{"title":"Scaling Down Off-the-Shelf Data Compression: Backwards-Compatible Fine-Grain Mixing","authors":"Michael Gray, P. Peterson, P. Reiher","doi":"10.1109/ICDCS.2012.21","DOIUrl":null,"url":null,"abstract":"Pu and Singaravelu presented Fine-Grain Mixing, an adaptive compression system which aimed to maximize CPU and network utilization simultaneously by splitting a network stream into a mixture of compressed and uncompressed blocks. Blocks were compressed opportunistically in a send buffer, they compressed as many blocks as they could without becoming a bottleneck. They successfully utilized all available CPU and network bandwidth even on high speed connections. In addition, they noted much greater throughput than previous adaptive compression systems. Here, we take a different view of FG-Mixing than was taken by Pu and Singaravelu and give another explanation for its high performance: that fine-grain mixing of compressed and uncompressed blocks enables off-the-shelf compressors to scale down their degree of compression linearly with decreasing CPU usage. Exploring the scaling behavior in-depth allows us to make a variety of improvements to fine-grain mixed compression: better compression ratios for a given level of CPU consumption, a wider range of data reduction and CPU cost options, and parallelized compression to take advantage of multi-core CPUs. We make full compatibility with the ubiquitous deflate decompress or (as used in many network protocols directly, or as the back-end of the gzip and Zip formats) a primary goal, rather than using a special, incompatible protocol as in the original implementation of FG-Mixing. Moreover, we show that the benefits of fine-grain mixing are retained by our compatible version.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"42 1","pages":"112-121"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 32nd International Conference on Distributed Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS.2012.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Pu and Singaravelu presented Fine-Grain Mixing, an adaptive compression system which aimed to maximize CPU and network utilization simultaneously by splitting a network stream into a mixture of compressed and uncompressed blocks. Blocks were compressed opportunistically in a send buffer, they compressed as many blocks as they could without becoming a bottleneck. They successfully utilized all available CPU and network bandwidth even on high speed connections. In addition, they noted much greater throughput than previous adaptive compression systems. Here, we take a different view of FG-Mixing than was taken by Pu and Singaravelu and give another explanation for its high performance: that fine-grain mixing of compressed and uncompressed blocks enables off-the-shelf compressors to scale down their degree of compression linearly with decreasing CPU usage. Exploring the scaling behavior in-depth allows us to make a variety of improvements to fine-grain mixed compression: better compression ratios for a given level of CPU consumption, a wider range of data reduction and CPU cost options, and parallelized compression to take advantage of multi-core CPUs. We make full compatibility with the ubiquitous deflate decompress or (as used in many network protocols directly, or as the back-end of the gzip and Zip formats) a primary goal, rather than using a special, incompatible protocol as in the original implementation of FG-Mixing. Moreover, we show that the benefits of fine-grain mixing are retained by our compatible version.