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

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Voruganti, Kaladhar, Korupolu, Madhukar R., Singh, Aameek
• Primary Examiner(s): Vital; Pierre M.
• Assistant Examiner(s): LIAO, JASON G
• Application Number:
  US11/281,739
• Publication Number:
  US 20070112870A1
• Time in Patent Office: 1,245 Days
• Field of Search: 707/200, 718/104
• US Class Current: 1/1
• CPC Class Codes: G06F 3/0605 by facilitating the interaction with a user or administrator
• CPC Class Codes: G06F 3/0637 Permissions
• CPC Class Codes: G06F 3/067 Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]

Abstract

A system efficiently and proactively assesses the impact of user'"'"'s actions on a network storage system. The system generally operates on a storage area network that includes a database represented by states and policies, before the user action is executed. The system comprises a storage monitor that captures a snapshot of the database states. An impact analysis module of the system then applies a user action to the snapshot; and further selectively applies at least some of the policies to the snapshot. The impact analysis module simulates the user action on the snapshot without applying actually changes to the database, and further analyzes whether the simulated user action violates at least one applied policy. The system takes the appropriate action based on the result of the analysis.

Claims

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.

2. The method according to claim 1, wherein applying the policies includes applying the policies incrementally.

3. The method according to claim 1, further comprising if the simulated user action violates at least one applied policy, taking a corrective action.

4. The method according to claim 3, wherein taking the corrective action comprises changing at least one of the policies.

5. The method according to claim 1, further comprising if the simulated user action does not violate at least one applied policy, said network storage system taking an execution forwarding action.

6. The method according to claim 5, wherein taking said execution forwarding action comprises forwarding the user action to an action execution level.

7. The method according to claim 6, wherein forwarding the user action to the action execution level comprises performing a specific execution action.

8. The method according to claim 5, wherein taking said execution action comprises sending an analysis result to a user interface.

9. The method according to claim 5, wherein taking said execution action comprises logging an analysis result of said simulated user action.