TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in HetNet
Long Chen, Jigang Wu, Xin-Xiang Zhang, Gangqiang Zhou · IEEE Transactions on Services Computing · 2018
In heterogeneous cellular network, task scheduling for computation offloading is one of the biggest challenges. Most works focus on alleviating heavy burden of macro base stations by moving the computation tasks on macro cell user equipment (MUE) to remote cloud or small cell base stations. But the selfishness of network users is seldom considered. Motivated by the multiple access mobile edge computing, this paper provides incentive for task transfer from macro cell users to small cell base stations. The proposed incentive scheme utilizes small cell user equipments to provide relay services. The problem of computation offloading is modeled as a two-stage auction, in which the remote MUEs with common social character can form a group and then buy the computation resource of small cell base stations with the relaying of small cell user equipment. A two-stage auction scheme named TARCO is contributed to maximize utilities for both sellers and buyers in the network. The truthfulness, individual rational and budget balance properties of TARCO are also proved in this paper. In addition, two algorithms are proposed to further refine TARCO on the social welfare of the network. One can achieve higher utility of MUEs and the other can obtain higher total social welfare. Extensive simulation results demonstrate that, TARCO is better than random algorithm by 104.90 percent in terms of average utility of MUEs, while the performance of TARCO is further improved up to 28.75 percent and 17.06 percent by the proposed two algorithms, respectively.