计算机科学
计算卸载
强化学习
服务器
分布式计算
能源消耗
移动边缘计算
边缘计算
GSM演进的增强数据速率
计算机网络
人工智能
生态学
生物
作者
Xinhan Wang,Huanlai Xing,Fuhong Song,Shouxi Luo,Penglin Dai,Bowen Zhao
出处
期刊:IEEE Transactions on Parallel and Distributed Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-08-01
卷期号:34 (8): 2479-2497
标识
DOI:10.1109/tpds.2023.3287633
摘要
Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This article studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.
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