Characterizing task completion latencies in multi-point multi-quality fog computing systems


Journal Article

© 2020 Elsevier B.V. Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive studies quantitatively characterizing the properties of fog computing architectures have been conducted. In this paper we examine the statistical properties of fog computing task completion latencies, which are important to understand to develop algorithms that match IoT nodes’ tasks with the best execution points within the fog computing substrate. Towards characterizing task completion latencies, we developed and deployed a set of benchmarks in 6 different locations, which included local nodes of different grades, conventional cloud computing services in two different regions, and Amazon Web Services (AWS) and Microsoft Azure serverless computing options. Using the developed infrastructure, we conducted a series of targeted experiments with a node invoking our benchmarks from different locations and in different conditions. The empirical study elucidated several important properties of task execution latencies, including latency variation across different execution points and execution options, and stability with respect to time. The study also demonstrated important properties of serverless execution options, and showed that statistical structure of computing latencies can be accurately characterized based on a small number (only 10–50) of latency samples. The complete measurement set we have captured as part of this study is publicly available.

Full Text

Duke Authors

Cited Authors

  • Gorlatova, M; Inaltekin, H; Chiang, M

Published Date

  • November 9, 2020

Published In

Volume / Issue

  • 181 /

International Standard Serial Number (ISSN)

  • 1389-1286

Digital Object Identifier (DOI)

  • 10.1016/j.comnet.2020.107526

Citation Source

  • Scopus