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Machine learning for metadata cache management

  • US 10,191,857 B1
  • Filed: 08/22/2017
  • Issued: 01/29/2019
  • Est. Priority Date: 01/09/2014
  • Status: Active Grant
First Claim
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1. A method comprising:

  • measuring, for each of a plurality of address spaces, an amount of randomness in a plurality of accesses to the plurality of address spaces; and

    evicting metadata stored in a cache that is associated with an address space corresponding to a measured amount of randomness that is greater than a particular threshold;

    wherein;

    measuring said amount of randomness comprises;

    capturing a plurality of addresses from the plurality of accesses;

    generating a first frequency domain representation of a first plurality of addresses from the captured plurality of addresses, wherein the first plurality of addresses correspond to a first region of the logical address space, and wherein the first frequency domain representation has a first frequency distribution;

    measuring an amount of randomness in the first frequency distribution by adding together frequency component values above a first cutoff frequency in the first frequency distribution;

    identifying the first region as a relatively low random region responsive to determining the frequency component values above the first cutoff frequency are less than a first threshold; and

    identifying the first region as a relatively high random region responsive to determining the frequency component values above the first cutoff frequency are greater than a first threshold;

    wherein the plurality of accesses target a logical address space.

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