network coding tomography for network failures
DESCRIPTION
Network Coding Tomography for Network Failures. Hongyi Yao. Sidharth Jaggi Minghua Chen. Tomography (CAT Scan). Computerized Axial. 1. Tomography. Heart. Y=TX T?. 2. Network Tomography. [V96]…. Objectives : Topology estimation Failure localization. @#$%&*. 001001. - PowerPoint PPT PresentationTRANSCRIPT
Network Coding Tomography for Network Failures
Sidharth JaggiMinghua Chen
Hongyi Yao
Tomography (CAT Scan)
Computerized Axial
1
Tomography
Heart
2
Y=TXT?
Network Tomography[V96]…
Objectives:•Topology estimation•Failure localization
Failure type:•Adversarial error: The corrupted packets are carefully chosen by the enemies for specific reasons. •Random error: The network packets are randomly polluted.
001001
@#$%&*
3
Tomography type Active tomography[RMGR04,CAS06]:
All network nodes work cooperatively for tomography. Probe packets from the sources are required. Heavy overhead on computation & throughput.
Passive tomography [RMGR04, CA05, Ho05, This work]: Tomography is done during normal communications. Zero overhead on computation & throughput.
4
Network coding Network coding suffices to achieve
to the optimal throughput for multicast[RNSY00].
Random linear network coding suffices, in addition to its distributed feature and low design complexity[TMJMD03].
S
r1 r2
m1
m1
m2
m2
m2m1
m1+m2am1+bm2
5
Source: Sends packets. Organized as:
Internal Nodes: Random linear coding
Sink gets Y:
Random Linear Network Coding
TX TY=T
X I
v1
v2a1v1+a2v2
Information T: Recover Topology [Sharma08]
6X I =
v1 v2
a1v1+a2v2
Network Coding Aids Tomography
Routing scheme is used by u: x(e3)=x(e1), x(e4)=x(e2).
Probe messages: M=[1, 2]
su
r
e1
e2
e3
e4
1
2
x=2
3
2
3
2
7
5
YE=[3, 2] YM=[1,2]E=YE-YM=[2,0]x[1,0]x[0,1]
e3e1
3+2
3+2 2.YE=[7, 5] YM=[5,3]E=YE-YM=[2,2]
3
2
e1
Network coding scheme is used by u:x(e3)=x(e1)+2x(e2), x(e4)=x(e1)+x(e2).
x[1,1]x[2,1]
x
xx
x
Routing scheme is not enough for r to locate error edge e1.
Network coding scheme is enough for r to locate error edge e1.
7
x
0x
2x
back
Summary of Contribution
Topology estimation
Failure localization
Passive tomography for random linear network coding
Adversary error
Random error
Exponential Hardness proof
Exponential[HLCWK05]
Polynomial
No result
Polynomial
Exponential[FM05,HLCWK05]
It turns out that the idea underlying the example holds even the coding is done in a random fashion.
Random linear network coding has great advantages.
Passive = low overhead.
WHY?
No result
8
Failure type
[This work][This work]
[This work] [This work]
Core Concept: IRV
Edge Impulse Response Vector (IRV):The linear transform from the edge to the receiver.Using IRVs we can estimate topology and locate failures.
1
1
1
[1 0 0]
1 00
3
12
3
9
3
2 9 6
[0 3 2]
[2 9 6]
26
2 3
3
1
2 41. Relation between IRVs and network structure:
e1
e2
e3
IRV(e1) is in the linear space spanned by IRV(e2) and IRV(e3).
0 0
9
2. Unique mapping from edge to IRV: For random linear network coding, two independent edges has independent IRVs with high probability.
Network tomography by IRVs
The concept of IRV significantly aids network tomography: The relations between IRVs and
network structure is used to estimate network topology.
The unique mapping between network edge and its IRV is used to locate network failures.
Topology Estimation for Random Errors
Why study random failures:
For network without errors, the only information about the network is the transform matrix T. Thus recovering network topology is hard [SS08].
Surprisingly, for network with random failures (errors, or packet loss), the IRV of the failure edge will be exposed, letting us recovering network topology efficiently.
Topology Estimation for Random Errors Stage 1: Collect IRVs
[2,1]
[3,2]
E1=4 , 227 , 1518 , 10
[1,3]
[1,1]
E2=0 , 03 , 36 , 14
<E1> <E2>= < >[0 3 2]
[0 3 2]
10
Stage 2: Recover topology
[0 3 2]
[2 9 6]
[0 0 4]
IRVs from Stage 1: [0 3 2]
[1 0 0]
[0 1 0]
[0 0 1][0 0 2]
[2 9 6]
[0 0 4]
According to: IRV(e1) is in the linear space spanned by IRV(e2) and IRV(e3).
e1
e2 e3
11
Topology Estimation for Random Errors
Random Failure Localization
Locating random failures:
[2,1]
[3,2]
[0 3 2]
[1 0 0]
[0 1 0]
[0 0 1]
[0 0 2]
[0 3 2]
[2 9 6]
[0 0 4]
IRVs:
[2 9 6]
E= [2,1] + [3,2] =
[2 9 6]
[0 3 2]
4 , 227 , 1518 , 10
in < >?[4 27 18]
[2 15 10]
[2 9 6]
[0 3 2]
Preliminaries: The Impulse Response Vector (IRV) of each edge.As long as the topology is given, we can do error localization.
12
Exp
Summary of our contribution
Topology estimation
Failure localization
Adversary error
Random error
Exponential Hardness proof
Exponential[HLCWK05]
Polynomial
No result
Polynomial
Exponential[FM05,HLCWK05]
No result
Failure type
[This work][This work]
[This work] [This work]
Future direction Current work: From existing good
network codes to tomography algorithms. Another direction: From some criteria to
new network codes. For instance, network Reed-Solomon
code[HS10], satisfies: Optimal multicast throughput Low complexity and distributed designing. Significantly aids tomography:
Failure localization without centralized topology information.
Adversary localization can be done in polynomial time.
Related works
Network Coding Tomography for Network Failures
Thanks!
Questions?
14
Details in: Hongyi Yao and Sidharth Jaggi and Minghua Chen, Network Tomography for Network Failures, under submission to IEEE Trans. on Information Theory, and arxiv: 0908-0711