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Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing
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DOI：10.1109/TVT.2021.3070723.png)
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Abstract
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This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as masters which offload their computations to edge devices acting as workers. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master; ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.
Keywords:
Task analysis
Stochastic processes
Distributed computing
Resilience
Servers
Security
Resource management
Auction theory
Bayesian methods
coded distributed computing
game theory
incomplete information
Internet of things
machine learning
Markov processes
mobile edge computing
resiliency
resource allocation and pricing
security
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