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On the design of resource allocation algorithms for low-latency video analytics

On the design of resource allocation algorithms for low-latency video analytics
On the design of resource allocation algorithms for low-latency video analytics

In this paper, we study how to design resource allocation algorithms for data analytics services that are computationally intensive and have low-latency requirements. As a paradigm application, we consider a video surveillance service where video streams are analyzed in the cloud with deep-learning algorithms (i.e., object detection and image classification). We present a network model that allows data analytics tasks to be processed in multiple stages, and propose an algorithm that provides low congestion when the arrival rate is constant over time. The algorithm also allows other types of data analytics to be carried out in the cloud in order to maximize resource utilization. The performance of the proposed algorithm is evaluated using simulation, and our results show that it is possible to obtain low-delay while maximizing the use of network resources.

2155-7586
468-473
IEEE
Valls, Victor
78221c8f-4ec0-41e0-a50d-15498e18f533
Kwon, Heesung
138eee1f-0d4c-4135-97f3-485fd1bbf1f8
Laporta, Tom
409fa656-ef4a-4073-82d5-01ab764ad2ee
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Tassiulas, Leandros
7457d150-1cdc-48de-9aff-308549167f33
Valls, Victor
78221c8f-4ec0-41e0-a50d-15498e18f533
Kwon, Heesung
138eee1f-0d4c-4135-97f3-485fd1bbf1f8
Laporta, Tom
409fa656-ef4a-4073-82d5-01ab764ad2ee
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Tassiulas, Leandros
7457d150-1cdc-48de-9aff-308549167f33

Valls, Victor, Kwon, Heesung, Laporta, Tom, Stein, Sebastian and Tassiulas, Leandros (2019) On the design of resource allocation algorithms for low-latency video analytics. In 2018 IEEE Military Communications Conference, MILCOM 2018. vol. 2019-October, IEEE. pp. 468-473 . (doi:10.1109/MILCOM.2018.8599750).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we study how to design resource allocation algorithms for data analytics services that are computationally intensive and have low-latency requirements. As a paradigm application, we consider a video surveillance service where video streams are analyzed in the cloud with deep-learning algorithms (i.e., object detection and image classification). We present a network model that allows data analytics tasks to be processed in multiple stages, and propose an algorithm that provides low congestion when the arrival rate is constant over time. The algorithm also allows other types of data analytics to be carried out in the cloud in order to maximize resource utilization. The performance of the proposed algorithm is evaluated using simulation, and our results show that it is possible to obtain low-delay while maximizing the use of network resources.

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More information

Published date: 3 January 2019
Venue - Dates: 2018 IEEE Military Communications Conference, , Los Angeles, United States, 2018-10-29 - 2018-10-31

Identifiers

Local EPrints ID: 428318
URI: http://eprints.soton.ac.uk/id/eprint/428318
ISSN: 2155-7586
PURE UUID: 2c62cd02-05e5-4328-a261-4c1ec643df30
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857

Catalogue record

Date deposited: 21 Feb 2019 17:30
Last modified: 18 Feb 2021 17:11

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Contributors

Author: Victor Valls
Author: Heesung Kwon
Author: Tom Laporta
Author: Sebastian Stein ORCID iD
Author: Leandros Tassiulas

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