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Analysis and characterization of a Video-on-Demand service workload
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
Lund University.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
2015 (English)In: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys 2015, ACM Digital Library, 2015, 189-200 p.Conference paper, Published paper (Refereed)
Abstract [en]

Video-on-Demand (VoD) and video sharing services accountfor a large percentage of the total downstream Internet traf-fic. In order to provide a better understanding of the loadon these services, we analyze and model a workload tracefrom a VoD service provided by a major Swedish TV broad-caster. The trace contains over half a million requests gener-ated by more than 20000 unique users. Among other things,we study the request arrival rate, the inter-arrival time, thespikes in the workload, the video popularity distribution, thestreaming bit-rate distribution and the video duration distri-bution. Our results show that the user and the session ar-rival rates for the TV4 workload does not follow a Poissonprocess. The arrival rate distribution is modeled using a log-normal distribution while the inter-arrival time distributionis modeled using a stretched exponential distribution. Weobserve the “impatient user” behavior where users abandonstreaming sessions after minutes or even seconds of startingthem. Both very popular videos and non-popular videos areparticularly affected by impatient users. We investigate ifthis behavior is an invariant for VoD workloads.

Place, publisher, year, edition, pages
ACM Digital Library, 2015. 189-200 p.
National Category
Engineering and Technology Computer Systems
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:umu:diva-108393DOI: 10.1145/2713168.2713183Scopus ID: 2-s2.0-84942543893ISBN: 978-1-4503-3351-1 (print)OAI: oai:DiVA.org:umu-108393DiVA: diva2:852763
Conference
6th ACM Multimedia Systems Conference, MMSys 2015; Portland; United States; 18 March 2015 through 20 March 2015; Code 113421
Projects
CACTOSCloud Control
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2016-06-07Bibliographically approved
In thesis
1. Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
Open this publication in new window or tab >>Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis studies cloud capacity auto-scaling, or how to provision and release re-sources to a service running in the cloud based on its actual demand using an auto-matic controller. As the performance of server systems depends on the system design,the system implementation, and the workloads the system is subjected to, we focuson these aspects with respect to designing auto-scaling algorithms. Towards this goal,we design and implement two auto-scaling algorithms for cloud infrastructures. Thealgorithms predict the future load for an application running in the cloud. We discussthe different approaches to designing an auto-scaler combining reactive and proactivecontrol methods, and to be able to handle long running requests, e.g., tasks runningfor longer than the actuation interval, in a cloud. We compare the performance ofour algorithms with state-of-the-art auto-scalers and evaluate the controllers’ perfor-mance with a set of workloads. As any controller is designed with an assumptionon the operating conditions and system dynamics, the performance of an auto-scalervaries with different workloads.In order to better understand the workload dynamics and evolution, we analyze a6-years long workload trace of the sixth most popular Internet website. In addition,we analyze a workload from one of the largest Video-on-Demand streaming servicesin Sweden. We discuss the popularity of objects served by the two services, the spikesin the two workloads, and the invariants in the workloads. We also introduce, a mea-sure for the disorder in a workload, i.e., the amount of burstiness. The measure isbased on Sample Entropy, an empirical statistic used in biomedical signal processingto characterize biomedical signals. The introduced measure can be used to charac-terize the workloads based on their burstiness profiles. We compare our introducedmeasure with the literature on quantifying burstiness in a server workload, and showthe advantages of our introduced measure.To better understand the tradeoffs between using different auto-scalers with differ-ent workloads, we design a framework to compare auto-scalers and give probabilisticguarantees on the performance in worst-case scenarios. Using different evaluation cri-teria and more than 700 workload traces, we compare six state-of-the-art auto-scalersthat we believe represent the development of the field in the past 8 years. Knowingthat the auto-scalers’ performance depends on the workloads, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components;an analyzer, and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads, and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2015. 16 p.
Series
Report / UMINF, ISSN 0348-0542 ; 15.09
Keyword
cloud computing, autoscaling, workloads, performance modeling, controller design
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-108398 (URN)978-91-7601-330-4 (ISBN)
Public defence
2015-10-02, N360, Naturveterhuset Building, Umeå University, Umeå, 14:00 (English)
Opponent
Supervisors
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2015-09-11 Created: 2015-09-10 Last updated: 2015-10-07Bibliographically approved

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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • Other locale
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Output format
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  • asciidoc
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