scaling properties of the internet graph
DESCRIPTION
Scaling Properties of the Internet Graph. Aditya Akella With Shuchi Chawla, Arvind Kannan and Srinivasan Seshan PODC 2003. Internet Evolution. AS-level graph. AS interconnects: varied capacities. Internet Evolution. Say, network doubles in size. Internet Evolution. - PowerPoint PPT PresentationTRANSCRIPT
Scaling Properties of the Internet GraphAditya Akella
With Shuchi Chawla, Arvind Kannan and Srinivasan Seshan
PODC 2003
Internet Evolution
AS interconnects: varied capacities
AS-level graph
Internet Evolution Say, network
doubles in size
Internet Evolution
Moore’s-law like scaling sufficient?
If so, good scaling!
Double all capacities?
Internet Evolution Plain doubling
not enough?
Moore’s-law like scaling insufficient?
Internet Evolution
Congested hot-spots
If so, poor scaling!!
Plain doubling not enough?
Key Questions
How does the worst congestion grow? O(n)? O(n2)?
How much of this is due to… Power-law structure?
Other distributions Routing algorithm?
BGP-Policy routing Traffic demand matrix?
What can be done? Redesign the network? Change routing?
Outline
Analysis Overview
Results from simulation
Discussion of results, network design
Conclusion
Outline
Analysis OverviewOutline key observations
Results from simulation
Discussion of results, network design
Conclusion
Analysis
To understand scaling properties of power-law graphs Sanity check the (more realistic) simulation results
Simple evolutionary model Preferential Connectivity
Known to yield power-law graphs Unit traffic between all node-pairs
Routed along the shortest path
How does maximum congestion depend on n, the number of vertices? Congestion on an edge == number of shortest path routes using the
edge
Analysis mainly for intuition; simulation results have the final say.
Key Observations (I) e* -- edge between the top two degree nodes s1 and s2.
Observation 1: A significant fraction of single-source shortest path trees (n) trees) in the graph contain e*.
S1
S2
e*
S1
S2
e*
e* occurs in both trees
Key Observations (II)
Observation 2: In at least a constant fraction of the (n) shortest path trees, s1 and s2 retain at least a constant fraction of their degrees.
S1
S2
e*
4/4
4/5S1
S2
e*
5/5
3/4
S1 ,S2 retain most of their degrees
Key Observations (III)
Observation 3: The degrees of s1 and s2 are (n1/).
And
In each tree that e* belongs to, congestion on
e* min{degtree(s1), degtree(s2)}.
S1
S2
e*
So…
Congestion(e*) 3
Key Result
Theorem: The expected maximum edge congestion is (n1+1/) (shortest path routing, any-2-any).
(n1.8) or worse for the Internet. Bad Scaling!
Outline
Analysis Overview
Results from simulation
Discussion of results, network design
Conclusion
Outline
Analysis Overview
Results from simulationMethodologyA few plots
Discussion of results, network design
Conclusion
Methodology: Outline
TopologyPower-law
Real AS-level topologies Inet-3.0 generated synthetic
Exponential Inet-3.0 generated; density same as similar-
sized Inet power-law graphs
Tree-like Grown from the preferential connectivity model
Methodology: Outline
Routing algorithmShortest-pathBGP routing
Policy-based, valley-free Synthetic graphs: heuristically classify edges
before imposing policy routing
Methodology: Outline
Traffic matrixUniform demands: Any-2-any
Between all pairs
Non-uniform: Clout model Between “leaves” or “stubs” Popularity: average degree of the neighbors Stub identification
Methodology: Outline
Topology X Routing X Traffic matrix
We seek Max edge congestion as a function of n
Shortest-Path Routing (Any-2-any)
Exponential >> Power law graphs > Power-law trees
Policy Routing (Any-2-Any)
Poor scaling just like shortest path, but…
Policy Routing vs. Shortest PathAny-2-Any
Synthetic Graphs
Real Graphs
Policy routing is never worse!
The Clout Model
Scaling is even worse
Same true for policy… But policy routing is better again!
Outline
Analysis overview
Results from simulation
Discussion of results, network design
Conclusion
Discussion
Scaling according to Moore’s law insufficientCongested hot-spots in the “core”
May have to alter routing or the macroscopic structureRouting: Diffuse demand in a centralized
mannerStructure: Add additional edges to the graph
Adding Parallel Links
Intuition: Congestion higher on edges with higher avg degree
Adding Parallel Links
#parallel links is dependant on degrees of nodes at the ends of the edge
Candidate functionsMinimum, Maximum, Sum and Product of
degrees Shortest path routing, any-2-any New edge congestion = edge
congestion/#parallel links
Parallel Links
Even min yields (n) scaling!Desirable extent of AS-AS peering
Conclusion
Congestion scales poorly in Internet-like graphs
Policy-routing does not worsen the congestion
Alleviation possible via simple, straight-forward mechanisms