Name
Abstract | ||
|---|---|---|
Self-adaptive clouds extend upstream the regular cloud platforms with special autonomy features dedicated to handling increasing workload and service failures. The identification of such features is not necessarily an easy task. Sometimes those can be explicitly stated by QoS requirements or in preliminary material available to requirements engineers. Often though, they are implicit so that autonomy features capturing has to be undertaken. This paper elaborates on a methodology of capturing autonomy requirements for self-adaptive clouds with ARE, the Autonomy Requirements Engineering approach. In this approach, autonomy features are detected as special self-* objectives backed up by different capabilities and quality characteristics. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IPDPSW.2014.151 | Parallel & Distributed Processing Symposium Workshops |
Keywords | Field | DocType |
cloud computing,formal specification,natural sciences computing,quality of service,ARE,QoS requirements,autonomy feature detection,autonomy requirements engineering,feature identification,self-* objectives,self-adaptive science clouds,service failures,workload handling,autonomic systems,autonomy requirements,self-adaptive clouds | Software engineering,Computer science,Workload,Autonomy,Requirements engineering,Quality of service,Self adaptive,Management science,Distributed computing,Cloud computing | Conference |
Citations | PageRank | References |
1 | 0.40 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emil Vassev | 1 | 263 | 41.81 |
| Mike Hinchey | 2 | 494 | 51.89 |