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      Discriminative fine-grained mixing for adaptive compression of data streams

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      Author(s)
      Gedik, B.
      Date
      2014
      Source Title
      IEEE Transactions on Computers
      Print ISSN
      0018-9340
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      63
      Issue
      9
      Pages
      2228 - 2244
      Language
      English
      Type
      Article
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      Abstract
      This paper introduces an adaptive compression algorithm for transfer of data streams across operators in stream processing systems. The algorithm is adaptive in the sense that it can adjust the amount of compression applied based on the bandwidth, CPU, and workload availability. It is discriminative in the sense that it can judiciously apply partial compression by selecting a subset of attributes that can provide good reduction in the used bandwidth at a low cost. The algorithm relies on the significant differences that exist among stream attributes with respect to their relative sizes, compression ratios, compression costs, and their amenability to application of custom compressors. As part of this study, we present a modeling of uniform and discriminative mixing, and provide various greedy algorithms and associated metrics to locate an effective setting when model parameters are available at run-time. Furthermore, we provide online and adaptive algorithms for real-world systems in which system parameters that can be measured at run-time are limited. We present a detailed experimental study that illustrates the superiority of discriminative mixing over uniform mixing. © 2013 IEEE.
      Keywords
      Adaptive compression
      Stream compression
      Adaptive algorithms
      Algorithms
      Bandwidth
      Bandwidth compression
      Data communication systems
      Data transfer
      Mixing
      Online systems
      Parameter estimation
      Real time systems
      Greedy algorithms
      Model parameters
      Partial compressions
      Real-world system
      Relative sizes
      Data compression
      Permalink
      http://hdl.handle.net/11693/25425
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TC.2013.103
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      • Department of Computer Engineering 1561
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