{"title":"最小化不相关机器的流程时间","authors":"N. Bansal, Janardhan Kulkarni","doi":"10.1145/2746539.2746601","DOIUrl":null,"url":null,"abstract":"We consider some classical flow-time minimization problems in the unrelated machines setting. In this setting, there is a set of m machines and a set of n jobs, and each job j has a machine dependent processing time of pij on machine i. The flow-time of a job is the amount of time the job spends in a system (its completion time minus its arrival time), and is one of the most natural measure of quality of service. We show the following two results: an $O(min(log2 n, log n log P)) approximation algorithm for minimizing the total flow-time, and an O(log n) approximation for minimizing the maximum flow-time. Here P is the ratio of maximum to minimum job size. These are the first known poly-logarithmic guarantees for both the problems.","PeriodicalId":20566,"journal":{"name":"Proceedings of the forty-seventh annual ACM symposium on Theory of Computing","volume":"142 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2014-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Minimizing Flow-Time on Unrelated Machines\",\"authors\":\"N. Bansal, Janardhan Kulkarni\",\"doi\":\"10.1145/2746539.2746601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider some classical flow-time minimization problems in the unrelated machines setting. In this setting, there is a set of m machines and a set of n jobs, and each job j has a machine dependent processing time of pij on machine i. The flow-time of a job is the amount of time the job spends in a system (its completion time minus its arrival time), and is one of the most natural measure of quality of service. We show the following two results: an $O(min(log2 n, log n log P)) approximation algorithm for minimizing the total flow-time, and an O(log n) approximation for minimizing the maximum flow-time. Here P is the ratio of maximum to minimum job size. These are the first known poly-logarithmic guarantees for both the problems.\",\"PeriodicalId\":20566,\"journal\":{\"name\":\"Proceedings of the forty-seventh annual ACM symposium on Theory of Computing\",\"volume\":\"142 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the forty-seventh annual ACM symposium on Theory of Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2746539.2746601\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the forty-seventh annual ACM symposium on Theory of Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2746539.2746601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
摘要
考虑了一些经典的不相关机器设置下的流时间最小化问题。在此设置中,有一组m台机器和一组n个作业,每个作业j在机器i上的处理时间pij与机器相关。作业的流时间是作业在系统中花费的时间(其完成时间减去其到达时间),是最自然的服务质量度量之一。我们展示了以下两个结果:一个$O(min(log2 n, log n log P))近似算法用于最小化总流时间,一个$O(log n)近似算法用于最小化最大流时间。这里P是最大作业大小与最小作业大小的比值。这是已知的第一个针对这两个问题的多对数保证。
We consider some classical flow-time minimization problems in the unrelated machines setting. In this setting, there is a set of m machines and a set of n jobs, and each job j has a machine dependent processing time of pij on machine i. The flow-time of a job is the amount of time the job spends in a system (its completion time minus its arrival time), and is one of the most natural measure of quality of service. We show the following two results: an $O(min(log2 n, log n log P)) approximation algorithm for minimizing the total flow-time, and an O(log n) approximation for minimizing the maximum flow-time. Here P is the ratio of maximum to minimum job size. These are the first known poly-logarithmic guarantees for both the problems.