Problem
Research questions and friction points this paper is trying to address.
Throughput Maximization
MapReduce
Energy-Harvesting
Wireless Devices
Resource Allocation
Throughput Maximization for MapReduce-Based Collaborative Computing over Energy-Harvesting Wireless Devices
🤖 AI Summary
研究通过DDPG-CVX算法优化基于MapReduce的异构无线设备计算任务分配，以最大化平均吞吐量，同时考虑能量收集和能耗约束。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
DDPG-CVX
Deep Deterministic Policy Gradient
convex programming
energy harvesting
resource allocation
🔎 Similar Papers
No similar papers found.
