Task scheduling for mobile edge computing using genetic algorithm and conflict graphs Articles uri icon

publication date

  • August 2020

start page

  • 8805

end page

  • 8819

issue

  • 8

volume

  • 69

International Standard Serial Number (ISSN)

  • 0018-9545

Electronic International Standard Serial Number (EISSN)

  • 1939-9359

abstract

  • In this paper, we consider parallel and sequential task offloading to multiple mobile edge computing servers. The task consists of a set of inter-dependent sub-tasks, which are scheduled to servers to minimize both offloading latency and failure probability. Two algorithms are proposed to solve the scheduling problem, which are based on genetic algorithm and conflict graph models, respectively. Simulation results show that these algorithms provide performance close to the optimal solution, which is obtained through exhaustive search. Furthermore, although parallel offloading uses orthogonal channels, results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading. On the other hand, parallel offloading provides less latency. However, as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases.

keywords

  • conflict graphs; genetic algorithms; mobile edge computing; parallel offloading; sequential offloading