Name
Abstract | ||
|---|---|---|
Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video streaming service running on the cluster. The method is service-agnostic in the sense that it takes as input operating-systems statistics instead of service-level metrics. We show that feature set reduction significantly improves prediction accuracy in our case, while simultaneously reducing model computation time. We also discuss design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CNSM.2015.7367349 | CNSM |
Keywords | Field | DocType |
Quality of service, cloud computing, network analytics, statistical learning, machine learning | Data mining,Computer science,Server,Testbed,Computer network,Communications system,Quality of service,Analytics,Computer cluster,Cloud computing,Computation | Conference |
ISSN | Citations | PageRank |
2165-9605 | 11 | 0.92 |
References | Authors | |
20 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rerngvit Yanggratoke | 1 | 63 | 5.77 |
| Jawwad Ahmed | 2 | 85 | 7.97 |
| John Ardelius | 3 | 61 | 5.83 |
| Christofer Flinta | 4 | 39 | 8.71 |
| Andreas Johnsson | 5 | 46 | 10.68 |
| Daniel Gillblad | 6 | 119 | 11.99 |
| Rolf Stadler | 7 | 706 | 70.88 |