Download presentation

Presentation is loading. Please wait.

Published bySharyl Todd Modified over 2 years ago

1
EL9331 Meridian: A Lightweight Network Location Service without Virtual Coordinates Bernard Wong, Aleksandrs Slivkins, Emin Gun Sirer SIGCOMM’05 ( Slides revised from the Authors’ slides) Presented by Xiaojun Hei

2
EL9332 Outline r Introduction m Research Motivation m Virtual Coordinates vs. Meridian using Query routing r Meridian Overview r Applications of Meridian m Closest Node Discovery m Central Leader Election m Node selection with multi-constraints r Performance Evaluation r Conclusion Remarks

3
EL9333 Network Location Service r Selecting nodes based on network location (defined by RTTs) is a basic building block for distributed systems r Three categories of network location problems m Finding the closest node to one target node Locate the closet web mirror site Locate the closet game server for one player m Finding the closest node to the center of a set of nodes Perform efficient application level multicast Find an appropriate relay node between two hosts m Finding a node that is < r i ms from target t i for all targets ISP find a server that satisfies a Service Level Agreement with a client Grid users can find a set of nodes with a bounded inter-node latency

4
EL9334 Related Work: Virtual Coordinates r Maps high-dimensional network measurements into a location in a smaller Euclidean space m GNP, Vivaldi, Lighthouse, ICS, VL, BBS, PIC, NPS, etc. r The authors claimed three problems of the Coordinate approach m Inherent coordinate embedding error (Agree) m A snapshot in time of the network location of a node (???) Coordinates become stale over time Latency estimates based on coordinates computed at different times can lead to additional errors m Requires additional P2P substrate to solve network (???) location problems without centralized servers or O(N) state with a centralized server

5
EL9335 Global Network Positioning (GNP) r Model the Internet as a geometric space (e.g. 3-D Euclidean) r Characterize the position of any end host with coordinates r Use computed distances to predict actual distances 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 )

6
EL9336 Landmark Operations r Compute Landmark coordinates by minimizing the overall discrepancy between measured distances and computed distances y x Internet (x 2,y 2 ) (x 1,y 1 ) (x 3,y 3 ) L1L1 L2L2 L3L3 L1L1 L2L2 L3L3 r Small number of distributed hosts called Landmarks measure inter-Landmark distances

7
EL9337 Ordinary Host Operations r 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 r Ordinary host computes its own coordinates relative to the Landmarks by minimizing the overall discrepancy between measured distances and computed distances

8
EL9338 Meridian Contributions r Lightweight, scalable and accurate system for keeping track of location-information of Meridian nodes r Theoretical analysis shows that Meridian provides robust performance, high scalability and good load balance. r Comprehensive evaluation based on both simulations parameterized with RTTs from a large scale network measurements and a PlanetLab deployment

9
EL9339 Meridian Overview r Lightweight framework for direct node selection without computing coordinates m Combine querying routing with active measurement m Each query-hop brings the query exponentially closer to the destination r Design goals: m Accurate: Find satisfying nodes with high probability m General: User can fully express their network location requirements m Scalable: O(logN) state per node, O(logN) hops per query r Target node need not be part of Meridian overlay; however, the selected node must be part of the overlay m Only latency to target is needed

10
EL93310 Meridian Framework r Construct loosely structured Meridian overlay using multi-resolution rings m Peers are placed into small fixed number of concentric, non-overlapped rings r Overlay Management for Ring Membership m Number of nodes per ring, k m Achieve geographic diversity by ring member replacement r Gossip based node discovery m Each node discovers new nodes m Bootstrap for new joining Meridian node

11
EL93311 Multi-resolution Rings r Set of concentric, non- overlapping rings m Exponentially increasing radii r Node placement m Node A measures the distance to a peer, and places this peer in an appropriate ring of node A m At most k nodes in each ring m Favor nearby neighbor nodes m Retains a sufficient number of pointers to remote regions

12
EL93312 Multi-resolution Rings r Set of concentric, non- overlapping rings m Exponentially increasing radii r Node placement m Node A measures the distance to a peer, and places this peer in an appropriate ring of node A m At most k nodes in each ring m Favor nearby neighbor nodes m Retains a sufficient number of pointers to remote regions

13
EL93313 Multi-resolution Rings r Set of concentric, non- overlapping rings m Exponentially increasing radii r Node placement m Node A measures the distance to a peer, and places this peer in an appropriate ring of node A m At most k nodes in each ring m Favor nearby neighbor nodes m Retains a sufficient number of pointers to remote regions

14
EL93314 Multi-resolution Rings r Set of concentric, non- overlapping rings m Exponentially increasing radii r Node placement m Node A measures the distance to a peer, and places this peer in an appropriate ring of node A m At most k nodes in each ring m Favor nearby neighbor nodes m Retains a sufficient number of pointers to remote regions

