Reinforcement Learning Approach With Gossip-Based Consensus

Abstract

Efficient task scheduling in large-scale distributed systems remains challenging because workloads are dynamic, resources are heterogeneous, and centralized schedulers introduce coordination bottlenecks. This paper presents DRL-MADRL, a lightweight decentralized multi-agent deep reinforcement learning scheduler for heterogeneous cloud-edge systems. Each agent maintains a per-node utilization-estimate vector updated by randomized pairwise gossip, enabling decentralized execution with O(1) peer contacts per update and O(N) payload per gossip event. The scheduler combines a NumPy actor-critic policy, priority-capacity assignment scoring, gossip-augmented observations, optional contention-adaptive score blending, and workload-feedback reward shaping. We formulate scheduling as a Decentralized Partially Observable Markov Decision Process and evaluate the method on a 100-node heterogeneous system with 1,000 tasks per episode across three contention regimes. Against seven baselines, including Random, Weighted Round-Robin, Priority-Min-Min, Priority-Capacity Heuristic, PPO, MADDPG, and MAPPO, DRL-MADRL is competitive with the strongest heuristic under low contention and achieves the best SLA satisfaction at moderate and high contention. At moderate contention, DRL-MADRL achieves 19.16 seconds average task completion time (ATCT) and 90.60 percent SLA satisfaction, improving SLA by 2.73 percentage points over PPO and 2.98 percentage points over PCH. The NumPy implementation requires approximately 80 KB per agent and sub-10 ms inference latency.