Predicting Real-time Service-level Metrics from Device Statistics
2014 (English)Report (Other academic)
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
KTH Royal Institute of Technology, 2014. , 9 p.
TRITA-EE, ISSN 1653-5146 ; 2014:053
Quality of service, cloud computing, network analytics, statistical learning, machine learning, video streaming
Computer Science Communication Systems Telecommunications
IdentifiersURN: urn:nbn:se:kth:diva-152637OAI: oai:DiVA.org:kth-152637DiVA: diva2:750694