{"title":"系统内部资源负荷趋势评估的自适应技术","authors":"S. Casolari, M. Colajanni, Stefania Tosi","doi":"10.1109/ICAS.2009.30","DOIUrl":null,"url":null,"abstract":"Modern distributed systems that have to avoid performance degradation and system overload require several runtime management decisions for load balancing and load sharing, overload and admission control, job dispatching and request redirection. As the external workload and the internal resource behavior of the modern system is highly complex and variable, self-adaptive techniques require a stable vision of the system behavior. In this paper we propose a trend model that guarantees a robust interpretation for load-aware decision algorithms. Various experimental results in a Web cluster demonstrate that the proposed models and algorithms guarantee better stability of the load and a reduction of the response time experienced by the users.","PeriodicalId":258907,"journal":{"name":"2009 Fifth International Conference on Autonomic and Autonomous Systems","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Self-Adaptive Techniques for the Load Trend Evaluation of Internal System Resources\",\"authors\":\"S. Casolari, M. Colajanni, Stefania Tosi\",\"doi\":\"10.1109/ICAS.2009.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Modern distributed systems that have to avoid performance degradation and system overload require several runtime management decisions for load balancing and load sharing, overload and admission control, job dispatching and request redirection. As the external workload and the internal resource behavior of the modern system is highly complex and variable, self-adaptive techniques require a stable vision of the system behavior. In this paper we propose a trend model that guarantees a robust interpretation for load-aware decision algorithms. Various experimental results in a Web cluster demonstrate that the proposed models and algorithms guarantee better stability of the load and a reduction of the response time experienced by the users.\",\"PeriodicalId\":258907,\"journal\":{\"name\":\"2009 Fifth International Conference on Autonomic and Autonomous Systems\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fifth International Conference on Autonomic and Autonomous Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAS.2009.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Conference on Autonomic and Autonomous Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAS.2009.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Self-Adaptive Techniques for the Load Trend Evaluation of Internal System Resources
Modern distributed systems that have to avoid performance degradation and system overload require several runtime management decisions for load balancing and load sharing, overload and admission control, job dispatching and request redirection. As the external workload and the internal resource behavior of the modern system is highly complex and variable, self-adaptive techniques require a stable vision of the system behavior. In this paper we propose a trend model that guarantees a robust interpretation for load-aware decision algorithms. Various experimental results in a Web cluster demonstrate that the proposed models and algorithms guarantee better stability of the load and a reduction of the response time experienced by the users.