Name
Abstract | ||
|---|---|---|
In cloud, service performances are expected to meet various QoS requirements stably, and a great challenge for achieving this comes from the great workload fluctuations in stateful systems. So far, few previous works have endeavored for handling overload caused by such fluctuations. In this paper, we propose an efficient overload control strategy to solve this problem. Crucial server status information is indexed by R-tree to provide global view for data movement. Based on index, a two-step filtering approach is introduced to eliminate irrational server candidates. A server selection algorithm considering workload patterns is presented afterwards to acquire load-balancing effects. Extensive experiments are conducted to evaluate the performance of our strategy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-29426-6_3 | APWeb Workshops |
Keywords | Field | DocType |
efficient overload control strategy,crucial server status information,great challenge,great workload fluctuation,server selection algorithm,workload pattern,extensive experiment,data movement,global view,irrational server candidate | Data mining,Decision tree,Computer science,Workload,Selection algorithm,Quality of service,Filter (signal processing),Real-time computing,Overload control,Stateful firewall,Database,Cloud computing | Conference |
Citations | PageRank | References |
2 | 0.39 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiling Sun | 1 | 27 | 3.01 |
| Jiajie Xu | 2 | 278 | 39.90 |
| Zhiming Ding | 3 | 348 | 38.93 |
| Y. Gao | 4 | 17 | 5.51 |
| Kuien Liu | 5 | 107 | 10.51 |