Change is hard adapting dependency graph models for unified diagnosis in wired wireless networks
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Change Is Hard: Adapting Dependency Graph Models For Unified Diagnosis in Wired/Wireless Networks PowerPoint PPT Presentation


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Change Is Hard: Adapting Dependency Graph Models For Unified Diagnosis in Wired/Wireless Networks. Lenin Ravindranath, Victor Bahl, Ranveer Chandra, David A. Maltz, Jitendra Padhye, Parveen Patel. Enterprise Network (of the Near Future). Stationary servers hosted in wired cloud/DC

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Change Is Hard: Adapting Dependency Graph Models For Unified Diagnosis in Wired/Wireless Networks

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Change is hard adapting dependency graph models for unified diagnosis in wired wireless networks

Change Is Hard: Adapting Dependency Graph Models ForUnified Diagnosis in Wired/Wireless Networks

Lenin Ravindranath, Victor Bahl, Ranveer Chandra, David A. Maltz, Jitendra Padhye, Parveen Patel


Enterprise network of the near future

Enterprise Network (of the Near Future)

  • Stationary servers hosted in wired cloud/DC

  • Nomadic users connect via wireless, VPN, RAS, etc.

Data

Center

Network

Inter-Building Network

Campus user

Servers

RAS

Firewalls

Internet

Access Points

Remote user via VPN


End to end performance issues are a result of wired and wireless components

End-to-end performance issues are a result of wired and wireless components

  • Hard to figure out which component to blame

URL fetch time: wired desktop client and nomadic laptop client


Existing solutions

Existing solutions

  • No existing scheme works end-to-end in mixed wired/wireless environments


Mnm take aways

MnM Take Aways

1. Unified view of the wireless/wired network

2. User location needs to be a first class consideration

3. A system architecture that can deal with constantly changing dependencies, is easy to deploy and takes corrective action


Mnm s hammer dynamic dependency graphs

MnM’s hammer: Dynamic Dependency Graphs

  • Dependency graphs

    • Link observations to root causes

    • Use a fault inference algorithm, e.g., Sherlock

  • Deal with frequent topology changes due to mobility

    • Constantly monitor end-systems to detect changes

    • Apply differences to existing dependency graph

  • Consider location as a first-class component

    • Bootstrap the location system without help from static infrastructure

    • Use white-box monitoring to determine location


Example scenario client accesses http foo

Example scenario:client accesses http://foo

DNS Server

Kerberos Server

Web Server

Client C


Stationary dependency graph

Stationary dependency graph


Dynamic dependency graphs

Dynamic dependency Graphs

Internet Path

Location

RAS Server

Access Point

Routers ...

Network

Path:CKerberos

Path:CWbSrv

Path:C  DNS

Kerberos server

Web Server

DNS server

Services

Name Resolution (C  DNS)

Certificate Fetch (C  Kerberos)

HTTP Get

(C  WebSrv)

Remote Gateway RTT

Local Gateway RTT

Client C accesses http://foo


Mnm system architecture

MnM System Architecture

Runs on every monitored machine

Runs on a central server


Incrementally building an dependency graph

Incrementally building an dependency graph

Location

Expert

RAS

Expert

WiFi

Expert

Location

Internet Path

RAS Server

Access Point

Routers ...

Net

Expert

Path:CKerberos

Path:CWbSrv

Path:C DNS

Service

Expert

Kerberos server

Web Server

DNS server

Type: NetworkService

Name Resolution (C  DNS)

Type: NetworkService

Certificate Fetch (C  Kerberos)

Type: NetworkService

HTTP Get

(C  WebSrv)

HTTP

Expert

Local Gateway RTT

Remote Gateway RTT

Type: Http.Request

Instance: http://foo

Client: C


Example end to end diagnosis

Example: end-to-end diagnosis

HTTP

Actuator

WiFi

Actuator

HTTP

Expert

Inference

Engine

RTT

Monitor

WiFi

Expert

Measurement

Response

Analysis

Fault Observation

Observation

State

Root-cause

Analysis

RC:

Hand-off

Recovery:

Change AP

Agent

Inference Engine


Evaluation

Evaluation

  • Controlled experiments

    • Verified accuracy of MnM diagnosis

  • Two week study on 27 user laptops and 10 servers


Location profiling techniques

Location Profiling Techniques

  • AP-based location, default

  • Outlook calendar-based, if available

  • Cluster similar looking WiFi signatures to identify unnamed locations, e.g., a coffee shop


Calendar based location profiles

Calendar-based Location Profiles


Location priors

Location Priors


Impact of using location priors

Impact of Using Location Priors


Conclusion

Conclusion

  • End-to-end performance diagnosis in mixed wired/wireless environments requires special considerations

    • The system needs to cope with constantly changing dependencies

    • Location needs to be a first-class component

  • MnM is an extensible system architecture for diagnosing performance faults using dynamic dependency graphs


Backup

Backup


Accuracy results

Accuracy Results


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