Opportunistic Coded Distributed Computing: An Evolutionary Game Approach

Han Yu, Dusit Niyato, Cyril Leung, Dong In Kim

Abstract: Task offloading has been proposed and studied to overcome the problem of energy and computation constrained terminals. Computationally intensive tasks are often parallelable, and therefore the execution time can be further improved via a coded distributed computing (CDC) approach, as CDC offers robustness against stragglers by introducing redundant computational tasks. In this paper, we study a user-centric task offloading problem, in which the edge performs the of-floaded computation with CDC. Furthermore, the extent of the straggler's effect on servers is also unknown to the user. This requires users to explore server and code settings of the CDC, and “opportunistically” select the best combo to maximize the utility. For simplicity, we refer to this scenario as opportunistic coded distributed computing. We formulate the problem as an evolutionary game in which each user is self-interested. The payoff is calculated based on the monetary cost of CDC-as-a-Service and total delay, weighted by user-defined parameter values. For the game solution, an evolutionary stable equilibrium (ESS) is used, i.e., probabilistic joint selection of server and code configuration. To obtain the ESS, we present an iterative algorithm based on the revision protocol. A theoretical analysis of equilibrium in terms of existence, uniqueness, stationarity, and stability is provided. Numerical simulations are conducted to support the theoretical findings and the adaption of equilibrium states to the hyper-parameters.