research-article

Open-source software tools for measuring resources consumption and DASH metrics

Authors:

Mario Montagud,

Juan Antonio De Rus,

Rafael Fayos-Jordan,

Miguel Garcia-Pineda,

Jaume Segura-GarciaAuthors Info & Claims

MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference

Pages 261 - 266

https://doi.org/10.1145/3339825.3394931

Published: 27 May 2020 Publication History

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Abstract

When designing and deploying multimedia systems, it is essential to accurately know about the necessary requirements and the Quality of Service (QoS) offered to the customers. This paper presents two open-source software tools that contribute to these key needs. The first tool is able to measure and register resources consumption metrics for any Windows program (i.e. process id), like the CPU, GPU and RAM usage. Unlike the Task Manager, which requires manual visual inspection for just a subset of these metrics, the developed tool runs on top of the Powershell to periodically measure these metrics, calculate statistics, and register them in log files. The second tool is able to measure QoS metrics from DASH streaming sessions by running on top of TShark, if a non-secure HTTP connection is used. For each DASH chunk, the tool registers: the round-trip time from request to download, the number of TCP segments and bytes, the effective bandwidth, the selected DASH representation, and the associated parameters in the MPD (e.g., resolution, bitrate). It also registers the MPD and the total amount of downloaded frames and bytes. The advantage of this second tool is that these metrics can be registered regardless of the player used, even from a device connected to the same network than the DASH player.

References

[1]

M. Montagud, J. A. Núñez, T. Karavellas, I. Jurado, S. Fernández, "Convergence between TV and VR: Enabling Truly Immersive and Social Experiences", Workshop on Virtual Reality, co-located with ACM TVX 2018, Seoul (South Korea), June 2018.

Google Scholar

[2]

J. Segura-Garcia, M. Montagud, S. Mirasol-Menacho, J. Oleza-Simó "Theatrical Virtual Acoustic rendering with head movement interaction", AI EDAM, Volume 33, Issue 3, pp. 359--368, August 2019.

Google Scholar

[3]

T. Stockhammer, "Dynamic adaptive streaming over HTTP: standards and design principles", ACM MMSYS 2011, San Jose, CA (USA), February 2011.

Digital Library

Google Scholar

[4]

DASH: https://mpeg.chiariglione.org/standards/mpeg-dash, Last Access: February 2020.

Google Scholar

[5]

A. Bentaleb, C. Timmerer, A. C. Begen, R. Zimmermann, "Bandwidth prediction in low-latency chunked streaming", 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV '19), Amherst, MA (USA), June 2019

Digital Library

Google Scholar

[6]

M. B. Yahia, Y. Le Louedec, G. Simon, L. Nuaymi, X. Corbillon, "HTTP/2-based Frame Discarding for Low-Latency Adaptive Video Streaming". ACM Trans. Multimedia Comput. Commun. Appl. 15, 1, Article 18, February 2019.

Digital Library

Google Scholar

[7]

A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, and R. Zimmermann., A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP. IEEE Communications Surveys Tutorials 21, 1, 562--585, Firstquarter 2019.

Crossref

Google Scholar

[8]

J. W. Kleinrouweler, S. Cabrero, P. Cesar, "Delivering Stable High-Quality Video: An SDN Architecture with DASH Assisting Network Elements", ACM Multimedia Systems 2016 Conference (MMSys '16), Klagenfurt am Wörthersee (Austria), May 2016.

Google Scholar

[9]

C. Timmerer, A. Zabrovskiy, "Automating QoS and QoE Evaluation of HTTP Adaptive Streaming Systems", ZTE COMMUNICATIONS, 17(1), March 2019

Google Scholar

[10]

M. Montagud, E. Meyerson, I. Fraile, S. Fernández, "Modular Testbed for KPI Monitoring in Omnidirectional Video Streaming Scenarios", MMEDIA 2019, València (Spain), March 2019.

Google Scholar

Cited By

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Fernandez SMontagud MRincón DMoragues JCernigliaro GEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Addressing Scalability for Real-time Multiuser Holo-portation: Introducing and Assessing a Multipoint Control Unit (MCU) for Volumetric VideoProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613777(9243-9251)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613777
Subramanyam SViola IJansen JAlexiou EHanjalic ACesar PMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Evaluating the Impact of Tiled User-Adaptive Real-Time Point Cloud Streaming on VR Remote CommunicationProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548220(3094-3103)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548220
Langa SCliment MCernigliaro GRincon Rivera D(2022)Toward Hyper-Realistic and Interactive Social VR Experiences in Live TV ScenariosIEEE Transactions on Broadcasting10.1109/TBC.2021.312349968:1(13-32)Online publication date: Mar-2022
https://doi.org/10.1109/TBC.2021.3123499
Show More Cited By

Index Terms

Open-source software tools for measuring resources consumption and DASH metrics
1. Information systems
  1. Information systems applications
2. Software and its engineering
  1. Software creation and management

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Published In

MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference

May 2020

403 pages

ISBN:9781450368452

DOI:10.1145/3339825

General Chairs:
Laura Toni
University College London
,
Ali C. Begen
Ozyegin University and Networked Media
,
Program Chairs:
Özgü Alay
University of Oslo and Simula Metropolitan
,
Christian Timmerer
Alpen-Adria-Universität Klagenfurt and Bitmovin

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2020

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Funding Sources

Spanish Ministry of Science, Innovation and Universities
Fundacion BBVA

Conference

MMSys '20

Sponsor:

SIGMM

MMSys '20: 11th ACM Multimedia Systems Conference

June 8 - 11, 2020

Istanbul, Turkey

Acceptance Rates

MMSys '20 Paper Acceptance Rate 18 of 55 submissions, 33%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Citations

Cited By

View all

Fernandez SMontagud MRincón DMoragues JCernigliaro GEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Addressing Scalability for Real-time Multiuser Holo-portation: Introducing and Assessing a Multipoint Control Unit (MCU) for Volumetric VideoProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613777(9243-9251)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613777
Subramanyam SViola IJansen JAlexiou EHanjalic ACesar PMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Evaluating the Impact of Tiled User-Adaptive Real-Time Point Cloud Streaming on VR Remote CommunicationProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548220(3094-3103)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548220
Langa SCliment MCernigliaro GRincon Rivera D(2022)Toward Hyper-Realistic and Interactive Social VR Experiences in Live TV ScenariosIEEE Transactions on Broadcasting10.1109/TBC.2021.312349968:1(13-32)Online publication date: Mar-2022
https://doi.org/10.1109/TBC.2021.3123499
Langa SMontagud MCernigliaro GRivera D(2022)Multiparty Holomeetings: Toward a New Era of Low-Cost Volumetric Holographic Meetings in Virtual RealityIEEE Access10.1109/ACCESS.2022.319628510(81856-81876)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3196285
Gunkel SHindriks RAssal KStokking HDijkstra-Soudarissanane SHaar FNiamut OAlay ÖHsu CBegen A(2021)VRCommProceedings of the 12th ACM Multimedia Systems Conference10.1145/3458305.3459595(65-79)Online publication date: 24-Jun-2021
https://dl.acm.org/doi/10.1145/3458305.3459595
Faria VGonçalves JSilva CVieira GMascarenhas D(2020)SDToW: A Slowloris Detecting Tool for WMNsInformation10.3390/info1112054411:12(544)Online publication date: 25-Nov-2020
https://doi.org/10.3390/info11120544

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