Computer Genomics: Towards Self- Change and Configuration Management
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Computer Genomics: Towards Self- Change and Configuration Management ( http://research.microsoft.com/sn/strider). Yi-Min Wang Senior Researcher & Group Manager Systems Management Research Group ( http://research.microsoft.com/sm/ ). OUTLINE. Change & Configuration Management

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Computer Genomics: Towards Self- Change and Configuration Management(http://research.microsoft.com/sn/strider)

Yi-Min Wang

Senior Researcher & Group Manager

Systems Management Research Group

(http://research.microsoft.com/sm/)


Outline
OUTLINE Management

  • Change & Configuration Management

  • Genomics & Computer Genomics

  • What We’ve Learned From The Analogy

  • Systems Management

    • Configuration Troubleshooting

    • Patch Impact Analysis

    • Spyware Management

  • Towards Self-Management


Change configuration management

Management

Spyware

Download

Setting

Change

Patching

Change & Configuration Management

  • Problem Scope

    • Setting changes through Control Panel, program executions, etc.

    • Software installations, updates, and patching

    • Drive-by downloads of spyware

O(101) to O(102)

processes

O(105) Registry

entries and files


Configuration errors

Persistent Management

Configuration

Settings

Aging

Volatile

State

Executable

Files

Process

App Reinstallation

System Restore

OS Re-imaging

Patching

Configuration Errors

  • Persistent: cannot be solved by restart / reboot

  • A major contributor to Internet service unavailability and computer user frustration

App Restart

Rejuvenation

Machine Reboot


Genomics computer genomics
Genomics & Computer Genomics Management

  • “A”, “C”, “G”, and “T” are the four DNA letters of the genetic alphabet

    • “1” and “0” are the binary letters of the computer genetic alphabet

  • 3 billion base pairs arranged into 24 distinct chromosomes

    • Windows Registry is typically 50MB (or 400 mega bits) arranged into several hives


  • Gene Management: a stretch of sequence in a specific position on a DNA strand

    • Computer gene: a Registry entry (a stretch of bit sequence) in a specific position of a hive identified by a hierarchical path name

  • Gene carries the instructions for making a particular protein through gene expression

    • Registry entry carries the instructions for configuring a particular process instantiation


  • Less than 2 percent of the human genome is made up of protein-coding sequences

  • The rest labeled as ‘junk’ DNA

    • A lot of Registry entries are not configuration settings, but rather “operational states” such as usage counts, most recently used files, etc.

    • They can be labeled as ‘junk’ entries as far as configuration management is concerned


  • Any two persons’ genome is >99.9% identical protein-coding sequences

    • Registry snapshots from two different days on the same machine typically have about 99% of the entries identical between them

  • Even between mouse and human genes, the similarities range from 70% to 90%

    • Even across different machines, there is a high degree of similarity


  • Majority of variations in the genome sequence simply create diversity

    • Majority of variations in Registry simply reflect diversity in hardware/software installation and user preferences

  • But some genetic differences are responsible for causing diseases: the gene for Huntington’s disease was found at the tip of the short arm of Chromosome 4

    • Some differences in Registry data are responsible for configuration problems.

    • For example, the gene for the “Short-cuts-do-not- work” problem was found at the following Registry location:HKEY_CLASSES_ROOT\CLSID\{00021401-0000-0000-C000-000000000046}\shellex\MayChangeDefaultMenu


Huntington’s Gene & Human Chromosomes diversity

http://www.hdsa-wi.org/chromosomes.gif

Short-cuts-do-not-work’s Gene


  • Most diseases involve the interaction of several genes diversity

  • Studies have shown irrefutable evidence of the role environment plays in gene expression

    • Studies of Registry problems reveal that the “healthy” or “sick” values of many entries are not absolute on their own and very often depend on the environment of individual machines


  • Gene therapy can potentially treat diseases by using normal genes to replace a defective gene

  • But some failed experiments have shown the risk of unexpected side effects of creating new diseases

    • The equivalent of gene therapy can be easily performed with a Registry or file editor

    • But direct modifications to these low-level state information can potentially cause inconsistency and lead to more serious problems


What we ve learned from the analogy
What We’ve Learned From The Analogy genes to replace a defective gene

  • Configuration problems are solvable

    • One order of magnitude easier than the genomics problem

  • Techniques for complexity reduction

    • Noise filtering through “junk” labeling

    • Diff can be very powerful: two orders of magnitude reduction

    • Attack the Mess with the Mass: statistical analysis across multiple machines

  • Computer Genomics Database for problem detection and repair

    • Problems with known root causes: which gene causes which problem and how to fix it

    • Problems with unknown root causes: which action should be tried to provide safe gene therapy


No 1 configuration troubleshooting
No.1: Configuration Troubleshooting genes to replace a defective gene

  • “It worked yesterday, but not today.”

