Name
Abstract | ||
|---|---|---|
Data centers providing modern interactive applications are enriched by autonomous management decision systems that are able to clone and migrate virtual machines, to re-distribute resources or to re-map services in real-time. At the basis of all these decisions, there is the need of a continuous evaluation of the state of system resources and of detecting when some relevant changes are occurring. Unfortunately, the load of interactive applications reaching the system is intrinsically heterogeneous with consequent highly variable effects on the resource behavior emerging from system monitors. Hence, existing algorithms for online detection of state changes are affected by low precision and scarce robustness when they are applied to modern contexts. We propose a novel model for online detection of relevant state changes that combines a filtered representation of the raw measures with adaptive detection rules. Experiments carried out on real and emulated data sets confirm that the proposed model is able to timely signal all relevant state changes, to limit false detections and, even more important, its results are robust in highly variable contexts. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/PCCC.2010.5682302 | 2010 IEEE 29TH INTERNATIONAL PERFORMANCE COMPUTING AND COMMUNICATIONS CONFERENCE (IPCCC) |
Keywords | Field | DocType |
real time,virtual machine,resource manager,decision theory,resource allocation,data center | Resource management,Data set,Virtual machine,Computer science,Decision system,Real-time computing,Robustness (computer science),Resource allocation,Decision theory,Autonomous management | Conference |
ISSN | Citations | PageRank |
1097-2641 | 3 | 0.41 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Casolari | 1 | 209 | 14.45 |
| Michele Colajanni | 2 | 1558 | 126.74 |
| Stefania Tosi | 3 | 38 | 5.22 |
| Francesco Lo Presti | 4 | 1073 | 78.83 |