Truthful mechanisms for partial and full allocation in a multi-mapping multi-tasking allocation system in mobile edge computing

In this paper, we address the multi-mapping multi-task allocation and pricing problem in mobile edge computing (MEC). Based on different allocation scenarios, we propose partial and full allocation models. The partial model enables an MD’s tasks to be partially offloaded with the MD bidding for a single task to be performed on the server (one bid per task). The full model offloads all or none of the MD’s tasks with the MD bidding for all of them together. We consider a multi-mapping multi-tasking model with platform constraints to enable mobile devices to offload tasks to multiple servers that meet the platform and version requirements. This paper aims to solve the social welfare maximization problem which is the sum of MDs’ valuations. We propose the greedy mechanisms for the partial and full allocation models, respectively. Selfish MDs have an incentive to manipulate the bidding system by making untrue bids to obtain a greater allocation. Therefore, we demonstrate that our proposed mechanisms achieve truthfulness, which drives the system into an equilibrium in which no MD has incentives to maximize utility by untruthfully declaring valuations. Moreover, we show the proposed two mechanisms achieve the desired properties, including computation efficiency and individual rationality. Experiment results show that the proposed mechanisms obtain near-optimal solutions in a reasonable amount of time.