Download Topology-Aware Overlay Construction and Server Selection

Topology-Aware Overlay
Construction and Server
Selection
Sylvia Ratnasamy
Mark Handley
Richard Karp
Scott Shenker
Infocom 2002
Connections of a node
Introduction
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Problem: Inefficient routing in large-scale networks
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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
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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
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Introduction
Distributed Binning
Topologically-aware construction of overlay
networks
Topologically-aware server selection
Conclusion
Extracting proximity information
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Measuments that can be used to derive topological information:
 traceroute: 
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2 sec
a
BGP routing table: 
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s
intended for network diagnostic purposes,
too heavy-weight,
excessive load on the network,
disabled ICMP at some sites for security
not directly available for end users,
requires privilege or third party service
Network latency:
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7 sec
b
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often a direct indicator of network performance,
light-weight,
end-to-end measurement,
non-intrusive manner
5 sec
c
t
Distributed Binning
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Goal:
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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:
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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
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Scheme: (Cont.)
 After finding ordering, we calculate absolute values of each RTT
in ordering as follows
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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:
l2
Level 0 0-100 ms
l1
Level 1 100-200 ms
Level 2 > 200ms
Node A’s bin becomes “l2l3l1:0 1 2”
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57 ms
232 ms
A
l3
117 ms
Topologically close nodes likely to have same ordering and
belong to same bin
Distributed Binning
Distributed Binning Scheme
Performance of Distributed
Binning
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Even though it is clearly scalable, does it do a reasonable
job?
Metric used:
Average Inter  bin latency
Gain Ratio 
Average Intra-bin la tency
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
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A higher gain ratio indicates a higger reduction in latency,
hence more desirable
Performance of Distributed
Binning
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Datasets or test topologies:
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TS-10K and TS-1K:
 Transit-Stub topologies with 10000
and 1000 nodes respectively.
 2-level hierarchy
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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
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Other binning algorithms used in experiments:
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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
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Experiments:
Effect of number of landmarks (#level=1)
Effect of number of levels (#landmarks=12)
Performance of Distributed
Binning
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Experiments:
Comparison of different binning techniques(#levels=1)
Topologically-aware construction
of overlay networks
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Two types of overlay networks
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Structured:
 Nodes are interconnected in some well-defined
manner(Application-level)
Unstructured:
 Much less structured like Gnutella,Freenet
Metric for evaluation:
Latency Stretch 
Average Inter - node Latency Overlay Network
Average Inter - node Latency UnderlyingNetwork
Topologically-sensitive CAN
construction
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Content-Addressable Network
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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
• d-dimensional space
with n zones
y
(x,y)
Peer
Q(x,y) Query/
Resource
•Routing path of
length:
(d/4)n 1/d
•Algorithm:
Choose the
neighbor nearest
to the destination
Q(x,y)
key
Contribution to CAN
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Construct CAN topologies that are congruent with underlying IP
topology
Scheme:
 With m landmarks, m! such ordering is possible
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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
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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…
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When a node joins CAN at a random point, the node determines
its associated bin based-on delay measurement
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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
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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”
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Optimal overlay is NP-hard, so used some heuristic
called Short-Long
Topologically-aware construction
of unstructured overlays
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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
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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
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Replication of content over Internet gives rise to the
problem of server selection
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Parameter: Server load and distance(in term of Network
Latency)
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_Replication Server
Client
Topology-aware server
selection
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Server selection process with distributed binning works as follows:
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Compared performance to 3 schemes:
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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
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:
Latency Strecth 
LatencySelected Server
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
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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!