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Predicting Real-time Service-level Metrics from Device Statistics
KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2680-9065
Ericsson Research, Sweden.
Swedish Institute of Computer Science (SICS), Sweden.
Ericsson Research, Sweden.
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2015 (English)In: IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, Ottawa, Canada, IEEE Communications Society, 2015Conference paper (Refereed)
Abstract [en]

While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices. Specifically, we collect statistics from a Linux kernel of a server machine and predict client-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach service-independent and unique. While our current lab configuration is simple, our results, gained through extensive experimentation, prove the feasibility of accurately predicting client-side metrics, such as video frame rates and RTP packet rates, often within 10-15% error (NMAE), also under high computational load and across traces from different scenarios.

Place, publisher, year, edition, pages
IEEE Communications Society, 2015.
Keyword [en]
Quality of service, cloud computing, network analytics, statistical learning, machine learning, video streaming
National Category
Communication Systems Computer Systems Telecommunications
Research subject
Computer Science; Electrical Engineering
URN: urn:nbn:se:kth:diva-158063DOI: 10.1109/INM.2015.7140318ISI: 000380495900049ScopusID: 2-s2.0-84942572120ISBN: 978-3-9018-8276-0OAI: diva2:774205
IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, Ottawa, Canada, May 11-15 2015
VINNOVA, 2013-03895

QC 20150527

Available from: 2014-12-22 Created: 2014-12-22 Last updated: 2016-09-23Bibliographically approved
In thesis
1. Data-driven Performance Prediction and Resource Allocation for Cloud Services
Open this publication in new window or tab >>Data-driven Performance Prediction and Resource Allocation for Cloud Services
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cloud services, which provide online entertainment, enterprise resource management, tax filing, etc., are becoming essential for consumers, businesses, and governments. The key functionalities of such services are provided by backend systems in data centers. This thesis focuses on three fundamental problems related to management of backend systems. We address these problems using data-driven approaches: triggering dynamic allocation by changes in the environment, obtaining configuration parameters from measurements, and learning from observations. 

The first problem relates to resource allocation for large clouds with potentially hundreds of thousands of machines and services. We developed and evaluated a generic gossip protocol for distributed resource allocation. Extensive simulation studies suggest that the quality of the allocation is independent of the system size for the management objectives considered.

The second problem focuses on performance modeling of a distributed key-value store, and we study specifically the Spotify backend for streaming music. We developed analytical models for system capacity under different data allocation policies and for response time distribution. We evaluated the models by comparing model predictions with measurements from our lab testbed and from the Spotify operational environment. We found the prediction error to be below 12% for all investigated scenarios.

The third problem relates to real-time prediction of service metrics, which we address through statistical learning. Service metrics are learned from observing device and network statistics. We performed experiments on a server cluster running video streaming and key-value store services. We showed that feature set reduction significantly improves the prediction accuracy, while simultaneously reducing model computation time. Finally, we designed and implemented a real-time analytics engine, which produces model predictions through online learning.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 53 p.
TRITA-EE, ISSN 1653-5146 ; 2016:020
National Category
Communication Systems Computer Systems Telecommunications Computer Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
urn:nbn:se:kth:diva-184601 (URN)978-91-7595-876-7 (ISBN)
Public defence
2016-05-03, F3, Lindstedtsvägen 26, KTH Campus, Stockholm, 14:00 (English)
VINNOVA, 2013-03895

QC 20160411

Available from: 2016-04-11 Created: 2016-04-01 Last updated: 2016-05-30Bibliographically approved

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