TY - JOUR
T1 - Task scheduling for mobile edge computing using genetic algorithm and conflict graphs
AU - Al-Habob, Ahmed A.
AU - Dobre, Octavia A.
AU - Armada, Ana Garcia
AU - Muhaidat, Sami
N1 - Funding Information:
Manuscript received April 13, 2020; revised May 6, 2020; accepted May 7, 2020. Date of publication May 15, 2020; date of current version August 13, 2020. This work was supported in part by the Memorial University Chair, in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) through its Discovery program, in part by the Chair of Excellence at UC3M, and in part by the Spanish National Project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE). The review of this article was coordinated by Dr. K. Bian. (Corresponding author: Octavia Dobre.) Ahmed A. Al-Habob and Octavia A. Dobre are with the Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1C 5S7, Canada (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - 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-T]asks increases, the latency gap between parallel and sequential schemes decreases.
AB - 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-T]asks increases, the latency gap between parallel and sequential schemes decreases.
KW - Conflict graphs
KW - genetic algorithms
KW - mobile edge computing
KW - parallel offloading
KW - sequential offloading
UR - http://www.scopus.com/inward/record.url?scp=85090151876&partnerID=8YFLogxK
U2 - 10.1109/TVT.2020.2995146
DO - 10.1109/TVT.2020.2995146
M3 - Article
AN - SCOPUS:85090151876
SN - 0018-9545
VL - 69
SP - 8805
EP - 8819
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 8
M1 - 9094341
ER -