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Topology-Aware Overlay Construction and Server Selection Sylvia Ratnasamy Mark Handley Richard Karp Scott Shenker Infocom 2002

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Connections of a node

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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

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Outline Introduction Distributed Binning Topologically-aware construction of overlay networks Topologically-aware server selection Conclusion

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Extracting proximity information Measuments that can be used to derive topological information: traceroute: intended for network diagnostic purposes, too heavy-weight, excessive load on the network, disabled ICMP at some sites for security BGP routing table: not directly available for end users, requires privilege or third party service Network latency: often a direct indicator of network performance, light-weight, end-to-end measurement, non-intrusive manner s a b c t 2 sec 7 sec 5 sec

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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)

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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 ms Level ms Level 2 > 200ms Node As bin becomes l 2 l 3 l 1 :0 1 2 Topologically close nodes likely to have same ordering and belong to same bin l3l3 l1l1 l2l2 A 232 ms 117 ms 57 ms

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Distributed Binning Distributed Binning Scheme

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

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Performance of Distributed Binning Datasets or test topologies: TS-10K and TS-1K: Transit-Stub topologies with 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

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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 _

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

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Performance of Distributed Binning Experiments: Comparison of different binning techniques(#levels=1)

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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:

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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

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2D CAN Example x State of the system at time t Peer Resource Zone In this 2 dimensional space, a key is mapped to a point (x,y)

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Routing in CAN y Peer Q(x,y) (x,y) d-dimensional space with n zones Routing path of length: Algorithm: Choose the neighbor nearest to the destination Q(x,y) Query/ Resource key

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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

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Gain in CAN using Distributed Binning Stretch for a 2D CAN; topology TS-1K;#levels=1Stretch for a 2D CAN; topology PLRG2;#levels=1

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Topologically-aware construction of unstructured overlays Aims much less structured overlay such as Gnutella, Freenet Focusing on the following general problem in unstructured overlays: Optimal overlay is NP-hard, so used some heuristic called Short-Long 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

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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 BinShort-Long (Contribution) : A node picks k/2 neighbors at random from its bin and picks remaining k/2 at random Current Node Nearby Nodes Distant Nodes Other Nodes

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Gain in Unstructured Overlay using Distributed Binning Unstructured overlays; TS-10K;#levels=1;#landmarks=12

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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

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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 clients 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:

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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

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Topology-aware server selection-Node Perspective CDF of latency stretch for NLANR dataCDF of latency stretch for TS-10K data

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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 _

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