Topology-Aware Overlay Construction and Server

Selection

Sylvia RatnasamyMark HandleyRichard 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: 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

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 msLevel 1 100-200 msLevel 2 > 200ms

Node A’s bin becomes “l2l3l1:0 1 2”

Topologically close nodes likely to have same ordering and belong to same bin

l3

l1l2

A

232 ms

117 ms

57 ms

Distributed Binning

Distributed Binning Scheme

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

tencytra-bin laAverage In

ybin latencterAverage InGain Ratio

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:

Network Underlying

NetworkOverlay

Latency node-Inter Average

Latency node-Inter AverageretchLatency St

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

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)

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

1/d(d/4)n

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:

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”

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 NodeNearby NodesDistant NodesOther Nodes

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:

rverOptimal Se

erverSelected S

Latency

LatencyrecthLatency St

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 NLANR dataCDF of latency stretch for TS-10K 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!