Mesh Network Visualization Proposal: Difference between revisions

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== Project ==
== Project Summery ==


The suggested project will aim to provide visualization capabilities for mesh network analysis. The KDE Marble widget would support display mechanism, along with a database backed data source. Provided capabilities include :
This project will aim to provide a visual research tool for mesh network analysis. A mapping tool capable of vector graphic over-lays on top of satellite imagery would be used [GRASS is one such example]. The suggested tool would make use of a database backed data source. Provided capabilities include :


1. IP to Geographical position mapping using the Marble widget.
1. Marble XO client mapping based on GPS data.
2. Add map overlays utilizing several visual techniques such as edge coloration, opacity levels & node grouping.
2. Various map overlays utilizing several visual techniques.
3. Add interactive capability by making mesh node/edges selectable.
3. Add interactive capability by making mesh node/edges selectable.
4. Add animated versions of the map overlays (Time permitting)
4. Add animated versions of the map overlays (Time permitting)


Current unability to run Merble under XO (being a KDE widget) was taken into account - never the less since the suggested tool's main goal is mesh network analysis & research tool XO users will probable be providing simulation data rather than actually running the tool. This issue may become irrelevantly once widget support is added to XO.
The suggested tool's main goal is to provide a mesh network analysis & research tool. XO users will probable be providing simulation data rather than actually running the tool. Likely scenarios in which the tool would be used include wireless network installations at OLPC program sites, mesh testing & mesh research.


== General ==
== General ==


Mesh network visualization is an effective tool for mesh network analysis & testing. This tool would make use of the KDE Marble widget to display data assumed to exist in some database backed form. Different 'map overlays' would directly effect what data is extracted from the database, loaded into memory and displayed through Marble.
Mesh network analysis could be assisted considerable by a dedicated visual research tool. Different 'mesh data overlays' would be applied on top of satellite imagery, loading different database subsets into memory and possible animating them.


The Marble widget was chosen since it scales well alongside mesh size (WAN / LAN size mesh networks ). Presented data would be throttled according to map zoom levels, grouping nodes as clusters when zooming out. Ability to un/hide data layers would also keep large scale scenarios clear / small scale scenarios detailed. Viewing Marble as a 'global' scale mapping tool is mis-accurate : being similar to Google Earth - it would be usable also at the village \ municipal scale provided adequate maps are supplied. This makes the tool correlate well with OLPC's needs. As well, Marble makes integrating interactive user capabilities easy such as making nodes selectable. GPS support in marble would ease using GPS oriented XO mesh data.
Presented data within the mapping tool would be throttled according to map zoom levels, grouping XO nodes as clusters when zooming out. Ability to un/hide data layers would also keep large scale scenarios clear. User interactive capabilities in the form of context menus will be provided.


Most overlays map nodes by their GPS position. If no GPS data is available an approximated node position could be computed by some algorithm based on relative signal strength. Background imagery could be disabled in this case.
Following is a list of several visual techniques and mesh network metrics to which they apply. Several node types would be displayed such as clients, AP, Routers, Clusters (when zoomed out). All mappings place nodes on map by their geographical positing.


== Visualization ==

[[Image:Network analysis tool vision.png |thumb | right | A vision of how this tool would look like ]]

Following is a list of several visual techniques and a partial mesh network metrics set to which they apply. Several map icons would be used for various network entities such as clients, AP, routers, clusters (when zoomed out).


1. Simple topology representation
1. Simple topology representation

:interconnect nodes according to signal, actual data flow etc. Automatic node grouping would make this view scale easily to client, router or cluster levels. Illustrates mesh area covering.
interconnect nodes according to signal, actual data flow etc. Automatic node grouping would make this view scale easily to client, router or cluster levels. Illustrates mesh network cover. A 'village scale' representation may show active XO links and how they all connect to a school router.


2. Icon changing of nodes
2. Icon changing of nodes

:Change node icons according to status (failed, busy, unreachable, router, XO laptop etc. )
Change node icons according to status (failed, busy, unreachable, router, XO laptop etc. )


3. 'Heat' colorizing of inter-node edges
3. 'Heat' colorizing of inter-node edges
:applicable mesh metrics include node signal strength, node load rate, average node throughput, node collision / retransmission rates, load vs. signal strength, load vs. collisions, dropped packet count etc.
:Examples:
:: a 'hot' segment in the 'dropped packet' overlay would indicate a network segment being over loaded with data.
:: a 'cool' segment in the 'load vs. signal strength' would indicate under utilized network segments.


applicable to node signal strength, node load rate, average node throughput, node collision / retransmission rates, load vs. signal strength, load vs. collisions, dropped packet count etc.
:This technique could make mesh bottle necks stand out & point out unreliable network segments.