15
EL93315 Ring Membership Management r The # of nodes per ring, k m Tradeoff between accuracy and overhead m Proper selection is needed r Periodically reassess ring membership decisions to provide greater diversity -> incur additional overhead m k primary ring members and l secondary ring members Geographically distributed ring members provides more “ utility ” than clustered ones for reaching remote nodes

16
EL93316 Ring Membership Management (cont ’ ) r To improve the g eographic diversity of nodes within each ring using hypervolume computation Geographic diversity m Hypervolume of the k-polytope formed by the selected nodes r Each node should have a local, non-exported coordinate Coordinates : tuple, d i i =0 r Use greedy algorithm m A node starts out with the k+l polytope m Iteratively drops the vertex whose absent leads to the smallest reduction in hypervolume until k vertices remain

17
EL93317 Gossip Based Node Discovery r Goal of gossip protocol m For each node, to discover and maintain a sufficiently diverse set of nodes in the network r Node discovery algorithm m Each node A randomly picks a node B from each of its rings and sends a gossip packet to B containing a randomly chosen node from rings m On receiving the packet, node B determines through direct probes its latency to A and to each of the nodes contained in the gossip packet m After sending a gossip packet to a node in each of rings, node A wait until the start of its next gossip period and then begins again

18
EL93318 Gossip Based Node Discovery (Cont ’ ) r Re-ringing ensures that stale latency information is updated Newly discovered nodes are placed on B ’ s rings as secondary members r The newly joining node contacts a Meridian node and acquires its entire list of ring members m Places them on its own rings

19
EL93319 Applications of Meridian r Closest Node Discovery r Central Leader Election r Node selection with multi-constraints

20
EL93320 Closest Node Discovery Client sends a “ closest node discovery to target T ” request to a Meridian node A r Node A determines its latency d to T r Node A probes its ring members between (1-β)d and (1+β)d to determine their distances to the target r The request is forwarded to the closest node thus discovered that is at least β times closer to the target than A r Process continues until no node that is β times closer can be found

21
EL93321 Closest Node Discovery A

22
EL93322 Closest Node Discovery A

23
EL93323 Closest Node Discovery A

24
EL93324 Closest Node Discovery A

25
EL93325 Closest Node Discovery A

26
EL93326 Closest Node Discovery A

27
EL93327 Closest Node Discovery A

28
EL93328 Closest Node Discovery A B

29
EL93329 Closest Node Discovery A B

30
EL93330 Closest Node Discovery A B

31
EL93331 Closest Node Discovery A B

32
EL93332 Closest Node Discovery A B

33
EL93333 Closest Node Discovery A B

34
EL93334 Closest Node Discovery A B

35
EL93335 Closest Node Discovery A B C

36
EL93336 Closest Node Discovery A B C

37
EL93337 Closest Node Discovery A B C

38
EL93338 Closest Node Discovery A B C

39
EL93339 Closest Node Discovery A B C

40
EL93340 Performance Evaluation r Evaluation via a large scale simulation and a PlanetLab deployment r Simulation parameterized by real latency measurements m 2500 DNS servers, latency between 6.25 million node pairs using the King measurement technique m DNS servers are authorities name servers for domains found in the Yahoo web directory r Network size up to 2000 nodes m 500 nodes reserved as targets m K=16 nodes per ring, 9 rings per node, s=2, probing packet size of 50 bytes, β=0.5 a=1ms m 4 runs in total. Each run has 25000 queries.

41
EL93341 Evaluation: Closest Node Discovery r Meridian has an order of magnitude less error than GNP and Vivaldi

42
EL93342 Evaluation: Closest Node Discovery r CDF of relative error shows Meridian is more accurate for both typical nodes and fringe nodes

43
EL93343 Effect of nodes/ring k r A modest number of nodes per ring achieves low error

44
EL93344 Effect of β r An increase β in significantly improves accuracy for β<0.5

45
EL93345 Scalability Evaluation

46
EL93346 Conclusion r A lightweight accurate system for node selection m Well designed Meridian overlay management protocols m Combine query routing with active measurement m An order of magnitude less error than virtual coordinates r Theoretical analysis on performance bound of the Meridian system (not covered in the presentation)

Similar presentations

OK

Vivaldi Coordinate Service Justin Ma, Patrick Verkaik, Michael Vrable Department of Computer Science And Engineering UCSD CSE222A, Winter 2005.

Vivaldi Coordinate Service Justin Ma, Patrick Verkaik, Michael Vrable Department of Computer Science And Engineering UCSD CSE222A, Winter 2005.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Free ppt on layers of atmosphere Light energy for kids ppt on batteries Ppt on db2 mainframes learning Ppt on cross-sectional study epidemiology Ppt on sources of energy for class 8th sample Ppt on electric meter testing companies Download ppt on teamviewer 7 Ppt on mass media in india Ppt on indian youth Ppt on image compression using wavelet transform