Sequential Offloading for Distributed DNN Computation in Multiuser MEC Systems

doi:10.1109/JIOT.2023.3279271

2023-10-15

王丰 (Feng Wang)

Songfu Cai *

Vincent K. N. Lau

DOI：10.1109/JIOT.2023.3279271

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摘要

En 中文

This article studies a sequential task offloading problem for a multiuser mobile-edge computing (MEC) system. While most of the existing works consider static one-shot offloading optimization with fixed wireless channel conditions and fixed computational tasks, we consider a dynamic optimization approach, which embraces wireless channel fluctuations and random deep neural network (DNN) task arrivals over an infinite horizon. Specifically, we introduce a local CPU workload queue (WD-QSI) and a MEC server workload queue (MEC-QSI) to model the dynamic workload of DNN tasks at each wireless device (WD) and the MEC server, respectively. The transmit power and the partitioning of the local DNN task at each WD are dynamically determined based on the instantaneous channel conditions (to capture the transmission opportunities) and the instantaneous WD-QSI and MEC-QSI (to capture the dynamic urgency of the tasks) to minimize the average latency of the DNN tasks. The joint optimization can be formulated as an ergodic Markov decision process (MDP), in which the optimality condition is characterized by a centralized Bellman equation. However, the brute force solution of the MDP is not viable due to the curse of dimensionality as well as the requirement for knowledge of the global state information. To overcome these issues, we first decompose the MDP into multiple lower dimensional sub-MDPs, each of which can be associated with a WD or the MEC server. Next, we further develop a parametric online $Q$ -learning algorithm, so that each sub-MDP is solved locally at its associated WD or the MEC server. The proposed solution is completely decentralized in the sense that the transmit power for sequential offloading and the DNN task partitioning can be determined based on the local channel state information (CSI) and the local WD-QSI at the WD only. Additionally, no prior knowledge of the distribution of the DNN task arrivals or the channel statistics will be needed for the MEC server. The proposed solution can achieve the superb performance over various state-of-the-art baselines.

Keyword:

Computation offloading

deep neural network (DNN) inferencing

infinite-horizon Markov decision process (MDP)

mobile-edge computing (MEC)

parametric Q-learning