Examples:

- a 'hot' segment in the 'dropped packet' overlay would indicate a network segment being over loaded with data.
- a 'cool' segment in the 'load vs. signal strength' would indicate under utilized network segments.

This technique could make mesh bottle necks stand out & point out unreliable network segments.


4. Varied opacity edge coloration
4. Varied opacity edge coloration
:applicable mesh metrics are collision / retransmission rates (higher opacity levels for more reliable segments), edge load rate.


applicable mesh metrics are collision / retransmission rates (higher opacity levels for more reliable segments), edge load rate.
Although this might be beyond the scope of GSoC, most interesting visualizations would emerge when animating the above overlays. Implementation would call for either pre-loading all relevant data into memory or intensive database communication (sacrificing performance). Animation would help unveil network dynamics: Animating the load overlay would allow 'seeing' how routing algorithms respond to route around congested network segments (essentially entropy in action). Animated 'Load vs. Signal' overlay could show mesh reaction to segment / node (router) failure. Also interesting to view is network load shifting during the day.


Most interesting visualizations would emerge when animating the above overlays. Implementation would call for either pre-loading all relevant data or intensive database communication (sacrificing performance). This would help unveil network dynamics: Animating the load overlay would allow 'seeing' how routing algorithms respond to route around congested network segments (essentially entropy in action). Animated 'Load vs. Signal' overlay could show mesh reaction to segment / node (router) failure. Network load shifting during the day could be investigated to enhance usage during the night for certain tasks (automatic client software updating for example).
Interactive user capabilities include node/edge displaying IP data, segment protocol histogram, Tx\Rx ratios, etc. upon being selected. User interface will allowing for easy selection of overlay type & hiding of mapped elements.


Interactive user capabilities include node/edge displaying IP data, segment protocol histogram, Tx\Rx ratios, etc. upon being selected.
* Note :

:Although the XO-1 currently doesn't support a GPS module one might expected it to be incorporated in future versions. As well an approximated node position could be computed by applying a triangulation algorithm on the given data set.
One far reached application might be to used the tool in an 'real time fashion' where selecting two nodes may initiate a ping command to measure network lag time between them.

== Longterm Road Map ==

One far reached application might be to used the tool in a 'real time fashion'. Network statistical information would be gathered 'online' as experiments progress. Some possible applications of this would be :
# selection of two nodes may initiate a ping command to measure network lag time between them.
# Injection of simulated network node failures would ease the testing of the networks rerouting algorithms.
# File entropy simulations - measuring the speed in which the latest release of Ubuntu could reach opposite sides of the network using various P2P protocols.


== Data Representation ==

The libpcap file format is the de-facto packet gathering format & should be thought as the default data input format. Unfortunately it currently lacks interface statistics data, vital for mesh analysis (hopefully provided in next generation libpcap file format). Since the tool will be database backed automatic database generation from a set XO libpcap files is a desirable feature though may exceed GSoC time scale.

This still leaves some mesh metrics (router level statistics) that would have to be provided in some other form (preferable as an experiment database). Relevant mesh overlays will become available through database schema analysis. Example : a database linking nodes to their libpcap files with GPS data (expressed in the DB schema) would result in a GPS based overlay with an option to view each XO's libcap file in wireshark.




== Deliverables ==
== Deliverables ==
Since the potential of this tool is wide I will focus on the following features as a start :


1. Support for all non-animated GPS based overlays backed by some DB.
1. A working Marble widget capable to produce all non-animated data mappings.
2. User interaction enabling node & edge selection
2. User interaction enabling node & edge selection.
3. Ability to hide nodes / edges
3. Ability to hide nodes / edges.
4. A layers menu for overlay selection & un/hiding of map elements.
4. A layers menu for overlay selection & un/hiding of map elements.
5. Auto node un/grouping upon zooming in/out ( optional )
5. Auto node un/grouping upon zooming in/out ( optional )
6. Animated overlays ( optional, depending on progress )
6. Animated overlays ( optional, depending on progress )


All these should provide the OLPC with an highly effective mesh network test & analysis tool. The open source community would benefit as well since added Marble features may be useful for other applications. This might be far-reached but enabling mesh network analysis at a country scale, say Peru, is not unconceivable either.
These should provide the OLPC with an highly effective mesh network analysis tool.



Any mentor comment through the actual application (submitted on 2008/03/30 11:42:44 PDT) is welcome.
Any mentor comment through the actual application (submitted on 2008/03/30 11:42:44 PDT) is welcome.

