TY - GEN
T1 - Optimal Task Offloading and Resource Allocation in Software-Defined Vehicular Edge Computing
AU - Choo, Sukjin
AU - Kim, Joonwoo
AU - Pack, Sangheon
N1 - Funding Information:
ACKNOWLEDGMENT This research was supported in part by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIP) (No. 2017R1E1A1A01073742) and in part by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2018-2017-0-01633) supervised by the IITP(Institute for Information & communications Technology Promotion).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/16
Y1 - 2018/11/16
N2 - In vehicular edge computing (VEC), resource-intensive tasks are offloaded to computing nodes at the network edge. Owing to high mobility and distributed nature, optimal task offloading in vehicular environments is still a challenging problem. In this paper, we first introduce a software-defined vehicular edge computing (SD-VEC) architecture where a controller not only guides the vehicles' task offloading strategy but also determines the edge cloud resource allocation strategy. To obtain the optimal strategies, we formulate a problem on the edge cloud selection and resource allocation to maximize the probability that a task is successfully completed within a pre-specified time limit. Since the formulated problem is a well-known NP-hard problem, we devise a mobility-aware greedy algorithm (MGA) that determines the amount of edge cloud resources allocated to each vehicle. Trace-driven simulation results demonstrate that MGA provides near-optimal performance and improves the successful task execution probability compared with conventional algorithms.
AB - In vehicular edge computing (VEC), resource-intensive tasks are offloaded to computing nodes at the network edge. Owing to high mobility and distributed nature, optimal task offloading in vehicular environments is still a challenging problem. In this paper, we first introduce a software-defined vehicular edge computing (SD-VEC) architecture where a controller not only guides the vehicles' task offloading strategy but also determines the edge cloud resource allocation strategy. To obtain the optimal strategies, we formulate a problem on the edge cloud selection and resource allocation to maximize the probability that a task is successfully completed within a pre-specified time limit. Since the formulated problem is a well-known NP-hard problem, we devise a mobility-aware greedy algorithm (MGA) that determines the amount of edge cloud resources allocated to each vehicle. Trace-driven simulation results demonstrate that MGA provides near-optimal performance and improves the successful task execution probability compared with conventional algorithms.
KW - software-defined network (SDN)
KW - task offloading
KW - vehicular edge computing (VEC)
UR - http://www.scopus.com/inward/record.url?scp=85059460722&partnerID=8YFLogxK
U2 - 10.1109/ICTC.2018.8539726
DO - 10.1109/ICTC.2018.8539726
M3 - Conference contribution
AN - SCOPUS:85059460722
T3 - 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018
SP - 251
EP - 256
BT - 9th International Conference on Information and Communication Technology Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information and Communication Technology Convergence, ICTC 2018
Y2 - 17 October 2018 through 19 October 2018
ER -