predicting internet network distance with coordinates-based

T. S. Eugene Ng [email protected] Carnegie Mellon University 1

Predicting Internet Network Distance with Coordinates-Based Approaches

T.S. Eugene Ng and Hui ZhangDepartment of Computer Science

Carnegie Mellon University


Problem Statement

• Network distance– Round-trip propagation and transmission delay– Relatively stable, may be predictable

• Given two Internet hosts, can we accurately estimate the network distance between them without sending any RTT probes between them?


Why Predict Network Distance?

• Want to measure network performance to improve performance of applications– Napster, content addressable overlays, overlay multicast

• Huge number of paths to measure• TCP bandwidth and RTT probes are time-consuming

• Predicted network distance enables fast and scalable first-order performance optimization– Eliminate poor choices– Refine when needed


IDMaps [Francis et al. ‘99]

• Servers maintain simplified topological map

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Disadvantage of Client-Server Architecture

• Unavoidable additional delay in communicating with servers

• Shared servers can become performance bottleneck


Can this Problem be Solved with a Peer-to-Peer Architecture?

• Flip the problem: Can end hosts maintain “coordinates” that describe their network locations?

• End hosts exchange coordinates to compute distance– High performance, high scalability

(10,50,22) (-9,-10,3)

Distance function

68ms


Approach 1: Global Network Positioning (GNP) Coordinates

• Model the Internet as a geometric space (e.g. 3-D Euclidean)

• Characterize the position of any end host with geometric coordinates

• Use geometric distances to predict network distances

y(x2,y2,z2)

x

z

(x1,y1,z1)

(x3,y3,z3)(x4,y4,z4)


GNP Landmark Operationsy

xInternet



xInternet

L1

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L3

• Small number of distributed hosts called Landmarks measure inter-Landmark distances



xInternet

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• Compute Landmark coordinates by minimizing the overall discrepancy between measured distancesand computed distances– Cast as a generic multi-dimensional global minimization

problem



xInternet

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Internet

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• Compute Landmark coordinates by minimizing the overall discrepancy between measured distancesand computed distances– Cast as a generic multi-dimensional global minimization

problem

(x2,y2)

(x1,y1)

(x3,y3)

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GNP Ordinary Host Operations

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Internet

(x2,y2)

(x1,y1)

(x3,y3)

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Internet

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• Each ordinary host measures its distances to the Landmarks, Landmarks just reflect pings



x

Internet

(x2,y2)

(x1,y1)

(x3,y3)

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yL2

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(x2,y2)

(x1,y1)

(x3,y3)

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yL2

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Internet

L1

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• Ordinary host computes its own coordinates relative to the Landmarks by minimizing the overall discrepancy between measured distances and computed distances– Cast as a generic multi-dimensional global minimization

problem



x

Internet

(x2,y2)

(x1,y1)

(x3,y3)

L1

L2

L3

yL2

L1

L3


Internet

L1

L2

L3

• Ordinary host computes its own coordinates relative to the Landmarks by minimizing the overall discrepancy between measured distances and computed distances– Cast as a generic multi-dimensional global minimization

problem

(x4,y4)

L2

L1

L3


Important Questions

• What geometric model to use? • How to measure error in minimizations?• How to select Landmarks?• How many Landmarks?• What are the sources of prediction error?• How to reduce overhead?• Can we use geographical coordinates?

• Please see our paper• This talk: focus on performance comparisons


Approach 2: Triangulated Heuristic Coordinates

• Proposed by Hotz in 1994 for A* heuristic shortest network path search

• Provides upper and lower bounds for network distance– Assumes shortest path routing enforced

• This paper is the first study to apply and evaluate triangulated heuristic as a network distance prediction mechanism



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Hop count is used in this example



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Upper bound (U) = min (ai + bi)



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Upper bound (U) = min (ai + bi) = 3



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Upper bound (U) = min (ai + bi) = 3 Correct!


Evaluation Methodology

• 19 Probes– 12 in North America, 5 in East Asia, 2 in Europe

• 869 IP addresses called Targets we do not control– Span 44 countries

• Probes measure– Inter-Probe distances– Probe-to-Target distances

• See paper for more results


Evaluation Methodology (Cont’d)

• Choose a subset of well-distributed Probes to be Tracers/Base nodes/Landmarks

• Use the rest for evaluation

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Performance Metrics

• Directional relative error– Symmetrically measure over and under predictions

• Relative error = abs(Directional relative error)

),min( predictedmeasured

measuredpredicted−


Relative Error Comparison

90% = 0.97



90% = 0.9790% = 0.59



90% = 0.9790% = 0.59

90% = 0.50


Directional Relative Error Analysis

DRE

Frequency

95%

75%

Mean50%

25%

5%

*


Directional Relative Error Comparison


Why the Difference in Over-Predictions?

IDMaps

Straight-line distance used in this example



IDMaps


Triangulated Heuristic



IDMaps


Triangulated Heuristic

GNP (1-dimensional model)


Sensitivity to Infrastructure Node Placement

• Which nodes are used as Tracers/Bases/Landmarks matter

• High sensitivity means the approach is less robust• Test sensitivity by picking 20 random combinations of

6 infrastructure nodes and observe performance variance


6 Random Infrastructure Nodes(20 Experiments)

00.20.40.60.8

11.21.41.61.8

2

GNP TriangulatedHeuristic/U

IDMaps

90%

Rel

ativ

e E

rror

Max Mean Min Standard Deviation


Conclusions

• Coordinates-based approaches represent a new class of solutions

• These solutions fit well with the peer-to-peer architecture– Potentially better performance and scalability than the client-

server architecture

• Careful Internet evaluation shows that coordinates-based approaches are more accurate than IDMaps

• GNP is the most accurate and robust solution


Internet as an Euclidean Space? Remarkable!

Internet map as of 1998 byBill Cheswick, Bell LabsHal Burch, CMU

predicting internet network distance with coordinates-based

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