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. 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

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 wireless components

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


Mnm take aways
MnM wireless components 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 wireless components 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: wireless componentsclient accesses http://foo

DNS Server

Kerberos Server

Web Server

Client C


Stationary dependency graph
Stationary wireless componentsdependency graph


Dynamic dependency graphs
Dynamic wireless componentsdependency 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 wireless components System Architecture

Runs on every monitored machine

Runs on a central server


Incrementally building an dependency graph
Incrementally building an wireless componentsdependency 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 wireless components

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 wireless components

  • Controlled experiments

    • Verified accuracy of MnM diagnosis

  • Two week study on 27 user laptops and 10 servers


Location profiling techniques
Location Profiling Techniques wireless components

  • AP-based location, default

  • Outlook calendar-based, if available

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



Location priors
Location Priors wireless components


Impact of using location priors
Impact of Using Location Priors wireless components


Conclusion
Conclusion wireless components

  • 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 wireless components


Accuracy results
Accuracy Results wireless components


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