[[Category:GSoC proposals]]

Latest revision as of 19:27, 14 April 2008

Project Summery

This project will aim to provide a visual research tool for mesh network analysis. A mapping tool capable of vector graphic over-lays on top of satellite imagery would be used [GRASS is one such example]. The suggested tool would make use of a database backed data source. Provided capabilities include :

  1. Marble XO client mapping based on GPS data.
  2. Various map overlays utilizing several visual techniques.
  3. Add interactive capability by making mesh node/edges selectable.
  4. Add animated versions of the map overlays (Time permitting) 

The suggested tool's main goal is to provide a mesh network analysis & research tool. XO users will probable be providing simulation data rather than actually running the tool. Likely scenarios in which the tool would be used include wireless network installations at OLPC program sites, mesh testing & mesh research.

General

Mesh network analysis could be assisted considerable by a dedicated visual research tool. Different 'mesh data overlays' would be applied on top of satellite imagery, loading different database subsets into memory and possible animating them.

Presented data within the mapping tool would be throttled according to map zoom levels, grouping XO nodes as clusters when zooming out. Ability to un/hide data layers would also keep large scale scenarios clear. User interactive capabilities in the form of context menus will be provided.

Most overlays map nodes by their GPS position. If no GPS data is available an approximated node position could be computed by some algorithm based on relative signal strength. Background imagery could be disabled in this case.


Visualization

A vision of how this tool would look like

Following is a list of several visual techniques and a partial mesh network metrics set to which they apply. Several map icons would be used for various network entities such as clients, AP, routers, clusters (when zoomed out).

1. Simple topology representation

interconnect nodes according to signal, actual data flow etc. Automatic node grouping would make this view scale easily to client, router or cluster levels. Illustrates mesh network cover. A 'village scale' representation may show active XO links and how they all connect to a school router.

2. Icon changing of nodes

Change node icons according to status (failed, busy, unreachable, router, XO laptop etc. )

3. 'Heat' colorizing of inter-node edges

applicable to node signal strength, node load rate, average node throughput, node collision / retransmission rates, load vs. signal strength, load vs. collisions, dropped packet count etc.

Examples:

- a 'hot' segment in the 'dropped packet' overlay would indicate a network segment being over loaded with data.     
- a 'cool' segment in the 'load vs. signal strength' would indicate under utilized network segments. 

This technique could make mesh bottle necks stand out & point out unreliable network segments.

4. Varied opacity edge coloration

applicable mesh metrics are collision / retransmission rates (higher opacity levels for more reliable segments), edge load rate.

Most interesting visualizations would emerge when animating the above overlays. Implementation would call for either pre-loading all relevant data or intensive database communication (sacrificing performance). This would help unveil network dynamics: Animating the load overlay would allow 'seeing' how routing algorithms respond to route around congested network segments (essentially entropy in action). Animated 'Load vs. Signal' overlay could show mesh reaction to segment / node (router) failure. Network load shifting during the day could be investigated to enhance usage during the night for certain tasks (automatic client software updating for example).

Interactive user capabilities include node/edge displaying IP data, segment protocol histogram, Tx\Rx ratios, etc. upon being selected.

One far reached application might be to used the tool in an 'real time fashion' where selecting two nodes may initiate a ping command to measure network lag time between them.

Longterm Road Map

One far reached application might be to used the tool in a 'real time fashion'. Network statistical information would be gathered 'online' as experiments progress. Some possible applications of this would be :

  1. selection of two nodes may initiate a ping command to measure network lag time between them.
  2. Injection of simulated network node failures would ease the testing of the networks rerouting algorithms.
  3. File entropy simulations - measuring the speed in which the latest release of Ubuntu could reach opposite sides of the network using various P2P protocols.


Data Representation

The libpcap file format is the de-facto packet gathering format & should be thought as the default data input format. Unfortunately it currently lacks interface statistics data, vital for mesh analysis (hopefully provided in next generation libpcap file format). Since the tool will be database backed automatic database generation from a set XO libpcap files is a desirable feature though may exceed GSoC time scale.

This still leaves some mesh metrics (router level statistics) that would have to be provided in some other form (preferable as an experiment database). Relevant mesh overlays will become available through database schema analysis. Example : a database linking nodes to their libpcap files with GPS data (expressed in the DB schema) would result in a GPS based overlay with an option to view each XO's libcap file in wireshark.


Deliverables

Since the potential of this tool is wide I will focus on the following features as a start :

  1. Support for all non-animated GPS based overlays backed by some DB.
  2. User interaction enabling node & edge selection. 
  3. Ability to hide nodes / edges.
  4. A layers menu for overlay selection & un/hiding of map elements.  
  5. Auto node un/grouping upon zooming in/out ( optional ) 
  6. Animated overlays ( optional, depending on progress ) 

These should provide the OLPC with an highly effective mesh network analysis tool.

Any mentor comment through the actual application (submitted on 2008/03/30 11:42:44 PDT) is welcome.