Joshua S. Auerbach, D. F. Bacon, P. Cheng, D. Grove, Ben Biron, Charlie Gracie, Bill McCloskey, Aleksandar Micic, Ryan Sciampacone
{"title":"Tax-and-spend: democratic scheduling for real-time garbage collection","authors":"Joshua S. Auerbach, D. F. Bacon, P. Cheng, D. Grove, Ben Biron, Charlie Gracie, Bill McCloskey, Aleksandar Micic, Ryan Sciampacone","doi":"10.1145/1450058.1450092","DOIUrl":null,"url":null,"abstract":"Real-time Garbage Collection (RTGC) has recently advanced to the point where it is being used in production for financial trading, military command-and-control, and telecommunications. However, among potential users of RTGC, there is enormous diversity in both application requirements and deployment environments.\n Previously described RTGCs tend to work well in a narrow band of possible environments, leading to fragile systems and limiting adoption of real-time garbage collection technology.\n This paper introduces a collector scheduling methodology called tax-and-spend and the collector design revisions needed to support it. Tax-and-spend provides a general mechanism which works well across a variety of application, machine, and operating system configurations. Tax-and-spend subsumes the predominant pre-existing RTGC scheduling techniques. It allows different policies to be applied in different contexts depending on the needs of the application. Virtual machines can co-exist compositionally on a single machine.\n We describe the implementation of our system, Metronome-TS, as an extension of the Metronome collector in IBM's Real-time J9 virtual machine product, and we evaluate it running on an 8-way SMP blade with a real-time Linux kernel. Compared to the state-of-the-art Metronome system on which it is based, implemented in the identical infrastructure, it achieves almost 3x shorter latencies, comparable utilization at a 2.5x shorter time window, and mean throughput improvements of 10-20%.","PeriodicalId":143573,"journal":{"name":"International Conference on Embedded Software","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Embedded Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1450058.1450092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 37
Abstract
Real-time Garbage Collection (RTGC) has recently advanced to the point where it is being used in production for financial trading, military command-and-control, and telecommunications. However, among potential users of RTGC, there is enormous diversity in both application requirements and deployment environments.
Previously described RTGCs tend to work well in a narrow band of possible environments, leading to fragile systems and limiting adoption of real-time garbage collection technology.
This paper introduces a collector scheduling methodology called tax-and-spend and the collector design revisions needed to support it. Tax-and-spend provides a general mechanism which works well across a variety of application, machine, and operating system configurations. Tax-and-spend subsumes the predominant pre-existing RTGC scheduling techniques. It allows different policies to be applied in different contexts depending on the needs of the application. Virtual machines can co-exist compositionally on a single machine.
We describe the implementation of our system, Metronome-TS, as an extension of the Metronome collector in IBM's Real-time J9 virtual machine product, and we evaluate it running on an 8-way SMP blade with a real-time Linux kernel. Compared to the state-of-the-art Metronome system on which it is based, implemented in the identical infrastructure, it achieves almost 3x shorter latencies, comparable utilization at a 2.5x shorter time window, and mean throughput improvements of 10-20%.