基于能量收集无线设备的MapReduce协同计算吞吐量最大化
Throughput Maximization for MapReduce-Based Collaborative Computing over Energy-Harvesting Wireless Devices
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中文总结 AI 辅助
针对能量收集无线设备的MapReduce协同计算，本文提出DDPG-CVX算法，联合优化资源分配，仿真显示其吞吐量较基准提升1.25至32.36倍。
中文摘要 AI 辅助
本文研究由可再生能量收集供电的异构无线设备上基于MapReduce的协同计算的资源分配问题。我们构建了一个长期平均吞吐量最大化问题，联合优化计算负载、阶段时间分配、发射功率和单设备能耗，约束条件包括电池演化、CPU频率和延迟限制。为在无需信道状态或能量到达先验知识的情况下在线求解该问题，我们提出了DDPG-CVX算法，将深度确定性策略梯度（DDPG）与凸规划相结合：DDPG根据观测到的电池和信道状态确定每个设备每时隙的能量预算，剩余资源分配变量则通过嵌入的凸求解器求解至全局最优。这种两阶段分解降低了DDPG的动作空间维度，同时保留了每时隙的解质量。仿真结果显示，DDPG-CVX算法的吞吐量达到代表性基准的1.25倍至32.36倍。
英文摘要
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.
