Name
Title | ||
|---|---|---|
Research on SaaS Service Performance Prediction Method in Dynamic Resource Environment |
Abstract | ||
|---|---|---|
Users behavior's uncertainty and service resource dynamic's change make the prediction of SaaS performance trend more complex and difficult. This paper proposes a set SaaS performance prediction approach based on time series, defines three key indexes including SaaS Service Transactions Index (STI), Service Resource Occupancy Index (SROI) and Service Performance Index (SPI), and describes the computing methods of the three indexes' time series. The paper also presents a fuzzy matching algorithm of SaaS performance prediction, and designs experiments to identify the effectiveness of the approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SOSE.2013.74 | SoSE |
Keywords | Field | DocType |
fuzzy set theory,saas performance prediction,dynamic resource environment,time series,fuzzy matching algorithm,saas service transactions index,service performance index,software service pattern,saas performance prediction approach,spi,pattern matching,software performance evaluation,sroi,saas performance trend,designs experiments,fuzzy matching,software as a service,performance indexes,sti,computing method,saas service performance prediction,saas service performance prediction method,service resource occupancy index,design of experiments,cloud computing,designs experiment,indexes,prediction algorithms,time series analysis,radar | Data mining,Performance index,Computer science,Real-time computing,Software as a service,Fuzzy set,Approximate string matching,Pattern matching,Performance prediction,Design of experiments,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-4673-5659-6 | 0 | 0.34 |
References | Authors | |
6 | 5 | |