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topology

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									Topology-Aware Overlay
Construction and Server
       Selection

            Sylvia Ratnasamy
                Mark Handley
                 Richard Karp
                Scott Shenker


          Infocom 2002
Connections of a node
Introduction
   Problem: Inefficient routing in large-scale networks
       In large-scale overlay networks, each node is logically
        connected to a small subset of other participants.
       Due to the lack of effort to ensure that application-level
        connectivity is congruent with underlying IP-level network
        topology
   Basic Idea: Optimize routing paths in network
       Define a binning scheme whereby nodes partition
        themselves into bins
       Nodes that fall within a given bin are relatively close to one
        another in terms of network latency
Outline
   Introduction
   Distributed Binning
   Topologically-aware construction of overlay
    networks
   Topologically-aware server selection
   Conclusion
Extracting proximity information
    Measuments that can be used to derive topological information:
      traceroute: 
                                                                          s
            intended for network diagnostic purposes,
            too heavy-weight,                                          2 sec
            excessive load on the network,
            disabled ICMP at some sites for security               a
        BGP routing table:                                             7 sec
            not directly available for end users,
            requires privilege or third party service                     b
        Network latency:       
            often a direct indicator of network performance,                   5 sec
            light-weight,                                      c
            end-to-end measurement,
            non-intrusive manner                                               t
Distributed Binning
   Goal:
       Have a set of nodes independently partition themselves
        into disjoint “bins”
       Nodes within a single bin are relatively closer to one
        another than to nodes not in their bin
   Scheme:
       A well-known set of machines that act as landmarks on the
        Internet
       Form a distributed binning of nodes based-on their relative
        distances
         A node measures round-trip-time (RTT) to each landmark
           and orders landmarks in order of increasing RTT
         Every node has an associated ordering of landmarks(or bin)
Distributed Binning
   Scheme: (Cont.)
     After finding ordering, we calculate absolute values of each RTT
      in ordering as follows
           We divide the range of possible latency values into a number of
            levels.
           Convert RTT values into level number and obtain a level vector
           Example:
            Level 0 0-100 ms                        l1            l2
            Level 1 100-200 ms
            Level 2 > 200ms                                       57 ms
                                                    232 ms                          l3

            Node A’s bin becomes “l2l3l1:0 1 2”                A           117 ms



       Topologically close nodes likely to have same ordering and
        belong to same bin
Distributed Binning




         Distributed Binning Scheme
Performance of Distributed
Binning
   Even though it is clearly scalable, does it do a reasonable
    job?
   Metric used:

                             Average In ter  bin latency
                Gain Ratio 
                             Average In tra-bin latency

       average inter-bin latency = average latency from a given node to all nodes not in its bin
       average intra-bin latency = average latency from a given node to all nodes in its bin



   A higher gain ratio indicates a higger reduction in latency,
    hence more desirable
Performance of Distributed
Binning
   Datasets or test topologies:
       TS-10K and TS-1K:
         Transit-Stub topologies with 10000
           and 1000 nodes respectively.
         2-level hierarchy


       PLRG1 and PLRG2:
         Power-Law Random graph with 1166 and 1779 nodes
         Edge latencies assigned randomly
       NLANR:
         Distributed network of over 100 active monitors
         Systematically perform scheduled measurement between
          each other
Performance of Distributed
Binning
   Other binning algorithms used in experiments:

       Random Binning:
         Each nodes selects a bin at random
         acts as a lower bound for the gain ratio
       Nearest Neighbor clustering:
         Each node is initially assigned to a cluster itself.
         At each iteration, two closest clusters are merged into a
          single cluster.
         The algorithm terminated when the required number of
          clusters is obtained

        _
Performance of Distributed
Binning
   Experiments:




     Effect of number of landmarks (#level=1)   Effect of number of levels (#landmarks=12)
Performance of Distributed
Binning
   Experiments:




             Comparison of different binning techniques(#levels=1)
Topologically-aware construction
of overlay networks
   Two types of overlay networks
       Structured:
         Nodes are interconnected in some well-defined
          manner(Application-level)
       Unstructured:
         Much less structured like Gnutella,Freenet

   Metric for evaluation:

