Network Coordinates ： Internet Distance Estimation

Jieming ZHU15-11-2011

Outline

2

• Motivation

• Problem Statement

• General Approaches

• Applications

• Open Issues

3

What is “Internet distance”?

• Round trip time– Symmetric– Relatively stable– Triangle inequality violation

• Bandwidth, loss rate– Not really “distance”, but useful– Asymmetric

4

Why estimate distances?

5

Why estimate distances?• Distance estimation can be used to optimize

large scale distributed systems:– Server selection– Locality aware peer-to-peer overlay networks– Application level multicast

• Problems with on-demand measurement:– Slow (e.g. ping N*(N-1) times)– High overhead

Outline

6

• Motivation

• Problem Statement

• General Approaches

• Applications

• Open Issues

7

Problem Statement• Network Coordinates: Internet as a

geometric space– Map each node to a position in the geometric space – Each host has a “coordinate”– Compute distances based on coordinates

Outline

8

• Motivation

• Problem Statement

• General Approaches

• Applications

• Open Issues

9

General Approaches• Landmark-based algorithms:

– Each node measure latency to set of landmark nodes– Use landmark nodes to calculate own coordinate– E.g. GNP [CMU], Lighthouses [Cambridge]

• Distributed algorithms:– Each node measures latency to random other nodes– Model embedding as a spring system– E.g. Vivaldi [MIT], DCS [Ottawa]

• Matrix factorization based algorithms– Based on SVD/NMF– E.g. IDES [Penn], Phoenix [Tsinghua]

10

1. GNP: Global Network Positioning• Landmark operations

– Compute the coordinates of the Landmarks by minimizing:

where

jiLLLL

SLLLL

SL

SL

Nji

jijiNddccf

|},...,{, 1

1),(),...,( min

2)(),(21212121

SHHHH

SHHHH dddd

11

1. GNP: Global Network Positioning• Ordinary host operations

– Ordinary host derives its own coordinates by using the coordinates of the landmarks

},...,{ 1

),()(minNi

iiLLL

SHLHL

SH ddcf

– Simplex downhill algorithm to solve the minimization problem

12

2. Vivaldi: Distributed

13

2. Vivaldi: Distributed

Confidence in remote node

Confidence in self

Adjust time step

14

2. Drawback: Euclidean embedding

N1

N2

N3A

B C

|| A || <= || B || + || C||

N1

N2

N3100 ms

48 ms 48 ms

100 <= 48 + 48100 <= 96

• TIV: Triangle Inequality Violation

• GNP & Vivaldi: TIVInaccuracy

15

3. IDES: MF based

16

Evaluation

IDES vs. GNP

Vivaldi vs. GNP Vivaldi vs. GNP

Outline

17

• Motivation

• Problem Statement

• General Approaches

• Applications

• Open Issues

18

Applications• File sharing systems: find the nearest peer• Database query optimization• Overlay network multicast• Context distribution networks• Location-aware server selection• Compact routing• Distributed network games: find the top k

nearest servers for the player

19

Applications

Outline

20

• Motivation

• Problem Statement

• General Approaches

• Applications

• Open Issues

21

Open Issues• Accuracy : TIV problem• Scalability : Efficient (fast convergence)

distributed algorithms• Robustness:

– Effect of network traffic– Impact of malicious nodes

• Stability– Vivaldi: Behavior of system when nodes are joining and

leaving (node churn)– GNP: Impact of Landmarks leaving the system

• Applications: Web service selection

Thank you!

Questions?