To zip or not to zip: Effective resource usage for real-time compression

Danny Harnik, Ronen Kat, Oded Margalit, Dmitry Sotnikov, Avishay Traeger

Research output: Contribution to conferencePaperpeer-review

37 Scopus citations


Real-time compression for primary storage is quickly becoming widespread as data continues to grow exponentially, but adding compression on the data path consumes scarce CPU and memory resources on the storage system. Our work aims to mitigate this cost by introducing methods to quickly and accurately identify the data that will yield significant space savings when compressed. The first level of filtering that we employ is at the data set level (e.g., volume or file system), where we estimate the overall compressibility of the data at rest. According to the outcome, we may choose to enable or disable compression for the entire data set, or to employ a second level of finer-grained filtering. The second filtering scheme examines data being written to the storage system in an online manner and determines its compressibility. The first-level filtering runs in mere minutes while providing mathematically proven guarantees on its estimates. In addition to aiding in selecting which volumes to compress, it has been released as a public tool, allowing potential customers to determine the effectiveness of compression on their data and to aid in capacity planning. The second-level filtering has shown significant CPU savings (up to 35%) while maintaining compression savings (within 2%).

Original languageEnglish
Number of pages13
StatePublished - 1 Jan 2013
Externally publishedYes
Event11th USENIX Conference on File and Storage Technologies, FAST 2013 - San Jose, United States
Duration: 12 Feb 201315 Feb 2013


Conference11th USENIX Conference on File and Storage Technologies, FAST 2013
Country/TerritoryUnited States
CitySan Jose

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Computer Networks and Communications


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