                               Average Inter - node LatencyOverlayNetwork
          Latency Stretch 
                              Average Inter - node LatencyUnderly ingNetwork
Topologically-sensitive CAN
construction
   Content-Addressable Network
       Scalable indexing system for large-scale decentralized
        storage applications on the Internet
       Built around a virtual multi-dimensional Cartesian
        coordinate space
       Entire coordinate space is dynamically partitioned among
        all the peers, i.e. every peer possesses its individual,
        distinct zone within the overall space
        A CAN peer maintains a routing table that holds the IP
        address and virtual coordinate zone of each of its neighbor
        coordinates
2D CAN Example
             State of the system at time t

                                                            Peer

                                                            Resource




                       Zone
                                                    x
   In this 2 dimensional space, a key is mapped to a point (x,y)
Routing in CAN
                          y
• d-dimensional space
with n zones
                              (x,y)                      Peer

                                               Q(x,y) Query/
•Routing path of                                      Resource
length:

      (d/4)n 1/d
•Algorithm:                           Q(x,y)
     Choose the
     neighbor nearest
     to the destination
                                                   key
Contribution to CAN
   Construct CAN topologies that are congruent with underlying IP
    topology
   Scheme:
     With m landmarks, m! such ordering is possible
           For example, if m=2, then possible orderings are “ab” and “ba”
       We partion the coordinate space into m! equal sized portions,
        each corresponding to a single ordering
           Divide the space along first dimension into m portions
           Each portion is then sub-divided along the second dimension into
            m-1 portions
           Each of these are divided into m-2 portion and so on…
       When a node joins CAN at a random point, the node determines
        its associated bin based-on delay measurement
       According to its landmark ordering, it takes place in the
        correspanding portion of CAN
Gain in CAN using Distributed
Binning




Stretch for a 2D CAN; topology TS-1K;#levels=1   Stretch for a 2D CAN; topology PLRG2;#levels=1
Topologically-aware construction
of unstructured overlays
   Aims much less structured overlay such as Gnutella,
    Freenet
   Focusing on the following general problem in
    unstructured overlays:

    “Given a set of n nodes on the Internet, have each node
    picks any k neighbor nodes from this set so that the
    average routing latency on the resultant overlay is low”


   Optimal overlay is NP-hard, so used some heuristic
    called Short-Long
Topologically-aware construction
of unstructured overlays
   Short-Long Heuristic
     A node picks its k neighbors by picking k/2 nodes closest to itself
      and then picks another k/2 nodes at random
     Well-connected pocket of closest nodes and inter-connections to
      far pockets with random picks

          Current Node
          Nearby Nodes
          Distant Nodes
          Other Nodes



   BinShort-Long (Contribution) :
     A node picks k/2 neighbors at random from its bin and picks
       remaining k/2 at random
Gain in Unstructured Overlay
using Distributed Binning




      Unstructured overlays; TS-10K;#levels=1;#landmarks=12
Topology-aware server
selection
   Replication of content over Internet gives rise to the
    problem of server selection
       Parameter: Server load and distance(in term of Network
        Latency)




       _
        Replication Server

        Client
Topology-aware server
selection
   Server selection process with distributed binning works as follows:
       If there exist one or more servers within same bin as client, then client is
        redirected to a random server from its own bin
       If no server exists within same bin as client, then an existing server whose
        bin is most similar to client’s bin is selected at random

   Compared performance to 3 schemes:
       Random: Client selects server at random
       Hotz Metric: Uses RTT measure from a node to well known landmarks to
        estimate internode distance (Triangle inequality)
       Cartesian Distance: Calculates Euclidean distance using level vector of node
        and selects the server with minimum distance

   Measurement for evaluation:
                                         LatencySelected Server
                   Latency Strecth 
                                         LatencyOptimal Server
Topology-aware server
selection




     Comparison of different schemes under following conditions:
         • 12 landmarks and 3 levels
         • 1000 servers for TS-10K, 100 servers for TS-1K, PLRG1 and
         PLRG2 and 10 for NLANR
Topology-aware server
selection-Node Perspective




 CDF of latency stretch for TS-10K data   CDF of latency stretch for NLANR data
Conclusion
   Described a simple,scalable,binning scheme that can be used to
    infer network proximity information
   Nature of the underlying network topology affects behavior of the
    scheme
   It is applied to the problem of topologically-aware overlay
    construction and server selection domains
   Three applications of distributed binning is given:
     Structured Overlay
     Unstructured Overlay
     Server selection
   A small number of landmarks yields significant improvements.
   Can be referred as network-level GPS system

_
Happy end! Thank you for your
         patience!

								
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