Artificial Intelligent Multi-Access Edge Computing Servers Management

Georgios Fragkos

doi:10.1109/ACCESS.2020.3025047

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Artificial Intelligent Multi-Access Edge Computing Servers Management

2020-01-01

Georgios Fragkos

Sean Lebien

Eirini Eleni Tsiropoulou *

DOI：10.1109/ACCESS.2020.3025047

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Abstract

En 中文

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.

Keywords:

Servers

Edge computing

Task analysis

Bayes methods

Quality of experience

Decision making

Quality of service

Multi-access edge computing (MEC)

artificial intelligence

decision making

Bayesian Truth Serum

Bayesian Learning Automata

Bayesian Belief

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