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CVF: Cross-Video Filtration on the edge
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-9249-1633
Chalmers University of Technology, Gothenburg, Sweden.
Ericsson Research.
Chalmers University of Technology, Gothenburg, Sweden.
2024 (English)In: MMSys '24: Proceedings of the 15th ACM Multimedia Systems Conference, Association for Computing Machinery (ACM), 2024, p. 231-242Conference paper, Published paper (Refereed)
Abstract [en]

Many edge applications rely on expensive Deep-Neural-Network (DNN) inference-based video analytics. Typically, a single instance of an inference service analyzes multiple real-time camera streams concurrently. In many cases,  only a fraction of these streams contain objects-of-interest at a given time. Hence, it is a waste of computational resources to process all frames from all cameras using the DNNs. On-camera filtration of frames has been suggested as a possible solution to improve the system efficiency and reduce resource wastage. However, many cameras do not have on-camera processing or filtering capabilities. In addition, filtration can be enhanced if frames across the different feeds are selected and prioritized for processing based on the system load and the available resource capacity. This paper introduces CVF, a Cross-video Filtration framework designed around video content and resource constraints. The CVF pipeline leverages compressed-domain data from encoded video formats, lightweight binary classification models, and an efficient prioritization algorithm. This enables the effective filtering of cross-camera frames from multiple sources, processing only a fraction of frames using resource-intensive DNN models. Our experiments show that CVF is capable of reducing the overall response time of video analytics pipelines by up to 50% compared to state-of-the-art solutions while increasing the throughput by up to 120%.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024. p. 231-242
Keywords [en]
Edge, Video Analytics, Video Filtration, Codecs
National Category
Computer Sciences
Research subject
Computer Science; Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-223020DOI: 10.1145/3625468.3647627Scopus ID: 2-s2.0-85191950336ISBN: 979-8-4007-0412-3 (print)OAI: oai:DiVA.org:umu-223020DiVA, id: diva2:1849484
Conference
ACM Multimedia Systems Conference 2024 (MMSys ’24), Bari, Italy, April 15-18, 2024
Available from: 2024-04-08 Created: 2024-04-08 Last updated: 2024-05-16Bibliographically approved
In thesis
1. Edge orchestration for latency-sensitive applications
Open this publication in new window or tab >>Edge orchestration for latency-sensitive applications
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Orkestrering av distribuerade resurser för latenskänsliga applikationer
Abstract [en]

The emerging edge computing infrastructure provides distributed and heterogeneous resources closer to where data is generated and where end-users are located, thereby significantly reducing latency. With the recent advances in telecommunication systems, software architecture, and machine learning, there is a noticeable increase in applications that require processing times within tight latency constraints, i.e. latency-sensitive applications. For instance, numerous video analytics applications, such as traffic control systems, necessitate real-time processing capabilities. Orchestrating such applications at the edge offers numerous advantages, including lower latency, optimized bandwidth utilization, and enhanced scalability. However, despite its potential, effectively managing such latency-sensitive applications at the edge poses several challenges such as constrained compute resources, which holds back the full promise of edge computing.

This thesis proposes approaches to efficiently deploy latency-sensitive applications on the edge infrastructure. It partly addresses general applications with microservice architectures and party addresses the increasingly more important video analytics applications for the edge. To do so, this thesis proposes various application- and system-level solutions aiming to efficiently utilize constrained compute capacity on the edge while meeting prescribed latency constraints. These solutions primarily focus on effective resource management approaches and optimizing incoming workload inputs, considering the constrained compute capacity of edge resources. Additionally, the thesis explores the synergy effects of employing both application- and system-level resource optimization approaches together.

The results demonstrate  the effectiveness of the proposed solutions in enhancing the utilization of edge resources for latency-sensitive applications while adhering to application constraints. The proposed resource management solutions, alongside application-level optimization techniques, significantly improve resource efficiency while satisfying application requirements. Our results show that our solutions for microservice architectures significantly improve end-to-end latency by up to 800% while minimizing edge resource usage. Additionally, the results indicate that our application- and system-level optimizations for orchestrating edge resources for video analytics applications can increase the overall throughput by up to 60%. 

Place, publisher, year, edition, pages
Umeå: Umeå University, 2024. p. 46
Series
UMINF, ISSN 0348-0542 ; 24.05
Keywords
Edge Computing, Resource Management, Latency-Sensitive Applications, Edge Video Analytics
National Category
Computer Sciences
Research subject
Computer Science; Computer Systems
Identifiers
urn:nbn:se:umu:diva-223021 (URN)978-91-8070-350-5 (ISBN)978-91-8070-351-2 (ISBN)
Public defence
2024-04-29, Hörsal UB.A.240 - Lindellhallen 4, 13:00 (English)
Opponent
Supervisors
Note

Incorrect date of publication on the posting sheet.

In publication: UMINF 24.04

Available from: 2024-04-08 Created: 2024-04-08 Last updated: 2024-04-23Bibliographically approved

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Rahmanian, Ali

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CiteExportLink to record
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Citation style
  • apa
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