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IBM Research

Policy Transformation Techniques in Policy-based System Management

Mandis Beigi, Seraphin Calo and Dinesh Vermamandis, scalo, dverma @ us.ibm.com

IBM ResearchJune 7, 2004

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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)

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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

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Policy-based System Management

Policies

Policy Repository

Policy Decision Point

Policy Enforcement

Point

Policy

Management Tool

Policies

Actions

Policies

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Policy Transformation Enablement

Configuration Policies

Policy Repository

Policy Decision Point

Policy Enforcement

Point

Policy Management Tool

Policy Transformation

Module

Goal Policies

Actions

Policies

Case Database

Static Transformation Rules

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Existing Approaches

Analytical ModelsNeed model of the systemNeed to solve for model parametersNeed to make simplifying assumptionsDrawback - Exact models do not exist for real-life environments

Online Adaptive ControlUsing concepts from control theoryDevelop a neural network modelDrawback - Discipline specific

Simulation ApproachModel the system using a simulatorDrawback – Discipline specific

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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 9.10.3.0/24, then reserve a bandwidth of

20 Mbps and provide an encryption of 128 bits

Transformation Rules:1. Schwab user is on the 9.10.3.0/24 subnet 2. Gold service is to provide a bandwidth of 20 Mbps and an

encryption of 128 bits

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Proposed Approach: Transformation based on table lookup

Transformation module holds a table of policies appropriate for the system

A

B

D

C

Config 1

Config 2

Hypercube for Incoming policy

Hypercube Representation of Input policy

Hypercube Representation of Output policies

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Proposed Approach: Transformation using case based reasoning

Applications using CBR:1. Diagnostics2. Planning3. 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

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Sample of a Case Database

Tier0 # of Disks

Tier1 # of Disks

Tier0 # of Nodes

Tier1 # of Nodes

User Response Time

1 4 1 2 0.039 sec3 2 2 4 0.029 sec2 3 2 4 0.082 sec4 1 1 2 0.015 sec2 1 3 3 0.042 sec2 4 1 2 0.053 sec1 2 2 4 0.032 sec3 2 3 4 0.098 sec

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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, G3Select E2, E4 and E5

Or give them different weights by sorting them according to most relevant to least relevant

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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

Search Strategy

Generation Scheme

Evaluation Measure

Complete Heuristic Non-deterministic

Accuracy

Consistency

Classic

Forward

Backward

Random

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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: ρ12 = ∑ x1 x2 / N σ1 σ2

2. Normalizing3. Data Unit Consistencies

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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

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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

Cluster 1

Cluster 3

Cluster 2 Config 2

Config1

Goal 3

Goal 2

Goal 1

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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

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Web-site Architecture

Web Presentation Server

Web Application Server

Database Server

Network

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Experiment

N: # of configuration parameters M: # of goal parameters k: # of clusters

N=21 M=16 k=data size/100

Each case:Generated 21 uniformly distributed random variablesMeasured the goal values

Generated 100,000 data points (cases)

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Components of the Transformation Module

Cases

HVWS Simulator

Transformation Module

Case Database

Data Preprocessing Component

Feature Reduction

Component

Clustering Component

Input policy (e.g. Configuration

values)

Output policy (e.g. Goal values)

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Accuracy of the System

Accuracy versus Data SizeN=21, M=16, k=dataSize/100

0100200300400500

DataSize

Dis

tanc

e

Euclidean distance PCA: M=16 -> M’=6 reduced M by 10 In real systems N can also be reduced

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Experiment Results

Cross correlation matrix Values between -1 and +1 10,000 cases

Configuration Knob Goal value Cross Correlation Value‘ThinkTime’ ‘SessionTime’ +0.949‘BackgroundUtilization’ ‘CPUUtilization’ +0.805

Tier0 # of Nodes

Tier1 # of Nodes

Tier2 # of Nodes

AverageResponseTime

0.08 0.18 0.13

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Online/Real Time Policy Transformation

Configuration Policies

Policy Repository

Policy Decision Point

Policy Enforcement

Point

Policy Management Tool

Policy Transformation

Module

Monitoring Module

Goal Policies

System Data

Actions

Policies

System Data

System Data

Case Database

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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

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Summary

Different types of transformation Discipline independent - generic solution Offline and online methods