Amanda RaybuckThe University of Texas at Austin, Wei ZhangMicrosoft, Kayvan MansoorshahiThe University of Texas at Austin, Aditya K. KamathUniversity of Washington, Mattan ErezThe University of Texas at Austin, Simon PeterUniversity of Washington
{"title":"MaxMem:分级主存服务器上大数据应用的托管和性能","authors":"Amanda RaybuckThe University of Texas at Austin, Wei ZhangMicrosoft, Kayvan MansoorshahiThe University of Texas at Austin, Aditya K. KamathUniversity of Washington, Mattan ErezThe University of Texas at Austin, Simon PeterUniversity of Washington","doi":"arxiv-2312.00647","DOIUrl":null,"url":null,"abstract":"We present MaxMem, a tiered main memory management system that aims to\nmaximize Big Data application colocation and performance. MaxMem uses an\napplication-agnostic and lightweight memory occupancy control mechanism based\non fast memory miss ratios to provide application QoS under increasing\ncolocation. By relying on memory access sampling and binning to quickly\nidentify per-process memory heat gradients, MaxMem maximizes performance for\nmany applications sharing tiered main memory simultaneously. MaxMem is designed\nas a user-space memory manager to be easily modifiable and extensible, without\ncomplex kernel code development. On a system with tiered main memory consisting\nof DRAM and Intel Optane persistent memory modules, our evaluation confirms\nthat MaxMem provides 11% and 38% better throughput and up to 80% and an order\nof magnitude lower 99th percentile latency than HeMem and Linux AutoNUMA,\nrespectively, with a Big Data key-value store in dynamic colocation scenarios.","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"19 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MaxMem: Colocation and Performance for Big Data Applications on Tiered Main Memory Servers\",\"authors\":\"Amanda RaybuckThe University of Texas at Austin, Wei ZhangMicrosoft, Kayvan MansoorshahiThe University of Texas at Austin, Aditya K. KamathUniversity of Washington, Mattan ErezThe University of Texas at Austin, Simon PeterUniversity of Washington\",\"doi\":\"arxiv-2312.00647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present MaxMem, a tiered main memory management system that aims to\\nmaximize Big Data application colocation and performance. MaxMem uses an\\napplication-agnostic and lightweight memory occupancy control mechanism based\\non fast memory miss ratios to provide application QoS under increasing\\ncolocation. By relying on memory access sampling and binning to quickly\\nidentify per-process memory heat gradients, MaxMem maximizes performance for\\nmany applications sharing tiered main memory simultaneously. MaxMem is designed\\nas a user-space memory manager to be easily modifiable and extensible, without\\ncomplex kernel code development. On a system with tiered main memory consisting\\nof DRAM and Intel Optane persistent memory modules, our evaluation confirms\\nthat MaxMem provides 11% and 38% better throughput and up to 80% and an order\\nof magnitude lower 99th percentile latency than HeMem and Linux AutoNUMA,\\nrespectively, with a Big Data key-value store in dynamic colocation scenarios.\",\"PeriodicalId\":501333,\"journal\":{\"name\":\"arXiv - CS - Operating Systems\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Operating Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2312.00647\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Operating Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2312.00647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MaxMem: Colocation and Performance for Big Data Applications on Tiered Main Memory Servers
We present MaxMem, a tiered main memory management system that aims to
maximize Big Data application colocation and performance. MaxMem uses an
application-agnostic and lightweight memory occupancy control mechanism based
on fast memory miss ratios to provide application QoS under increasing
colocation. By relying on memory access sampling and binning to quickly
identify per-process memory heat gradients, MaxMem maximizes performance for
many applications sharing tiered main memory simultaneously. MaxMem is designed
as a user-space memory manager to be easily modifiable and extensible, without
complex kernel code development. On a system with tiered main memory consisting
of DRAM and Intel Optane persistent memory modules, our evaluation confirms
that MaxMem provides 11% and 38% better throughput and up to 80% and an order
of magnitude lower 99th percentile latency than HeMem and Linux AutoNUMA,
respectively, with a Big Data key-value store in dynamic colocation scenarios.
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