Evaluating Load Adjusted Learning Strategies for Client Service Levels Prediction from Cloud-hosted Video Servers

Network managers that succeed in improving the accuracy of client video service level predictions, where the video is deployed in a cloud infrastructure, will have the ability to deliver responsive, SLA-compliant service to their customers. Meeting up-time guarantees, achieving rapid first-call resolution, and minimizing time-to-recovery af- ter video service outages will maintain customer loyalty. To date, regression-based models have been applied to generate these predictions for client machines using the kernel metrics of a server clus- ter. The effect of time-varying loads on cloud-hosted video servers, which arise due to dynamic user requests have not been leveraged to improve prediction using regularized learning algorithms such as the LASSO and Elastic Net and also Random Forest. We evaluate the performance of load-adjusted learning strategies using a number of learning algorithms and demonstrate that improved predictions are achieved irrespective of the learning approach. A secondary benefit of the load-adjusted learning approach is that it reduces the computational cost as long as the load is not constant. Finally, we demonstrate that Random Forest significantly improve the prediction performance produced by the best performing linear regression variant, the Elastic Net

State Acquisition in Computer Networks

We establish that State Acquisition should be per- formed in networks at a rate which is consistent with the rate-of-change of the element or service being observed. We demonstrate that many existing monitoring and service-level prediction tools do not acquire network state in an appropriate manner. To address this challenge: (1) we define the rate-of- change of different applications; (2) we use methods for analysis of unevenly spaced time series, specifically, time series arising from video and voice applications, to estimate the rate-of-change of these services; and finally, (3) we demonstrate how to acquire network state accurately for a number of real-world traces using Greedy Acquisition. The accuracy of State Acquisition is improved when it is performed at a rate which is consistent with its rate-of-change. An improvement in representation accuracy of one order of magnitude is achieved for voice and video streaming applications; this improvement does not incur any additional bandwidth or storage cost

Predicting Real-time Service-level Metrics from Device Statistics

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.QC 20150527VINNOVA REAL

Predicting Real-time Service-level Metrics from Device Statistics

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.QC 20150527VINNOVA REAL