ERP: Edge Resource Pooling for Data Stream Mobile Computing

Recently, the explosion of resource-hungry and delay-sensitive Internet-of-Things (IoT) applications as exemplified by wearable appliances, video surveillance, and connected vehicles have posed great challenges on the underlying IoT devices which typically have limited computation resource. In response, computation offloading is envisioned as a promising approach to augmenting capability of IoT devices. Toward real-time and efficient computation offloading, in this paper we propose a novel edge resource pooling framework, in which a massive crowd of devices at the network edge exploit device-to-device (D2D) collaboration for pooling and sharing computation resource with each other. Specifically, we first formulate the utility maximization problem under both computation and communication constraints as a mixed-integer linear programming problem, which is further proven to be NP-hard. To address this challenge, we propose a greedy heuristic based on the classical maximum network flow problem, and thus to schedule the task offloading in a cost-efficient manner. By considering the case that a centralized controller (e.g., a network operator) is not available, a decentralized task offloading scheme is further proposed, in which IoT devices communicate and determine D2D offloading strategy locally. Rigorous theoretical analysis and extensive evaluations demonstrate the effectiveness of the proposed algorithms.