Edge-AI: IoT Request Service Provisioning in Federated Edge Computing Using Actor-Critic Reinforcement Learning

边缘计算 计算机科学 边缘设备 GSM演进的增强数据速率 计算机网络 强化学习 供应 服务提供商 服务器 分布式计算 服务(商务) 计算机安全 云计算 人工智能 操作系统 经济 经济
作者
Hojjat Baghban,Amir Rezapour,Kuan‐Ching Li,Sirapop Nuannimnoi,ChingYao Huang
出处
期刊:IEEE Transactions on Engineering Management [Institute of Electrical and Electronics Engineers]
卷期号:: 1-10 被引量:10
标识
DOI:10.1109/tem.2022.3166769
摘要

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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