Artificial Intelligent Multi-Access Edge Computing Servers Management
Artificial Intelligent Multi-Access Edge Computing Servers Management
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DOI：
10.1109/access.2020.3025047
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
G. Fragkos;Sean Lebien;E. Tsiropoulou
中科院分区：
文献类型：
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作者：
G. Fragkos;Sean Lebien;E. Tsiropoulou
多接入边缘计算（MEC）的进步为MEC服务器作为智能实体融入物联网（IoT）环境以及5G无线接入网络铺平了道路。本文采用强化学习（RL）和贝叶斯推理的原理，提出了一种新的基于人工智能的MEC服务器激活机制。考虑的问题使MEC服务器的激活决策，旨在提高整个MEC系统的声誉，并考虑总计算成本，以有效地服务于用户的计算需求，同时保证他们的体验质量（QoE）先决条件的满足。每个MEC服务器利用贝叶斯学习自动机（BLA）理论自主决定是激活还是保持睡眠模式。基于贝叶斯真值血清（BTS）的概念，还引入了基于人为驱动的同行评审的边缘计算系统所提供服务的评估，该评估支持关于MEC服务器所提供服务的声誉机制的开发。智能MEC服务器的自主决策满意度是通过整体效用函数捕获的，它们的目标是以分布式的方式最大化。最后，通过建模和仿真得到了详细的数值结果，突出了该框架的关键操作特点和优越性。
The advances of multi-access edge computing (MEC) have paved the way for the integration of the MEC servers, as intelligent entities into the Internet of Things (IoT) environment as well as into the 5G radio access networks. In this paper, a novel artificial intelligence-based MEC servers’ activation mechanism is proposed, by adopting the principles of Reinforcement Learning (RL) and Bayesian Reasoning. The considered problem enables the MEC servers’ activation decision-making, aiming at enhancing the reputation of the overall MEC system, as well as considering the total computing costs to serve efficiently the users’ computing demands, guaranteeing at the same time their Quality of Experience (QoE) prerequisites satisfaction. Each MEC server decides in an autonomous manner whether it will be activated or remain in sleep mode by utilizing the theory of Bayesian Learning Automata (BLA). A human-driven peer-review-based evaluation of the edge computing system’s provided services is also introduced based on the concept of Bayesian Truth Serum (BTS), which supports the development of a reputation mechanism regarding the MEC servers’ provided services. The intelligent MEC servers’ autonomous decisions’ satisfaction is captured via a holistic utility function, which they aim to maximize in a distributed manner. Finally, detailed numerical results obtained via modeling and simulation, highlight the key operation features and superiority of the proposed framework.