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arXiv · 2608.25549

Throughput Maximization for MapReduce-Based Collaborative Computing over Energy-Harvesting Wireless Devices

Yuhang Li·Siqi Sun·Hongen Zheng·Xiaojing Chen·Shunqing Zhang·Yanzan Sun
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Abstract

This paper studies resource allocation for MapReduce-based collaborative computing over heterogeneous wireless devices powered by renewable energy harvesting. We formulate a long-run average throughput maximization problem that jointly optimizes computing load, phase time allocations, transmit power, and per-device energy consumption, subject to battery evolution, CPU frequency, and latency constraints. To solve this problem online without prior knowledge of channel states or energy arrivals, we propose a DDPG-CVX algorithm that couples Deep Deterministic Policy Gradient (DDPG) with convex programming. DDPG determines the per-slot energy budget for each device from observed battery and channel states; the remaining resource allocation variables are then resolved to global optimality by an embedded convex solver. This two-phase decomposition reduces the action-space dimensionality of DDPG while preserving per-slot solution quality. Simulations show that DDPG-CVX achieves 1.25$\times$$\sim$32.36$\times$ the throughput of representative benchmarks.

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Yuhang Li, Siqi Sun, Hongen Zheng, Xiaojing Chen, Shunqing Zhang, Yanzan Sun. 2026-08-26. Throughput Maximization for MapReduce-Based Collaborative Computing over Energy-Harvesting Wireless Devices. https://arxiv.org/abs/2608.25549

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