Predicting real-time service-level metrics from device statistics

Rerngvit Yanggratoke, Jawwad Ahmed, John Ardelius, Christofer Flinta, Andreas Johnsson, Daniel Gillblad, Rolf Stadler
2015 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)  
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
more » ... nt-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach serviceindependent 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.
doi:10.1109/inm.2015.7140318 dblp:conf/im/YanggratokeAAFJ15 fatcat:udd3kwwubjdivjdceuroeq4zm4