{"title":"为支持QoS的Web服务设计QoS代理算法","authors":"Tao Yu, Kwei-Jay Lin","doi":"10.1109/EEE.2004.1287283","DOIUrl":null,"url":null,"abstract":"QoS (quality of service) support in Web services is an important issue. We present a QoS-capable Web service architecture, QCWS, by introducing a QoS broker module between service clients and providers (servers). The functions of the QoS broker module include collecting QoS information about servers, making selection decisions for clients, and negotiating with servers to get QoS commitments. We propose two resource allocation algorithms (HQ and RQ) used by QoS broker when broker acts as the front-end of a server. The goals of algorithms are to maximize the server resource usage while minimizing the QoS instability for each client. The QoS performance and instability tradeoffs of both algorithms are studied by simulation.","PeriodicalId":360167,"journal":{"name":"IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"78","resultStr":"{\"title\":\"The design of QoS broker algorithms for QoS-capable Web services\",\"authors\":\"Tao Yu, Kwei-Jay Lin\",\"doi\":\"10.1109/EEE.2004.1287283\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"QoS (quality of service) support in Web services is an important issue. We present a QoS-capable Web service architecture, QCWS, by introducing a QoS broker module between service clients and providers (servers). The functions of the QoS broker module include collecting QoS information about servers, making selection decisions for clients, and negotiating with servers to get QoS commitments. We propose two resource allocation algorithms (HQ and RQ) used by QoS broker when broker acts as the front-end of a server. The goals of algorithms are to maximize the server resource usage while minimizing the QoS instability for each client. The QoS performance and instability tradeoffs of both algorithms are studied by simulation.\",\"PeriodicalId\":360167,\"journal\":{\"name\":\"IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"78\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EEE.2004.1287283\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEE.2004.1287283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The design of QoS broker algorithms for QoS-capable Web services
QoS (quality of service) support in Web services is an important issue. We present a QoS-capable Web service architecture, QCWS, by introducing a QoS broker module between service clients and providers (servers). The functions of the QoS broker module include collecting QoS information about servers, making selection decisions for clients, and negotiating with servers to get QoS commitments. We propose two resource allocation algorithms (HQ and RQ) used by QoS broker when broker acts as the front-end of a server. The goals of algorithms are to maximize the server resource usage while minimizing the QoS instability for each client. The QoS performance and instability tradeoffs of both algorithms are studied by simulation.