{firstname.lastname}@liris.cnrs.fr - http://liris.cnrs.fr/

Laboratoire d'InfoRmatique en Image et Systèmes d'informationLIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/Ecole Centrale de Lyon

Université Claude Bernard Lyon 1, bâtiment Nautibus43, boulevard du 11 novembre 1918 — F-69622 Villeurbanne cedex

http://liris.cnrs.fr

UMR 5205

GridNets 2006 - 2006/10/01

GridNets 2006 - 2006/10/01

Evaluation of network distances properties:NDS, the Network Distance Service.

Julien Gossa

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Network Distances (1)

Network distances are used for many purposesDistance Vector Protocols

RIV, IGRP, EIGRP, OSPF…Network problem solving

data managementnetwork topology discoveringresource brokeringnodes clustering…

Using distance is comfortableIt makes the network looking like the real worldDistances are also called metrics

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Network Distances (2)

Generally very simple:Latency (or RTT) only

IDMaps (Francis et al. 2001)Global Network Positioning (GPN) (Eugene et al. 2002)“Automatic clustering of grid nodes” (Xu et al. 2005}

Rarely BandwidthIDMaps (Francis et al. 2001) “when available“

But with a strong assumption: Euclidean SpaceMostly the properties: symmetry and triangle inequalityComes from the will to refer to the real world

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Network Distances (2)

But isn’t this assumption too strong?For instance, because of asymmetric IP routes or

asymmetric connection (ADSL)

MoreoverThe satisfaction can differ from a network to another one

According to the infrastructure homogeneity and conditionThe satisfaction might not be perfect, but acceptable

For instance, latency uplink and downlink can differ of 0.5%

We propose to identify exhaustively what are the interesting properties of network distances

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Network Distance Properties

The first 4 refer to Euclidean Space properties

The last 2 are useful in peculiar cases only

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Properties’ Satisfaction Degree

But:Distance are measured and thus approximateFor instance

The measure uplink and downlink may differ of 1%This difference can be acceptable or not

According to the end use of the distance

Thus, its necessary to compute satisfaction degreeFor each propertyBased on actual measurements

Thus for each environment And even at each time in case of great instability

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Properties’ Satisfaction Degree

One satisfaction degreePer propertyPer distancePer couple of end-points

statistics for the whole network:

Min/MaxMean/VarianceAnd RoutesRatio:

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Properties’ Satisfaction Degree

Finally, the global satisfaction degree is obtained with the RouteRatio

of a given property pfor a given metric m

It gives the ratio of routes which satisfaction degree is higher than a threshold t

t has to be defined by the userAccording to its use of the distanceAnd the impact of the properties dissatisfaction

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Experimentation

Experimentation has been done On our (small) test grid:

5 computers located in 3 cities (Lyon, Toulouse and Lille)Connected through the Internet (shared network)With measurements from the Network Distance Service (NWS)Which treatment are embedded in the Network Distance Service

We evaluated the satisfaction of:Symmetry, Triangle InequalitySubstitutability and Splitability

Of the metrics:Latency LatBandwidth BWA compound metrics DTT=3xLat+data_size/BW

For several values of data_size

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Experimentation

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Experimentation - Conclusion

The symmetry is well satisfied by latency and DTT but not by Bandwidth.

For instance, IDMaps should not be used with bandwidth in our test grid

The triangle inequality is not fully satisfied by Latency and Bandwidth

This property should be taken with extreme care (which compromise the use of most of the works using distances)

The splitability is very well satisfied by Latency but not by Bandwidth.

This meets the property of latency at routing level where packets are transferred from a routing device to one other, adding latency along the route.

Then substitutability is well satisfied by all the metrics. This results is biased because of the small size of our test grid: no

conclusion can be done.

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Conclusion

Network distance are popularly used

But with potentially too strong assumptions

To treat this issue, we haveIdentify the different interesting properties of network

distancesDefine a satisfaction degree for each and a method to

check the satisfaction of a property at network-scaleEmbed these computations in a globus Web Service

The Network Distance Service (NDS)

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Conclusion

Thanks for attention !