SODA

Predicting Real-time Service-level Metrics from Device Statistics

Yanggratoke, Rerngvit and Ahmed, Jawwad and Ardelius, John and Flinta, Christofer and Johnsson, Andreas (Ericsson Research) and Gillblad, Daniel and Stadler, Rolf (2014) Predicting Real-time Service-level Metrics from Device Statistics. [SICS Report]

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Abstract

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.

Item Type:SICS Report
ID Code:5750
Deposited By:Vicki Carleson
Deposited On:13 Nov 2014 13:06
Last Modified:13 Nov 2014 13:06

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