{"title":"QoS Explorer: A Tool for Exploring QoS in Composed Services","authors":"Conrad Hughes, J. Hillman","doi":"10.1109/ICWS.2006.108","DOIUrl":null,"url":null,"abstract":"This paper presents QoS explorer, an interactive tool we have developed which predicts quality of service (QoS) of a workflow from the QoS characteristics of its constituents, even when the relationships involved are complex. This facilitates design and instantiation of workflows to satisfy QoS constraints, as it enables the user to discover and focus effort on the aspects of a workflow which most affect their primary QoS concerns, thus improving efficiency of workflow development. Further, the underlying model we use is more sophisticated than those of similar recent work (Jaeger et al., 2005; Ardagna and Pernici, 2005; Menasce, 2004), and includes processing of entire statistical distributions and probabilistic states (instead of the simple numeric constants used elsewhere) to model such non-constant variables as execution time","PeriodicalId":408032,"journal":{"name":"2006 IEEE International Conference on Web Services (ICWS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Web Services (ICWS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWS.2006.108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
This paper presents QoS explorer, an interactive tool we have developed which predicts quality of service (QoS) of a workflow from the QoS characteristics of its constituents, even when the relationships involved are complex. This facilitates design and instantiation of workflows to satisfy QoS constraints, as it enables the user to discover and focus effort on the aspects of a workflow which most affect their primary QoS concerns, thus improving efficiency of workflow development. Further, the underlying model we use is more sophisticated than those of similar recent work (Jaeger et al., 2005; Ardagna and Pernici, 2005; Menasce, 2004), and includes processing of entire statistical distributions and probabilistic states (instead of the simple numeric constants used elsewhere) to model such non-constant variables as execution time