© 2002 IBM Corporation IBM Research 1 Policy Transformation Techniques in Policy- based System Management Mandis Beigi, Seraphin Calo and Dinesh Verma mandis, scalo, us.ibm.com IBM Research June 7, 2004

IBM Research © 2003 IBM Corporation 2 Policy Transformation Advantages  Simplifies policy-based management  Hides complex policies from administrators  Provides business level abstractions Objective  Build a generic policy transformer  To be used by many disciplines  Bidirectional policy transformer –Business level to low level configuration –Low level configuration to business level (Policy Advisor)

IBM Research © 2003 IBM Corporation 3 Policy Transformation  Types of transformation –Offline – Uses static predefined rules –Real time/online – feedback loop  Transformation taken place in 2 places –At management tool – before placed onto repository –At decision point – before send to the enforcement points

IBM Research © 2003 IBM Corporation 4 Policy-based System Management

IBM Research © 2003 IBM Corporation 5 Policy Transformation Enablement

IBM Research © 2003 IBM Corporation 6 Existing Approaches  Analytical Models Need model of the system Need to solve for model parameters Need to make simplifying assumptions Drawback - Exact models do not exist for real-life environments  Online Adaptive Control Using concepts from control theory Develop a neural network model Drawback - Discipline specific  Simulation Approach Model the system using a simulator Drawback – Discipline specific

IBM Research © 2003 IBM Corporation 7 Proposed Approach: Transformation using static rules  Example: High Level policy: If the user is from Schwab, then provide Gold level service Low Level Policy: If the user is from the subnet /24, then reserve a bandwidth of 20 Mbps and provide an encryption of 128 bits Transformation Rules: 1.Schwab user is on the /24 subnet 2.Gold service is to provide a bandwidth of 20 Mbps and an encryption of 128 bits

IBM Research © 2003 IBM Corporation 8 Proposed Approach: Transformation based on table lookup Transformation module holds a table of policies appropriate for the system

IBM Research © 2003 IBM Corporation 9 Proposed Approach: Transformation using case based reasoning Applications using CBR: 1.Diagnostics 2.Planning 3.Prediction A table of cases is kept from past measurements or training set Data my consist:  Noise  Inconsistent cases  Multiple cases having the same outcome  Missing measurements

IBM Research © 2003 IBM Corporation 10 Sample of a Case Database Tier0 # of Disks Tier1 # of Disks Tier0 # of Nodes Tier1 # of Nodes User Response Time sec sec sec sec sec sec sec sec

IBM Research © 2003 IBM Corporation 11 Feature Selection  A typical system would have many configuration parameters and goal values How do you know which are the right set of configs and goals to include in the transform data? How does one eliminate unnecessary responses  Need to select a subset of best features from the monitored data E1, E2, E3, E4, E5, E6  G1, G2, G3 Select E2, E4 and E5 Or give them different weights by sorting them according to most relevant to least relevant

IBM Research © 2003 IBM Corporation 12 Feature Selection  Backward Generation Remove one feature at a time until desired accuracy is obtained  Complete Search Strategy Consider all possible combinations of features  Accuracy Evaluation Measured against a set of known test-cases

IBM Research © 2003 IBM Corporation 13 Data Pre-processing  Removes irrelevant or redundant data to make data simpler  Reduces computational overhead  Increases accuracy 3-step process: 1.Dimensionality reduction  By Feature Selection – Weights based on accuracy  Principal Component Analysis (PCA) Combines correlated axes  Cross Correlation Matrix – Direct relationship of variables Linearly dependent variables Correlation:ρ 12 = ∑ x 1 x 2 / N σ 1 σ 2 2.Normalizing 3.Data Unit Consistencies

IBM Research © 2003 IBM Corporation 14 Principal Component Analysis  Finds components that represent maximum variance  A set of correlated variables  a set of uncorrelated variables  Reduces dimension of data  Reduces noise  Example: Linear reduction of 2 dimensions to 1 dimension

IBM Research © 2003 IBM Corporation 15 Data Clustering  K-nearest neighbor clustering  Fixed or variable number of clusters  Robust to noise in data  Find the cluster with the smallest distance to lookup data point

IBM Research © 2003 IBM Corporation 16 Experiments and Results  IBM High Volume Web Site simulator  Multi-tiered web site  Different workload patterns 1.Online shopping 2.Trading 3.Reservations 4.Auctions  User session characteristics  Software/hardware characteristics per tier

IBM Research © 2003 IBM Corporation 17 Web-site Architecture

IBM Research © 2003 IBM Corporation 18 Experiment  N: # of configuration parameters  M: # of goal parameters  k: # of clusters N=21M=16k=data size/100 Each case: Generated 21 uniformly distributed random variables Measured the goal values Generated 100,000 data points (cases)

IBM Research © 2003 IBM Corporation 19 Components of the Transformation Module

IBM Research © 2003 IBM Corporation 20 Accuracy of the System  Euclidean distance  PCA:M=16 -> M’=6 reduced M by 10  In real systems N can also be reduced

IBM Research © 2003 IBM Corporation 21 Experiment Results  Cross correlation matrix  Values between -1 and +1  10,000 cases Configuration KnobGoal valueCross Correlation Value ‘ThinkTime’‘SessionTime’ ‘BackgroundUtilization’‘CPUUtilization’ Tier0 # of Nodes Tier1 # of Nodes Tier2 # of Nodes Average Response Time

IBM Research © 2003 IBM Corporation 22 Online/Real Time Policy Transformation

IBM Research © 2003 IBM Corporation 23 Online Transformation  Online monitoring component  Ensures objectives are being met  Configuration parameters are dynamically modified  Configuration parameters and objectives are measured  Builds case database  Useful for state dependent systems

IBM Research © 2003 IBM Corporation 24 Summary  Different types of transformation  Discipline independent - generic solution  Offline and online methods