{"title":"计算网格下客户机-服务器系统的期限调度研究","authors":"A. Takefusa, S. Matsuoka, H. Casanova, F. Berman","doi":"10.1109/HPDC.2001.945208","DOIUrl":null,"url":null,"abstract":"The Computational Grid is a promising platform for the deployment of various high-performance computing applications. A number of projects have addressed the idea of software as a service on the network. These systems usually implement client-server architectures with many servers running on distributed Grid resources and have commonly been referred to as network-enabled servers (NES). An important question is that of scheduling in this multi-client multi-server scenario. Note that in this context most requests are computationally intensive as they are generated by high-performance computing applications. The Bricks simulation framework has been developed and extensively used to evaluate scheduling strategies for NES systems. The authors first present recent developments and extensions to the Bricks simulation models. They discuss a deadline scheduling strategy that is appropriate for the multi-client multi-server case, and augment it with \"Load Correction\" and \"Fallback\" mechanisms which could improve the performance of the algorithm. We then give Bricks simulation results. The results show that future NES systems should use deadline scheduling with multiple fallbacks and it is possible to allow users to make a trade-off between failure-rate and cost by adjusting the level of conservatism of deadline scheduling algorithms.","PeriodicalId":304683,"journal":{"name":"Proceedings 10th IEEE International Symposium on High Performance Distributed Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"102","resultStr":"{\"title\":\"A study of deadline scheduling for client-server systems on the Computational Grid\",\"authors\":\"A. Takefusa, S. Matsuoka, H. Casanova, F. Berman\",\"doi\":\"10.1109/HPDC.2001.945208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Computational Grid is a promising platform for the deployment of various high-performance computing applications. A number of projects have addressed the idea of software as a service on the network. These systems usually implement client-server architectures with many servers running on distributed Grid resources and have commonly been referred to as network-enabled servers (NES). An important question is that of scheduling in this multi-client multi-server scenario. Note that in this context most requests are computationally intensive as they are generated by high-performance computing applications. The Bricks simulation framework has been developed and extensively used to evaluate scheduling strategies for NES systems. The authors first present recent developments and extensions to the Bricks simulation models. They discuss a deadline scheduling strategy that is appropriate for the multi-client multi-server case, and augment it with \\\"Load Correction\\\" and \\\"Fallback\\\" mechanisms which could improve the performance of the algorithm. We then give Bricks simulation results. The results show that future NES systems should use deadline scheduling with multiple fallbacks and it is possible to allow users to make a trade-off between failure-rate and cost by adjusting the level of conservatism of deadline scheduling algorithms.\",\"PeriodicalId\":304683,\"journal\":{\"name\":\"Proceedings 10th IEEE International Symposium on High Performance Distributed Computing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"102\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 10th IEEE International Symposium on High Performance Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPDC.2001.945208\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 10th IEEE International Symposium on High Performance Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPDC.2001.945208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A study of deadline scheduling for client-server systems on the Computational Grid
The Computational Grid is a promising platform for the deployment of various high-performance computing applications. A number of projects have addressed the idea of software as a service on the network. These systems usually implement client-server architectures with many servers running on distributed Grid resources and have commonly been referred to as network-enabled servers (NES). An important question is that of scheduling in this multi-client multi-server scenario. Note that in this context most requests are computationally intensive as they are generated by high-performance computing applications. The Bricks simulation framework has been developed and extensively used to evaluate scheduling strategies for NES systems. The authors first present recent developments and extensions to the Bricks simulation models. They discuss a deadline scheduling strategy that is appropriate for the multi-client multi-server case, and augment it with "Load Correction" and "Fallback" mechanisms which could improve the performance of the algorithm. We then give Bricks simulation results. The results show that future NES systems should use deadline scheduling with multiple fallbacks and it is possible to allow users to make a trade-off between failure-rate and cost by adjusting the level of conservatism of deadline scheduling algorithms.