Name
Abstract | ||
|---|---|---|
Resource provisioning, the task of planning sufficient amounts of resources to meet service level agreements, has become an important management task in emerging cloud computing services. In this paper, we present a stochastic modeling approach to guide the resource provisioning task for future service clouds as the demand grows large. We focus on on-demand services and consider service availability as the key quality of service constraint. A specific scenario under consideration is when resources can be measured in base instances. We develop an asymptotic provisioning methodology that utilizes tight performance bounds for the Erlang loss system to determine the minimum capacity levels that meet the service availability requirements. We show that our provisioning solutions are not only asymptotically exact but also provide better QoS guarantees at all load conditions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2254756.2254816 | SIGMETRICS |
Keywords | Field | DocType |
qos guarantee,important management task,service constraint,on-demand service,cloud computing service,large scale cloud computing,future service cloud,service level agreement,erlang loss system,service availability requirement,service availability,cloud computing,loss network,stochastic model,quality of service | Service level,Cloud computing services,Computer science,Erlang (programming language),Computer network,Quality of service,Real-time computing,Provisioning,Utility computing,Thin provisioning,Distributed computing,Cloud computing | Conference |
Volume | Issue | ISSN |
40 | 1 | 0163-5999 |
Citations | PageRank | References |
4 | 0.41 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Tan | 1 | 12 | 1.60 |
| Yingdong Lu | 2 | 51 | 3.72 |
| Cathy Xia | 3 | 239 | 17.94 |