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. Index Terms-Mobile-edge computing, task offloading scheduling, power control, flow shop scheduling, convex optimization.
The simulation results show that task offloading scheduling ensures less delay than conventional techniques REF .
Joint Task Offloading Scheduling and Transmit Power Allocation for Mobile-Edge Computing Systems
2017 IEEE Wireless Communications and Networking Conference (WCNC)
2017 IEEE Wireless Communications and Networking Conference (WCNC)
Mathematics Computer Science