TY - GEN
T1 - Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems
AU - Mao, Yuyi
AU - Zhang, Jun
AU - Letaief, Khaled B.
PY - 2017/5/10
Y1 - 2017/5/10
N2 - Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.
AB - Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.
KW - Convex optimization
KW - Flow shop scheduling
KW - Mobile-edge computing
KW - Power control
KW - Task offloading scheduling
UR - http://www.scopus.com/inward/record.url?scp=85019740134&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2017.7925615
DO - 10.1109/WCNC.2017.7925615
M3 - Conference article published in proceeding or book
AN - SCOPUS:85019740134
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
Y2 - 19 March 2017 through 22 March 2017
ER -