Name
Abstract | ||
|---|---|---|
Predicting resource requirements for cloud services is critical for dimensioning, anomaly detection and service assurance. We demonstrate a system for real-time estimation of the needed amount of infrastructure resources, such as CPU and memory, for a given service. Statistical learning methods on server statistics and load parameters of the service are used for learning a resource prediction model. The model can be used as a guideline for service deployment and for real-time identification of resource bottlenecks. |
Year | Venue | Field |
|---|---|---|
2017 | IM | Service assurance,Cloud management,Anomaly detection,Software deployment,Computer science,Server,Computer network,Memory management,Dimensioning,Cloud computing,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christofer Flinta | 1 | 39 | 8.71 |
| Andreas Johnsson | 2 | 46 | 10.68 |
| Jawwad Ahmed | 3 | 85 | 7.97 |
| Farnaz Moradi | 4 | 33 | 6.22 |
| Rafael Pasquini | 5 | 43 | 12.82 |
| Rolf Stadler | 6 | 706 | 70.88 |