Auction-and-Learning Based Lagrange Coded Computing Model for Privacy-Preserving, Secure, and Resilient Mobile Edge Computing

被引:16
作者
Asheralieva, Alia [1 ]
Niyato, Dusit [2 ]
Xiong, Zehui [3 ]
机构
[1] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China
[2] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
[3] Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore 487372, Singapore
基金
中国国家自然科学基金; 新加坡国家研究基金会;
关键词
Task analysis; Distributed computing; Computational modeling; Security; Resilience; Bayes methods; Privacy; Auction theory; Bayesian methods; coded distributed computing; data privacy; deep learning; game theory; incomplete information; Internet of Things; machine learning; Markov processes; mobile edge computing; pricing; reinforcement learning; resiliency; resource allocation; security; COALITION-FORMATION; GAME;
D O I
10.1109/TMC.2021.3097380
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as "masters" offloading their computations to edge nodes acting as "workers". A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers; ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them; ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.
引用
收藏
页码:744 / 764
页数:21
相关论文
共 45 条
[1]   Mobile Edge Computing: A Survey [J].
Abbas, Nasir ;
Zhang, Yan ;
Taherkordi, Amir ;
Skeie, Tor .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (01) :450-465
[2]   Belief and truth in hypothesised behaviours [J].
Albrecht, Stefano V. ;
Crandall, Jacob W. ;
Ramamoorthy, Subramanian .
ARTIFICIAL INTELLIGENCE, 2016, 235 :63-94
[3]  
[Anonymous], 2016, 3GPP TS 36.300 V14.0.0
[4]  
[Anonymous], OPNET SIM DEV TOOL
[5]   Optimal Computational Offloading and Content Caching in Wireless Heterogeneous Mobile Edge Computing Systems With Hopfield Neural Networks [J].
Asheralieva, Alia .
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2021, 5 (03) :407-425
[6]   Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks [J].
Asheralieva, Alia ;
Niyato, Dusit .
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2021, 20 (03) :1092-1109
[7]   Distributed Dynamic Resource Management and Pricing in the IoT Systems With Blockchain-as-a-Service and UAV-Enabled Mobile Edge Computing [J].
Asheralieva, Alia ;
Niyato, Dusit .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (03) :1974-1993
[8]   Hierarchical Game-Theoretic and Reinforcement Learning Framework for Computational Offloading in UAV-Enabled Mobile Edge Computing Networks With Multiple Service Providers [J].
Asheralieva, Alia ;
Niyato, Dusit .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (05) :8753-8769
[9]   An Asymmetric Evolutionary Bayesian Coalition Formation Game for Distributed Resource Sharing in a Multi-Cell Device-to-Device Enabled Cellular Network [J].
Asheralieva, Alia ;
Quek, Tony Q. S. ;
Niyato, Dusit .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2018, 17 (06) :3752-3767
[10]   Bayesian Reinforcement Learning-Based Coalition Formation for Distributed Resource Sharing by Device-to-Device Users in Heterogeneous Cellular Networks [J].
Asheralieva, Alia .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2017, 16 (08) :5016-5032