Journal
IEEE-ACM TRANSACTIONS ON NETWORKING
Volume 28, Issue 3, Pages 1405-1418Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNET.2020.2983119
Keywords
Servers; Resource management; Task analysis; Edge computing; Optimization; Uncertainty; Cloud computing; Data offloading; Multi-access Edge Computing; computation and communication overhead; risk-based behavior; probabilistic uncertainty; utility functions; convex optimization
Categories
Funding
- NSF CRII Award [1849739]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1849739] Funding Source: National Science Foundation
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Multi-access Edge Computing (MEC) has emerged as a flexible and cost-effective paradigm, enabling resource constrained mobile devices to offload, either partially or completely, computationally intensive tasks to a set of servers at the edge of the network. Given that the shared nature of the servers' resources introduces high computation and communication uncertainty, in this paper we consider users' risk-seeking or loss-aversion behavior in their final decisions regarding the portion of their computing tasks to be offloaded at each server in a multi-MEC server environment, while executing the rest locally. This is achieved by capitalizing on the power and principles of Prospect Theory and Tragedy of the Commons, treating each MEC server as a Common Pool of Resources available to all the users, while being rivarlous and subtractable, thus may potentially fail if over-exploited by the users. The goal of each user becomes to maximize its perceived satisfaction, as expressed through a properly formulated prospect-theoretic utility function, by offloading portion of its computing tasks to the different MEC servers. To address this problem and conclude to the optimal allocation strategy, a non-cooperative game among the users is formulated and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal data offloading, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation, while useful insights about the users' data offloading decisions under realistic conditions and behaviors are presented.
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