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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 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.

For active, probing-based bandwidth measurements performed on top of the unifying IP layer, it may seem reasonable to expect the measurement problem in wireless networks, such as ad-hoc networks, to be no different than the one in wired networks. However, in networks with 802.1 1 wireless links we show that this is not the case. Our experiments show that the measured available bandwidth is dependent on the probe packet size (contrary to what is observed in wired networks). Another equally important finding is that the measured link capacity is dependent on the probe packet size and on the cross-traffic intensity.The study we present has been performed using a bandwidth measurement tool, DietTopp, that is based on the previously not implemented TOPP method. DietTopp measures the end-to-end available bandwidth of a network path along with the capacity of the congested link.

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