A Mobility-Aware Cross-edge Computation Offloading Framework for Partitionable Applications

Mobile Edge Computing has already become a new paradigm to reduce the latency in data transmission for resource-limited mobile devices by offloading computation tasks onto edge servers. However, for mobility-aware computation-intensive services, existing offloading strategies cannot handle the offloading procedure properly because of the lack of collaboration among edge servers. A data stream application is partitionable if it can be presented by a directed acyclic dataflow graph, which makes cross-edge collaboration possible. In this paper, we propose a cross-edge computation offloading (CCO) framework for partitionable applications. The transmission, execution and coordination cost, as well as the penalty for task failure, are considered. An online algorithm based on Lyapunov optimization is proposed to jointly determine edge site-selection and energy harvesting without priori knowledge. By stabilizing the battery energy level of each mobile device around a positive constant, the proposed algorithm can obtain asymptotic optimality. Theoretical analysis about the complexity and the effectiveness of the proposed framework is provided. Experimental results based on a real-life dataset corroborate that CCO can achieve superior performance compared with benchmarks where cross-edge collaboration is not allowed.