  • “It worked for that user, but not this user.”

  • “It worked on that machine, but not this machine.”

  • “I restarted the application, rebooted the machine, but still can’t fix the problem!”


Strider process for configuration troubleshooting

The program genes to replace a defective gene

keeps failing

Support

Articles

Config

Action

UI

App

Info

Doc

Tracing

State Diff

Intersection

Support Database

Lookup

Ownership

Mapping

PC

Genomics

Database

Noise Filtering

State Ranking

Filtered & Ranked

Candidate Set

Strider Process for Configuration Troubleshooting

Context Information Gathering phase

Complexity Reduction

Phase

It was

working

Now it

doesn’t

work

User

Tool


Cross restore point results

After diff & trace genes to replace a defective gene

intersection

Average Registry size

Two

Orders

Another

Two

Orders

Of

Magnitude

After state diff

Root cause

Order-ranking

After noise filtering

Cross-Restore-Point Results


No 2 patch impact analysis
No.2: Patch Impact Analysis genes to replace a defective gene

  • “If I apply this security patch, which one of the 3,000 applications in my company is going to be affected?”


Strider process for patch impact analysis

All Program genes to replace a defective gene

Executions

Applications Requiring

High-Priority Testing

Tracing

State Diff

Intersection

PC

Genomics

Database

Noise Filtering

(System Processes)

Process-to-Application Mapping

State Ranking

(Process Criticality)

Filtered & Ranked

Candidate Set

Strider Process for Patch Impact Analysis

Context Information Gathering phase

Complexity Reduction

Phase

Before

Patching

After

Patching

User

Tool


No 3 spyware management
No.3: Spyware Management genes to replace a defective gene

  • “I’m getting lots of pop-ups and my browser is crashing a lot. What software got installed on my machine?”


Strider process for spyware management

Objective Criteria Evaluation, Bundle genes to replace a defective gene

Information, & Support Articles

Reboot Machine

& Launch IE

Tracing

State Diff

Intersection

PC

Genomics

Database

Known-* Database

Lookup

Noise Filtering

(Known Goods)

State Ranking

(Behavior Criticality)

Filtered & Ranked

Candidate Set

Strider Process for Spyware Management

Context Information Gathering phase

Complexity Reduction

Phase

Before

Spyware

Infection

After

Spyware

Infection

User

Tool


Towards self management
Towards Self-Management genes to replace a defective gene

  • Flight Data Recorder (FDR)

    • Always-on tracing, diff’ing, intersection, noise filtering, and state ranking

    • Automatic genomic lookup for known problems

      • “Self-healing”, “known-bad”, and “wait for user complaint”

    • Automatic PeerPressure analysis for anomaly detection

    • Automatic generation of black-box application dependency database

    • Automatic trace analysis for new ASEP hooks

      • ASEP = Auto-Start Extensibility Point


Summary
Summary genes to replace a defective gene

  • The Strider Process for Handling Persistent-State Complexity

    • Diff

    • Trace

    • Intersection

    • Noise Filtering

    • State Ranking

    • Look-up


For more information google msr strider or http research microsoft com sn strider
For More Information genes to replace a defective geneGoogle “MSR Strider” or http://research.microsoft.com/sn/strider/

  • Configuration Management

    • Strider Troubleshooting: DSN’03, LISA’04, DSN’04, LISA’05

    • Glean: ICAC’04

    • Flight Data Recorder (FDR): LISA’05

    • Friends Troubleshooting Network (FTN): IPTPS’04

    • PeerPressure: SigMetrics’04 (poster)

  • Patch Management

    • ICAC’04

  • Spyware Management

    • LISA’05


Thank you
Thank You! genes to replace a defective gene

  • International Conference on Autonomic Computing (ICAC’05)

    • Tentative: May 2005 in Seattle


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