期刊
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
卷 5, 期 2, 页码 765-777出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGCN.2021.3071497
关键词
Task analysis; Cloud computing; Servers; Streaming media; Edge computing; Automobiles; Image segmentation; Edge computing; service offloading; computation reuse; serverless computing
资金
- National Institutes of Health [NIGMS/P20GM109090]
- National Science Foundation [CNS-2016714]
- Nebraska University Collaboration Initiative
Edge computing has emerged as an effective solution to extend cloud computing and meet the demand for low latency applications. CoxNet is an efficient computation reuse architecture that significantly reduces task execution time. Research results demonstrate that CoxNet effectively reduces the pressure on edge servers from computationally intensive tasks.
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.
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