Reverse Auction-Based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing

Abstract

This paper proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile Cloud-Edge computing. The basic idea is that the Cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint Mobile Devices (MDs). In RACORAM, the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. The original problem is decomposed into an equivalent master problem and subproblem, and low-complexity algorithms are proposed to solve the related optimization problems. Specifically, a Constrained Gradient Descent Allocation Method (CGDAM) is first proposed to determine the computation resource allocation strategy, and then a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Meanwhile, the CSC’s payment determination for the winning edge server owners is also presented. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC’s cost under different scenarios.