NDLI: Congestion-aware adaptive decentralised computation offloading and caching for multi-access edge computing networks

Content Provider	IET Digital Library
Author	Tefera, Getenet She, Kun Chen, Min Ahmed, Awais
Abstract	Multi-access edge computing (MEC) has attracted much more attention to revolutionising smart communication technologies and Internet of Everything. Nowadays, smart end-user devices are designed to execute sophisticated applications that demand more resources and explosively connected to the global ecosystem. As a result, the backhaul network traffic congestion grows enormously and user quality of experience is compromised as well. To address these challenges, the authors proposed congestion-aware adaptive decentralised computing, caching, and communication framework which can orchestrate the dynamic network environment based on deep reinforcement learning for MEC networks. MEC is a paradigm shift that transforms cloud services and capabilities platform at the edge of ubiquitous radio access networks in close proximity to mobile subscribers. The framework can evolve to perform augmented decision-making capabilities for the upcoming network generation. Hence, the problem is formulated using non-cooperative game theory which is nondeterministic polynomial (NP) hard to solve and the authors show that the game admits a Nash equilibrium. In addition, they have constructed a decentralised adaptive scheduling algorithm to leverage the utility of each smart end-user device. Therefore, their methodical observations using theoretical analysis and simulation results substantiate that the proposed algorithm can achieve ultra-low latency, enhanced storage capability, low energy consumption, and scalable than the baseline scheme.
Starting Page	3410
Ending Page	3419
Page Count	10
ISSN	17518628
Volume Number	14
e-ISSN	17518636
Issue Number	Issue 19, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/14/19
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2020.0630
Journal	IET Communications
Publisher Date	2020-09-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Scheduling Augmented Decision-making Capabilities Backhaul Network Traffic Congestion Cloud Computing Cloud Services Communication Framework Computational Complexity Computer Communication Computer Network And Technique Congestion-aware Adaptive Decentralised Computation Offloading Decentralised Adaptive Scheduling Algorithm Decision Making Deep Reinforcement Learning Distributed Processing Dynamic Network Environment Energy Consumption Game Theory Global Ecosystem Internet of Everything Internet of Things Knowledge Engineering Technique Learning in AI MEC Network Mobile Computing Mobile Radio System Mobile Subscribers Multi-access System Multiaccess Edge Computing Network Multiple Access Communication Nash Equilibrium Network Generation Noncooperative Game Theory NP Hard Problem Optimisation Optimisation Technique Pervasive Computing Quality of Experience Radio Access Network Radio Access System Smart Communication Technology Smart End-user Device Telecommunication Scheduling Telecommunication Traffic Ubiquitous Ubiquitous Radio Access Network Ultra-low Latency
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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