Name
Abstract | ||
|---|---|---|
Service assurance for the telecom cloud is a challenging task and is continuously being addressed by academics and industry. One promising approach is to utilize machine learning to predict service quality in order to take early mitigation actions. In previous work we have shown how to predict service-level metrics, such as frame rate for a video application on the client side, from operational data gathered at the server side. This gives the service provider early indications on whether the platform can support the current load demand. This paper extends previous work by addressing scalability issues for cluster-based services. Operational data being generated in large volumes, from several sources, and at high velocity puts strain on computational and communication resources. We propose and evaluate a distributed machine learning system based on the Winnow algorithm to tackle scalability issues, and then compare the new distributed solution with the previously proposed centralized solution. We show that network overhead and computational execution time is substantially reduced while maintaining high prediction accuracy making it possible to achieve real-time service quality predictions in large systems. |
Year | Venue | Field |
|---|---|---|
2016 | IEEE IFIP Network Operations and Management Symposium | Service assurance,Service quality,Computer science,Computer network,Service provider,Winnow,Distributed database,Customer Service Assurance,Cloud computing,Scalability,Distributed computing |
DocType | ISSN | Citations |
Conference | 1542-1201 | 1 |
PageRank | References | Authors |
0.41 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jawwad Ahmed | 1 | 85 | 7.97 |
| Andreas Johnsson | 2 | 46 | 10.68 |
| Rerngvit Yanggratoke | 3 | 63 | 5.77 |
| John Ardelius | 4 | 61 | 5.83 |
| Christofer Flinta | 5 | 39 | 8.71 |
| Rolf Stadler | 6 | 706 | 70.88 |