In recent years, edge computing has emerged as an effective solution to extend cloud computing and satisfy the demand of applications for low latency. However, with today's explosion of innovative applications (e.g., augmented reality, natural language processing, virtual reality), processing services for mobile and smart devices have become computation-intensive, consisting of multiple interconnected computations. This coupled with the need for delay-sensitivity and high quality of service put massive pressure on edge servers. Meanwhile, tasks invoking these services may involve similar inputs that could lead to the same output. In this paper, we present CoxNet, an efficient computation reuse architecture for edge computing. CoxNet enables edge servers to reuse previous computations while scheduling dependent incoming computations. We provide an analytical model for computation reuse joined with dependent task offloading and design a novel computing offloading scheduling scheme. We also evaluate the efficiency and effectiveness of CoxNet via synthetic and real-world datasets. Our results show that CoxNet is able to reduce the task execution time up to 66% based on a synthetic dataset and up to 50% based on a real-world dataset.
Keywords: Computation Reuse; Edge Computing; Serverless Computing; Service Offloading.