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A SimulationBased Study of Overlay Routing Performance

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Set up an application-level 'logical network' ... Only 2 overlay nodes are required to achieve optimal routing. ... defined as the number of pairwise shortest ... – PowerPoint PPT presentation

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Title: A SimulationBased Study of Overlay Routing Performance


1
A Simulation-Based Study of Overlay Routing
Performance
  • CS 268 Course Project
  • Andrey Ermolinskiy, Hovig Bayandorian, Daniel Chen

2
Problem Statement
  • The current Internet infrastructure is highly
    resistant to change new network-layer
    functionality is difficult to implement and
    costly to deploy.
  • Standard Internet routing protocols (BGP, OSPF)
    generally fail to deliver low- latency and
    high-throughput routing paths.
  • Coarse-grained routing metrics (e.g., hop count)
  • BGP policies
  • Lack of economic incentives to provide
    performance-based routing
  • Overlay networks offer an alternative method for
    deploying new routing functionality
  • Set up an application-level logical network.
  • Deploy a higher-level routing protocol that
    routes packets according to any desired routing
    metric.
  • Question 1 Given an alternative routing protocol
    that implements an arbitrary routing metric, to
    what extent can an overlay-based solution
    approximate a network-layer deployment?
  • Question 2 What is the best way to select
    overlay nodes?

3
Approach
  • Assume the existence of two distinct routing
    metrics WB (base), WO (overlay) and two
    routing protocols RB, RO that implement
    shortest path routing for WB and WO ,
    respectively.
  • Model the topology as a dualweighted graph
  • G (V, WB, WO).
  • RB is the default routing algorithm in G, but a
    subset of nodes O implements RO via an overlay
    mechanism.

CO(V1-V2-V5-V6) 17
CO(V1-V3-V2-V4-V5-V6) 9
4
Approach
  • To evaluate the performance of a given choice of
    O, compute the topology stretch.

Logical overlay subgraph
Dual-weighted topology graph
Stretch(v1, v6) 11 / 9
O v2, v4, v5
Stretch(v1, v6) 9 / 9 1 (Optimal Path)
O v2, v3 , v4, v5
5
Results Distribution of Stretch Values
  • Exhaustive search through the space of all
    subsets of nodes in two uniform random topologies
    from ?(N, p),

N 20, p 0.5
N 20, p 0.1
  • Only 2 overlay nodes are required to achieve
    optimal routing.
  • 90 of all overlays of size 11 or more achieve
    stretch value of 1.03.
  • 11 overlay nodes are required to achieve optimal
    routing.

6
Average Stretch in Large Random Topologies
  • Estimated average stretch for three uniform
    random topologies drawn from ?(1000, p).
  • A large fraction of the maximum achievable
    improvement lies with small selections of overlay
    nodes

For p 0.1 100 overlay nodes provide 66.0 of
the achievable improvement. For p 0.5 100
overlay nodes provide 86.4. For p 0.9 100
overlay nodes provide 89.9.
7
Choice of the Topology Model
Uniform random from ?(1050, 0.0196).
Waxman (N 1050, a 0.086, ß 0.2).
Transit-stub 1 transit domain with 50 nodes 1
stub domain per transit node 20 nodes per stub
domain, each domain is a complete subgraph.
8
Alternative Weight Distributions
  • Edge weights WB(vi, vj) and WO(vi,vj) were drawn
    from a bivariate Gaussian distribution.

Positively correlated weights
Negatively correlated weights
  • Gaussian with negatively correlated weights
    produces stretch bounded away from 1.
  • Distribution does not appear to affect the shape
    of the curve.

9
Computational Complexity of Optimal Overlay Search
OPTOVERLAY(G, k) Given a dual-weighted graph G,
find an optimal overlay of size at most k if one
exists.
  • We can show that OPTOVERLAY is NP-complete
  • The proof involves reducing 3-SAT to the decision
    problem Does there exist an optimal overlay of
    size k?

The figure to the left illustrates a sample
construction that corresponds to the Boolean
formula (x1 x2 x3) (x2 x3 x4)
10
Node Selection Heuristics
  • We investigated several node selection
    heuristics, including
  • Selecting a random subset
  • Selecting the best of k random subsets
  • Degree sorting
  • selecting nodes with the highest degree
  • Importance sorting
  • selecting the most important nodes
  • Node importance is defined as the number of
    pairwise shortest paths in the topology to which
    the node belongs.
  • Structure-aware selection (for transit-stub
    topologies)
  • Selecting only the transit nodes
  • Selecting only the periphery nodes
  • Selecting one node from each domain, etc.

11
Heuristics Results
  • Transit-stub topology
  • 4 transit domains
  • 25 nodes per transit domain
  • 1 stub domain per transit node
  • 9 nodes per stub domain
  • Waxman topology
  • N 1050, a 0.086, ß 0.2
  • Degree sorting works best.
  • 1 node from every stub domain works best.

12
Summary and Conclusions
  • Overlay networks work well on a range of
    simulated topology models and most of the
    improvement can be achieved with a fairly small
    selection of overlay nodes.
  • Picking an optimal overlay is hard, but degree
    sorting and other heuristics provide efficient
    approximations.
  • Random node selection works surprisingly well.

13
Heuristics Results
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