Policy-based management system with automatic policy selection and creation capabilities by using singular value decomposition technique

Abstract

A statistical approach implementing Singular Value Decomposition (SVD) to a policy-based management system for autonomic and on-demand computing applications. The statistical approach empowers a class of applications that require policies to handle ambiguous conditions and allow the system to “evolve” in response to changing operation and environment conditions. In the system and method providing the statistical approach, observed event-policy associated data, which is represented by an event-policy matrix, is treated as a statistical problem with the assumption that there are some underlying or implicit higher order correlations among events and policies. The SVD approach enables such correlations to be modeled, extracted and modified. From these correlations, recommended policies can be selected or created without exact match of policy conditions. With a feedback mechanism, new knowledge can be acquired as new situations occur and the corresponding policies to manage them are recorded and used to generate new event and policy correlations. Consequently, based on these new correlations, new recommended policies can be derived.

1 . An adaptive policy-based management system for computing systems comprising:
 a means for representing the occurrences of computer system events and action response policies from computing system resources into a first event-policy data structure;   a means for constructing a second event-policy data structure from said first event-policy data structure, said second event-policy data structure representing an event-policy vector space comprising associative patterns and correlations in the event-policy data;   a means for receiving observed event data set from a computing system resource;   a means for recommending a policy for said observed event data set based on existing policy vectors in said constructed event-policy vector space;   a means enabling updating of said first event-policy data structure and said second event-policy data structure representing said event-policy vector space as new observed event data sets are received, thereby increasing accuracy in generating recommended policies as new event knowledge is input.   
   
   
       2 . The adaptive policy-based management system as claimed in  claim 1 , further comprising:
 a means for storing received observed data event sets and corresponding action response policies from computing system resources;   an interface device for enabling a user to review and modify a recommended policy for said observed event data set;   a means for executing a recommended policy and determining that policy's effectiveness for managing said observed event data set; and,   if said executed recommended policy is determined effective, updating said storing means with said received observed data event sets and corresponding modified response policies.   
   
   
       3 . The adaptive policy-based management system as claimed in  claim 1 , wherein said means for recommending a policy for said observed event data set comprises:
 a means for constructing a pseudo-policy vector for an observed event set from data in said event-policy vector space; and,   a means for determining a recommended policy based on proximity of said pseudo-policy vector and existing policy vectors included in said event-policy vector space.   
   
   
       4 . The adaptive policy-based management system as claimed in  claim 3 , wherein said means for determining a recommended policy comprises means for applying a similarity metric between said pseudo-policy vector and one or more policy vectors. 
   
   
       5 . The adaptive policy-based management system as claimed in  claim 4 , wherein said applied similarity metric includes a dot product function, said recommended policy comprising a policy vector based on a resulting dot product value within a threshold value. 
   
   
       6 . The adaptive policy-based management system as claimed in  claim 5 , wherein more than one policy vectors provide dot product values below said threshold value, said system further comprising means for merging said one or more policy vectors to form a resultant recommended policy. 
   
   
       7 . The adaptive policy-based management system as claimed in  claim 3 , wherein said means for constructing a pseudo-policy vector for an observed event data set comprises obtaining a centroid of said event data points in said observed event data set and generating an event vector corresponding to said centroid. 
   
   
       8 . The adaptive policy-based management system as claimed in  claim 1 , wherein said first event-policy data structure comprises an event-policy matrix, said means for constructing a second event-policy data structure from said first event-policy data structure comprises means for implementing Singular Value Decomposition (SVD)] function on said event-policy matrix. 
   
   
       9 . The adaptive policy-based management system as claimed in  claim 1 , wherein said observed event data set from a computing system resource comprises one or more of: system faults, system status or performance information of said resources. 
   
   
       10 . The adaptive policy-based management system as claimed in  claim 1 , wherein said observed event data set includes a new event or new event patterns for which no existing policy matching condition exists. 
   
   
       11 . A method for policy-based management of computing systems, said method comprising:
 representing the occurrences of computer system events and action response policies from computing system resources into a first event-policy data structure;   constructing a second event-policy data structure from said first event-policy data structure, said second event-policy data structure representing an event-policy vector space comprising associative patterns and correlations in the event-policy data;   receiving observed event data set from a computing system resource;   recommending a policy for said observed event data set based on existing policy vectors in said constructed event-policy vector space;   enabling updating of said first event-policy data structure and said second event-policy data structure representing said event-policy vector space as new observed event data sets are received, thereby increasing accuracy in generating recommended policies as new event knowledge is input.   
   
   
       12 . The method as claimed in  claim 11 , further comprising:
 storing, in a data storage device, received observed data event sets and corresponding action response policies from computing system resources;   enabling a user to review and modify a recommended policy for said observed event data set via an interface;   executing a recommended policy and determining a policy's effectiveness for managing said observed event data set; and,   if said executed recommended policy is determined effective, updating said storing means with said received observed data event sets and corresponding modified response policies.   
   
   
       13 . The method as claimed in  claim 11 , wherein said recommending a policy for said observed event data set comprises:
 constructing a pseudo-policy vector for an observed event set from data in said event-policy vector space; and,   determining a recommended policy based on proximity of said pseudo-policy vector and existing policy vectors included in said event-policy vector space.   
   
   
       14 . The method as claimed in  claim 13 , wherein said determining a recommended policy comprises: applying a similarity metric between said pseudo-policy vector and one or more policy vectors. 
   
   
       15 . The method as claimed in  claim 14 , wherein said applied similarity metric includes a dot product function, said recommended policy comprising a policy vector based on a resulting dot product value within a threshold value. 
   
   
       16 . The method as claimed in  claim 15 , wherein more than one policy vectors provide dot product values below said threshold value, said method further comprising: merging said one or more policy vectors to form a resultant recommended policy. 
   
   
       17 . The method as claimed in  claim 11 , wherein said first event-policy data structure comprises an event-policy matrix, said constructing a second event-policy data structure from said first event-policy data structure comprises implementing a Singular Value Decomposition (SVD)] function on said event-policy matrix. 
   
   
       18 . A program storage device tangibly embodying software instructions which are adapted to be executed by a computing device to perform a method for policy-based management of computing systems according to  claim 13 . 
   
   
       19 . A method for creating new policies for automated decision-making, the method comprising:
 creating a correlation matrix having entries reflecting the correlation, in a set of existing policies, between a plurality of events and/or circumstances and a plurality of policies;   determining existence of a match between an observed set of events and/or circumstances against the entries in the correlation matrix, and,   if there is no exact match between the observed set of events and/or circumstances and the entries in said correlation matrix, then,   utilizing a singular-value decomposition (SVD) technique for constructing a new policy responsive to the observed set of events and/or circumstances and the correlation matrix.   
   
   
       20 . The method as in  claim 19 , further comprising: updating the correlation matrix to include the newly-constructed policy.