Poster: Data-Aware Edge Sampling for Aggregate Query Approximation

Joel Wolfrath, Abhishek Chandra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for realtime analytics. One estimate suggests that we should expect nine smart-devices per person by the year 2025 [1]. These devices generate data which might include sensor readings from a smart home, event or system logs on a device, or video feeds from surveillance cameras. As the number of devices increases, the cost of streaming the device data to the cloud over the wide-area network (WAN) will also increase substantially. Transferring and querying this data efficiently has become the focus of much academic research [2]-[5]. Edge computation affords us the opportunity to address this problem by utilizing resources close to the devices. Edge resources have many different use cases, including minimizing end-to-end latency or maximizing throughput [6], [7]. We restrict our focus to minimizing the required WAN bandwidth, which is an effort to address the increase in data volume.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-160
Number of pages3
ISBN (Electronic)9781728159430
DOIs
StatePublished - Nov 2020
Event5th IEEE/ACM Symposium on Edge Computing, SEC 2020 - Virtual, San Jose, United States
Duration: Nov 11 2020Nov 13 2020

Publication series

NameProceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020

Conference

Conference5th IEEE/ACM Symposium on Edge Computing, SEC 2020
Country/TerritoryUnited States
CityVirtual, San Jose
Period11/11/2011/13/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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