1 Cognitive Radio Networks Zhu Jieming Group Presentaion Aug. 29, 2011.
Network Coordinates : Internet Distance Estimation Jieming ZHU 15-11-2011.
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Transcript of Network Coordinates : Internet Distance Estimation Jieming ZHU 15-11-2011.
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?