On Joint Revenue Maximization, Resource Allocation, and Task Offloading for Crowdsourcing-Like Edge Computing

Edge computing has become a key technology to enable various Internet of Things (IoT) applications. We envision that in the future, edge computing systems will be platforms like crowdsourcing websites where IoT application providers can offload their tasks to the platform and pay it via the edge computing services, and the platform needs to seek appropriate edge servers (ESs) for executing tasks. To ensure ESs participate and provide good performance, i.e., to meet the stringent QoS requirement of IoT applications, the platform needs to properly reward them for computing services. In this paper, we consider the scenario where both the platform and ESs are self-interested entities, and focus on the setting that the payment made by application providers is ex-ante. We formulate a joint revenue maximization, resource allocation and task offloading optimization problem, and tackle it with a Stackelberg game formulation. A centralized algorithm based on Bayesian Optimization is proposed to solve the game. Moreover, we consider a practical setting where the platform is unable to collect full information when application providers and ESs may refuse to provide their sensitive information. To this end, we develop a decentralized solution based on neural network optimization together with a privacy-preserving information exchange protocol. We evaluate both mechanisms through numerical studies, and results indicate they are effective as compared to representative baselines.