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Method for proactive impact analysis of policy-based storage systems

  • US 7,519,624 B2
  • Filed: 11/16/2005
  • Issued: 04/14/2009
  • Est. Priority Date: 11/16/2005
  • Status: Expired due to Fees
First Claim
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1. A processor-implemented method for proactively assessing the impact of a user action on a network storage system that includes a database represented by states and policies, before execution of the user action, the method comprising:

  • classifying said polices in a plurality of policy classes;

    wherein said policies comprise;

    at least one individual entity-class policy defined only on instances of an entity class and holding on every instance of an entity class;

    at least one collection entity-class policy defined only on instances of an entity class and holding on a collection of instances from the entity class;

    at least one individual zone policy defined on attributes of zone instances of the network storage system and requiring evaluation over only one of an added zone and a modified zone;

    at least one collection zone policy defined on attributes of zone instances of the network storage system and requiring evaluation over multiple zones;

    receiving said user action;

    capturing a snapshot of the database states;

    maintaining intermediate states of said database states of said network storage system;

    maintaining attributes of said network storage system, wherein said attributes may include vendor, model, and operating system type;

    simulating the user action on the snapshot without applying changes to the database;

    selectively applying at least one of the policies to the snapshot;

    analyzing whether the simulated user action violates at least one of said applied policies, wherein said analyzing includes;

    predicting behaviour of resources of said network storage system using at least one independent resource model;

    generating intelligent data optimization structures each time the method is run;

    using the intelligent data optimization structures regarding evaluation of said simulated user action, using an independent caching substructure, an independent policy classification substructure, and an independent aggregation substructure;

    finding relevant policies and relevant regions affected by said simulated user action using said independent policy classification substructure;

    specifying said relevant policies in a high level specification language, wherein said high level specification language is selected from Ponder and Extensible Markup Language;

    exploiting data locality and commonality across different policies and across different evaluations using said independent caching substructure;

    performing evaluation of said classes of policies using said independent aggregation substructure; and

    creating a policy using a set of operations found within said policy classes;

    assessing an impact of at least one of said policies on a future event; and

    outputting visualization information regarding said impact of said user action on said network storage system over time.

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