期刊
IEEE TRANSACTIONS ON SERVICES COMPUTING
卷 15, 期 2, 页码 640-655出版社
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2021.3116597
关键词
Servers; Delays; Computational modeling; Cloud computing; Costs; Energy consumption; Collaboration; Mobile edge computing; cloud computing; QoS; computation offloading; DNN partition
资金
- National Natural Science Foundation of China [62071327, 61801325]
- Research and Innovation Project for Postgraduates in Tianjin [2020YJSZXS27]
This research focuses on optimizing the offloading of large-scale DNN models in a local-edge-cloud collaborative environment with limited resources, proposing a new algorithm that efficiently achieves DNN offloading strategies while reducing latency, energy consumption, and cost.
With the rapid development of the Internet of Things (IoT) and communication technology, Deep Neural Network (DNN) applications like computer vision, can now be widely used in IoT devices. However, due to the insufficient memory, low computing capacity, and low battery capacity of IoT devices, it is difficult to support the high-efficiency DNN inference and meet users' requirements for Quality of Service (QoS). Worse still, offloading failures may occur during the massive DNN data transmission due to the intermittent wireless connectivity between IoT devices and the cloud. In order to fill this gap, we consider the partitioning and offloading of the DNN model, and design a novel optimization method for parallel offloading of large-scale DNN models in a local-edge-cloud collaborative environment with limited resources. Combined with the coupling coordination degree and node balance degree, an improved Double Dueling Prioritized deep Q-Network (DDPQN) algorithm is proposed to obtain the DNN offloading strategy. Compared with existing algorithms, the DDPQN algorithm can obtain an efficient DNN offloading strategy with low delay, low energy consumption, and low cost under the premise of ensuring delay-energy-cost coordination and reasonable allocation of computing resources in a local-edge-cloud collaborative environment.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据