期刊
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEM.2022.3166769
关键词
Internet of Things; Task analysis; Edge computing; Dispatching; Costs; Servers; Training; Actor-critic (AC); deep reinforcement learning; edge computing; edge federation; Edge-AI; latency; service provisioning
资金
- Ministry of Education, Taiwan, under the project Trusted Intelligent Edge/Fog computing Technology RSC [107RSA0021]
- Ministry of Economic Affairs (MOEA) [106-EC-17-A-24-0619]
- National Natural Science Foundation of China [61872084]
- Guangdong-HongKong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology [2020B1212030010]
This article discusses the critical role of edge computing in the IoT environment and proposes the concept of the edge federation. It introduces an intelligent reinforcement learning-based request service provisioning system and shows that it outperforms baseline approaches in terms of profit and response latency in the edge computing context.
Edge computing plays a critical role in the Internet of Things (IoT) environment as it potentially supports the time-critical IoT applications' resources as well as latency requirements. In the near future, most edge service providers are envisioned to receive revenue from deploying these applications with the expenditures proportional to placing the offloaded requests from IoT devices and allocating the required resources. One way to maximize the edge profit and minimize the response latency is to integrate the edge nodes and form the edge federation. Therefore, edge service providers can have interoperability to distribute the IoT requests on the appropriate edge nodes in the light of providing satisfactory service levels to meet their objectives. Since the edge nodes are volatile and IoT time-critical applications are increasing, the edge nodes are envisioned to face massive traffic from IoT devices. Therefore, exploiting the traditional dynamic requests placement approaches cannot meet the SLA requirement of both IoT devices and edge service providers. In this article, we designed an intelligent reinforcement learning-based request service provisioning system (i.e., here, we call Edge-AI) as part of a smart edge orchestrator in the edge federation. We implement the proposed method, which is called DRL-Dispatcher, and compare it with greedy and random algorithms in edge federation. The experimental results show that the proposed DRL-Dispatcher performs better in terms of profit and low response latency as compared with the baseline approaches.
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