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T. S. Eugene Ng Mellon University1 Towards Global Network Positioning T. S. Eugene Ng and Hui Zhang Department of Computer.

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Presentation on theme: "T. S. Eugene Ng Mellon University1 Towards Global Network Positioning T. S. Eugene Ng and Hui Zhang Department of Computer."— Presentation transcript:

1 T. S. Eugene Ng Mellon University1 Towards Global Network Positioning T. S. Eugene Ng and Hui Zhang Department of Computer Science Carnegie Mellon University

2 T. S. Eugene Ng Mellon University2 New Challenges Large-scale distributed services and applications –Napster, Gnutella, End System Multicast, etc Large number of configuration choices K participants O(K 2 ) e2e paths to consider Stanford MIT CMU Berkeley CMU MIT Stanford Berkeley Stanford MIT CMU Berkeley CMU MIT Stanford Berkeley Stanford MIT CMU Berkeley CMU MIT Stanford Berkeley

3 T. S. Eugene Ng Mellon University3 Role of Network Distance Prediction On-demand network measurement can be highly accurate, but –Not scalable –Slow Network distance –Round-trip propagation and transmission delay –Relatively stable Network distance can be predicted accurately without on-demand measurement –Fast and scalable first-order performance optimization –Refine as needed

4 T. S. Eugene Ng Mellon University4 State of the Art: IDMaps [Francis et al 99] A network distance prediction service Tracer HOPS Server A B 50msA/B

5 T. S. Eugene Ng Mellon University5 What Can be Improved? Scalability Speed Accuracy

6 T. S. Eugene Ng Mellon University6 Global Network Positioning (GNP) Model the Internet as a geometric space (e.g. 3-D Euclidean) Characterize the position of any end host with coordinates Use computed distances to predict actual distances Reduce distances to coordinates y (x 2,y 2,z 2 ) x z (x 1,y 1,z 1 ) (x 3,y 3,z 3 ) (x 4,y 4,z 4 )

7 T. S. Eugene Ng Mellon University7 Landmark Operations Compute Landmark coordinates by minimizing the overall discrepancy between measured distances and computed distances –Cast as a generic multi-dimensional global minimization problem y x Internet (x 2,y 2 ) (x 1,y 1 ) (x 3,y 3 ) L1L1 L2L2 L3L3 L1L1 L2L2 L3L3 Small number of distributed hosts called Landmarks measure inter-Landmark distances

8 T. S. Eugene Ng Mellon University8 Ordinary Host Operations Each ordinary host measures its distances to the Landmarks, Landmarks just reflect pings x Internet (x 2,y 2 ) (x 1,y 1 ) (x 3,y 3 ) (x 4,y 4 ) L1L1 L2L2 L3L3 y L2L2 L1L1 L3L3 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

9 T. S. Eugene Ng Mellon University9 GNP Advantages Over IDMaps High scalability and high speed –End host centric architecture, eliminates server bottleneck –Coordinates reduce O(K 2 ) communication overhead to O(K*D) –Predictions are locally and quickly computable by end hosts Enable new applications –Structured nature of coordinates can be exploited Simple deployment –Landmarks are simple, non-intrusive (compatible with firewalls)

10 T. S. Eugene Ng Mellon University10 Evaluation Methodology 19 Probes we control –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 –Each distance is the minimum RTT of 220 pings

11 T. S. Eugene Ng Mellon University11 Evaluation Methodology (Contd) Choose a subset of well-distributed Probes to be Landmarks, and use the rest for evaluation TTTTT P3P3 P1P1 P4P4 P2P2 T (x 1,y 1 ) (x 2, y 2 )

12 T. S. Eugene Ng Mellon University12 Performance Metric Relative error –Symmetrically measure over and under predictions

13 T. S. Eugene Ng Mellon University13 GNP Accuracy 5-Dimensional Euclidean Space Model

14 T. S. Eugene Ng Mellon University14 GNP vs IDMaps 5-Dimensional Euclidean Space Model

15 T. S. Eugene Ng Mellon University15 Why the Difference? IDMaps tends to heavily over-predict short distances Consider (measured 50ms) –22% of all paths in evaluation –IDMaps on average over-predicts by 150 % –GNP on average over-predicts by 30% ???

16 T. S. Eugene Ng Mellon University16 Summary Network distance prediction is key to performance optimization in large-scale distributed systems GNP is scalable –End hosts carry out computations –O(K*D) communication overhead due to coordinates GNP is fast –Distance predictions are fast local computations GNP is accurate –Discover relative positions of end hosts


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