Analysis and characterization of a video-on-demand service workload
(2015) 6th ACM Multimedia Systems Conference p.189-200- Abstract
- Video-on-Demand (VoD) and video sharing services account for a large percentage of the total downstream Internet traffic. In order to provide a better understanding of the load on these services, we analyze and model a workload trace from a VoD service provided by a major Swedish TV broadcaster. The trace contains over half a million requests generated by more than 20000 unique users. Among other things, we study the request arrival rate, the inter-arrival time, the spikes in the workload and their cause, the video popularity distribution, the streaming bit-rate distribution and the video duration distribution. Contrary to some previously analyzed workloads in the literature, our results show that the user and the session arrival rates for... (More)
- Video-on-Demand (VoD) and video sharing services account for a large percentage of the total downstream Internet traffic. In order to provide a better understanding of the load on these services, we analyze and model a workload trace from a VoD service provided by a major Swedish TV broadcaster. The trace contains over half a million requests generated by more than 20000 unique users. Among other things, we study the request arrival rate, the inter-arrival time, the spikes in the workload and their cause, the video popularity distribution, the streaming bit-rate distribution and the video duration distribution. Contrary to some previously analyzed workloads in the literature, our results show that the user and the session arrival rates for the TV4 workload does not follow a Poisson process. The arrival rate distribution is modeled using a lognormal distribution while the interarrival time distribution is modeled using a stretched exponential distribution. We observe the “impatient user” behavior where users abandon streaming sessions after minutes or
even seconds of starting them. Both very popular videos and non-popular videos are specially affected by impatient users. We also show that this behavior is an invariant in VoD workloads and is neither affected by the average bit-rate nor by the number of videos a user watch. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8835022
- author
- Ali-Eldin, Ahmed ; Kihl, Maria LU ; Tordsson, Johan and Elmroth, Erik
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [Host publication title missing]
- pages
- 189 - 200
- publisher
- Association for Computing Machinery (ACM)
- conference name
- 6th ACM Multimedia Systems Conference
- conference dates
- 2015-03-18 - 2015-03-20
- external identifiers
-
- scopus:84942543893
- ISBN
- 978-1-4503-3351-1
- DOI
- 10.1145/2713168.2713183
- project
- EIT_VR CLOUD Cloud Control
- LCCC
- language
- English
- LU publication?
- yes
- id
- 490fc8a7-214b-44a6-9d10-534f09819a2a (old id 8835022)
- date added to LUP
- 2016-04-04 10:44:42
- date last changed
- 2022-05-01 20:23:24
@inproceedings{490fc8a7-214b-44a6-9d10-534f09819a2a,
abstract = {{Video-on-Demand (VoD) and video sharing services account for a large percentage of the total downstream Internet traffic. In order to provide a better understanding of the load on these services, we analyze and model a workload trace from a VoD service provided by a major Swedish TV broadcaster. The trace contains over half a million requests generated by more than 20000 unique users. Among other things, we study the request arrival rate, the inter-arrival time, the spikes in the workload and their cause, the video popularity distribution, the streaming bit-rate distribution and the video duration distribution. Contrary to some previously analyzed workloads in the literature, our results show that the user and the session arrival rates for the TV4 workload does not follow a Poisson process. The arrival rate distribution is modeled using a lognormal distribution while the interarrival time distribution is modeled using a stretched exponential distribution. We observe the “impatient user” behavior where users abandon streaming sessions after minutes or<br/><br>
even seconds of starting them. Both very popular videos and non-popular videos are specially affected by impatient users. We also show that this behavior is an invariant in VoD workloads and is neither affected by the average bit-rate nor by the number of videos a user watch.}},
author = {{Ali-Eldin, Ahmed and Kihl, Maria and Tordsson, Johan and Elmroth, Erik}},
booktitle = {{[Host publication title missing]}},
isbn = {{978-1-4503-3351-1}},
language = {{eng}},
pages = {{189--200}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{Analysis and characterization of a video-on-demand service workload}},
url = {{https://lup.lub.lu.se/search/files/5611815/8835046.pdf}},
doi = {{10.1145/2713168.2713183}},
year = {{2015}},
}