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This paper addresses the challenge of optimizing resource allocation for MapReduce-based collaborative computing on heterogeneous wireless devices that rely on renewable energy harvesting. By formulating a long-run average throughput maximization problem, the authors develop the DDPG-CVX algorithm, which integrates Deep Deterministic Policy Gradient (DDPG) with convex programming to efficiently allocate computing loads, phase time, transmit power, and energy consumption. The results demonstrate that DDPG-CVX significantly outperforms existing benchmarks, achieving throughput improvements ranging from 1.25 to 32.36 times.
DDPG-CVX achieves up to 32 times higher throughput for energy-harvesting wireless devices, revolutionizing collaborative computing efficiency.
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.
