TY - JOUR
T1 - Partial offloading strategy for mobile edge computing considering mixed overhead of time and energy
AU - Tang, Qiang
AU - Lyu, Haimei
AU - Han, Guangjie
AU - Wang, Jin
AU - Wang, Kezhi
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program, No. 2017YFE0125300 and the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U1801264, the Jiangsu Key Research and Development Program, No. BE2019648, in part by the Open fund of State Key Laboratory of Acoustics under Grant SKLA201901, in part by the National Natural Science Foundation of China (Grant Nos. 61772087, 61303043), in part by the Outstanding Youth Project of Hunan Province Education Department (Grant No. 18B162), and in part by the “Double First-class” International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology (Grant No. 2018IC23).
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Mobile edge computing (MEC) utilizes wireless access network to provide powerful computing resources for mobile users to improve the user experience, which mainly includes two aspects: time and energy consumption. Time refers to the latency consumed to process user tasks, while energy consumption refers to the total energy consumed in processing tasks. In this paper, the time and energy consumption in user experience are weighted as a mixed overhead and then optimized jointly. We formulate a mixed overhead of time and energy (MOTE) minimization problem, which is a nonlinear programming problem. In order to solve this problem, the block coordinate descent method to deal with each variable step by step is adopted. We further analyze the minimum value of delay parameters in the model, and examine two special cases: 1-offloading and 0-offloading. In 1-offloading, all the task data is offloaded to MEC server, and no data offloaded in 0-offloading. The necessary and sufficient conditions for the existence of two special cases are also deduced. Besides, the multi-user situation is also discussed. In the performance evaluation, we compare MOTE with other offloading schemes, such as exhaustive strategy and Monte Carlo simulation method-based strategy to evaluate the optimality. The simulation results show that MOTE always achieves the minimal overhead compared to other algorithms.
AB - Mobile edge computing (MEC) utilizes wireless access network to provide powerful computing resources for mobile users to improve the user experience, which mainly includes two aspects: time and energy consumption. Time refers to the latency consumed to process user tasks, while energy consumption refers to the total energy consumed in processing tasks. In this paper, the time and energy consumption in user experience are weighted as a mixed overhead and then optimized jointly. We formulate a mixed overhead of time and energy (MOTE) minimization problem, which is a nonlinear programming problem. In order to solve this problem, the block coordinate descent method to deal with each variable step by step is adopted. We further analyze the minimum value of delay parameters in the model, and examine two special cases: 1-offloading and 0-offloading. In 1-offloading, all the task data is offloaded to MEC server, and no data offloaded in 0-offloading. The necessary and sufficient conditions for the existence of two special cases are also deduced. Besides, the multi-user situation is also discussed. In the performance evaluation, we compare MOTE with other offloading schemes, such as exhaustive strategy and Monte Carlo simulation method-based strategy to evaluate the optimality. The simulation results show that MOTE always achieves the minimal overhead compared to other algorithms.
KW - Full granularity
KW - Partial offloading
KW - Mixed overhead
KW - Mobile edge computing
UR - http://www.scopus.com/inward/record.url?scp=85070335905&partnerID=8YFLogxK
U2 - 10.1007/s00521-019-04401-8
DO - 10.1007/s00521-019-04401-8
M3 - Article
SN - 0941-0643
VL - 32
SP - 15383
EP - 15397
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 19
ER -