{"title":"提高SSD性能的并行性和垃圾收集感知I/O调度器","authors":"Jiayang Guo, Yimin Hu, Bo Mao, Suzhen Wu","doi":"10.1109/IPDPS.2017.55","DOIUrl":null,"url":null,"abstract":"In this paper, we propose PGIS, a parallelism and garbage collection aware I/O Scheduler, which identifies the hot data based on trace characteristics to exploit the channel level internal parallelism of flash-based storage systems. PGIS not only fully exploits abundant channel resource in the SSD, but also it introduces a hot data identification mechanism to reduce the garbage collection overhead. By dispatching hot read data to different channel, the channel level internal parallelism is fully exploited. By dispatching hot write data to the same physical block, the garbage collection overhead has been alleviated. The experiment results show that compared with existing I/O schedulers, PGIS improves the response time and garbage collection performance significantly. Consequently, PGIS reduces the garbage collection overhead up to 30.9%, while exploiting channel level internal parallelism.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Parallelism and Garbage Collection Aware I/O Scheduler with Improved SSD Performance\",\"authors\":\"Jiayang Guo, Yimin Hu, Bo Mao, Suzhen Wu\",\"doi\":\"10.1109/IPDPS.2017.55\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose PGIS, a parallelism and garbage collection aware I/O Scheduler, which identifies the hot data based on trace characteristics to exploit the channel level internal parallelism of flash-based storage systems. PGIS not only fully exploits abundant channel resource in the SSD, but also it introduces a hot data identification mechanism to reduce the garbage collection overhead. By dispatching hot read data to different channel, the channel level internal parallelism is fully exploited. By dispatching hot write data to the same physical block, the garbage collection overhead has been alleviated. The experiment results show that compared with existing I/O schedulers, PGIS improves the response time and garbage collection performance significantly. Consequently, PGIS reduces the garbage collection overhead up to 30.9%, while exploiting channel level internal parallelism.\",\"PeriodicalId\":209524,\"journal\":{\"name\":\"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPDPS.2017.55\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2017.55","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallelism and Garbage Collection Aware I/O Scheduler with Improved SSD Performance
In this paper, we propose PGIS, a parallelism and garbage collection aware I/O Scheduler, which identifies the hot data based on trace characteristics to exploit the channel level internal parallelism of flash-based storage systems. PGIS not only fully exploits abundant channel resource in the SSD, but also it introduces a hot data identification mechanism to reduce the garbage collection overhead. By dispatching hot read data to different channel, the channel level internal parallelism is fully exploited. By dispatching hot write data to the same physical block, the garbage collection overhead has been alleviated. The experiment results show that compared with existing I/O schedulers, PGIS improves the response time and garbage collection performance significantly. Consequently, PGIS reduces the garbage collection overhead up to 30.9%, while exploiting channel level internal parallelism.