{"title":"为位置透明的调度网格需求提供光路和计算资源","authors":"Hong-Ha Nguyen, Mohan Gurusamy, Luying Zhou","doi":"10.1109/ONDM.2008.4578408","DOIUrl":null,"url":null,"abstract":"In this paper, we define a new problem of provisioning lightpaths and computing resources for a set of location- transparent scheduled grid demands in optical grid networks. A location-transparent scheduled grid demand specifies only an amount of computing resources needed in a specified time interval to process input data. The network node generating a demand is called a client node. There are several network nodes which have sufficient resources for a demand. These nodes are called resource nodes. An algorithm is used to choose a resource node to reserve a specified amount of computing resources and provision a lightpath between the resource node and the client node. Given a set of location-transparent scheduled grid demands, it is required to provision the best lightpath (i.e. wavelength resources) as well as computing resources available during the specified time interval for each demand so as to optimize a certain objective function. In our work, we develop integer linear programming (ILP) formulations for 2 objective functions: 1) Given a network capacity, maximize the number of demands accepted; 2) Minimize the total number of wavelength-links to honor a given set of demands. Because the ILP algorithms are computationally expensive, we also develop heuristics to deal with large networks. The simulation results show that our heuristics achieve good performance.","PeriodicalId":155835,"journal":{"name":"2008 International Conference on Optical Network Design and Modeling","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Provisioning lightpaths and computing resources for location-transparent scheduled grid demands\",\"authors\":\"Hong-Ha Nguyen, Mohan Gurusamy, Luying Zhou\",\"doi\":\"10.1109/ONDM.2008.4578408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we define a new problem of provisioning lightpaths and computing resources for a set of location- transparent scheduled grid demands in optical grid networks. A location-transparent scheduled grid demand specifies only an amount of computing resources needed in a specified time interval to process input data. The network node generating a demand is called a client node. There are several network nodes which have sufficient resources for a demand. These nodes are called resource nodes. An algorithm is used to choose a resource node to reserve a specified amount of computing resources and provision a lightpath between the resource node and the client node. Given a set of location-transparent scheduled grid demands, it is required to provision the best lightpath (i.e. wavelength resources) as well as computing resources available during the specified time interval for each demand so as to optimize a certain objective function. In our work, we develop integer linear programming (ILP) formulations for 2 objective functions: 1) Given a network capacity, maximize the number of demands accepted; 2) Minimize the total number of wavelength-links to honor a given set of demands. Because the ILP algorithms are computationally expensive, we also develop heuristics to deal with large networks. The simulation results show that our heuristics achieve good performance.\",\"PeriodicalId\":155835,\"journal\":{\"name\":\"2008 International Conference on Optical Network Design and Modeling\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Optical Network Design and Modeling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ONDM.2008.4578408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Optical Network Design and Modeling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ONDM.2008.4578408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Provisioning lightpaths and computing resources for location-transparent scheduled grid demands
In this paper, we define a new problem of provisioning lightpaths and computing resources for a set of location- transparent scheduled grid demands in optical grid networks. A location-transparent scheduled grid demand specifies only an amount of computing resources needed in a specified time interval to process input data. The network node generating a demand is called a client node. There are several network nodes which have sufficient resources for a demand. These nodes are called resource nodes. An algorithm is used to choose a resource node to reserve a specified amount of computing resources and provision a lightpath between the resource node and the client node. Given a set of location-transparent scheduled grid demands, it is required to provision the best lightpath (i.e. wavelength resources) as well as computing resources available during the specified time interval for each demand so as to optimize a certain objective function. In our work, we develop integer linear programming (ILP) formulations for 2 objective functions: 1) Given a network capacity, maximize the number of demands accepted; 2) Minimize the total number of wavelength-links to honor a given set of demands. Because the ILP algorithms are computationally expensive, we also develop heuristics to deal with large networks. The simulation results show that our heuristics achieve good performance.