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The Evolution of Layered Protocol Stacks Leads to

an Hourglass-Shaped Architecture

Saamer Akhshabi Constantine Dovrolis

Georgia Institute of Technologys.akhshabi,constantine@gatech.edu

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My co-author, Saamer Akhshabi(Very smart 2nd year PhD student,

he could not travel to Toronto)

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Outline• Motivation• Model description• Results• Concluding remarks

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Why did we write this paper?

Ethernet

Twisted Pair

SMTPHTTP

Thunderbird

SilverlightFirefox

PPP

Coaxial Cable

IPv4

UDPTCP

RTP

Optical Fiber

MPlayer …

…

…

…

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Why is the Internet protocol stack an hourglass?

Why ?-Random?-Designed?-Emergence?

Ethernet

Twisted Pair

SMTPHTTP

Thunderbird

SilverlightFirefox

PPP

Coaxial Cable

IPv4

UDPTCP

RTP

Optical Fiber

MPlayer …

…

…

…

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What happens at the “waist” compared to other layers?

Frequent innovations

Frequent innovations

Conserved (“ossified”)

Ethernet

Twisted Pair

SMTPHTTP

Thunderbird

SilverlightFirefox

PPP

Coaxial Cable

IPv4

UDPTCP

RTP

Optical Fiber

MPlayer …

…

…

…

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How can a new protocol survive at the waist?

X.25SNA

Ethernet

Twisted Pair

SMTPHTTP

Thunderbird

SilverlightFirefox

PPP

Coaxial Cable

IPv4

UDPTCP

RTP

Optical Fiber

MPlayer …

…

…

…

ATM IPv6

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What about “Future Internet” those architectures?

• Will these architectures also evolve to an hourglass in few years?

• How to make them more “evolvable”?– So that they can better

accommodate innovation?

– So that no single protocol at the waist “kills” all competitors

NDN XIAMpbilityFirst

Nebula

?

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Outline• Motivation• Model: EvoArch• Results• Conclusions

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Two Disclaimers• EvoArch is only an abstraction of protocol

stacks– EvoArch does not capture many practical aspects

and protocol-specific or layer-specific semantics

• EvoArch is certainly not the only model, or “the correct model”, for the emergence of hourglass-shaped network architectures– Models should be judged based on their

assumptions, parsimony and predictions

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Model description

Protocols as nodes

Protocol dependencies as edges Products: P(u)

Substrates:S(u)

Layer of u: l(u)

Layered acyclic network

u

Every layer provides a service

L

1

…

4

3

2

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The value of a protocol• The value of a protocol depends on the

value of its products• Protocols with valuable products are more

valuable

1 1

5

2

5

3

1 11111

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The generality of a layer

Ethernet

Twisted Pair

SMTPHTTP

ThunderbirdSilverlightFirefox

PPP

Coaxial Cable

IPv4

UDPTCP

RTP

Optical Fiber

MPlayer

As we go higher in the stack:• Protocols become less

general – they offer more specialized services

• The probability that a protocol is used by next-layer’s protocols decreases

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Generality as a probability• We introduce a

parameter called generality vector s

• s(l) : probability that new node at layer l+1 chooses each node at layer l as substrate

• s(l) decreases as we go higher in protocol stack

s(1) = 0.9

s(3) = 0.5

s(L-1) = 0.1

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Competition between protocols

• Two protocols at the same layer compete if they offer similar services– i.e., if they have large overlap in their products

• HTTP competes with FTP due to several overlapping products

• TCP does not compete with UDP because they have minimal service overlap

TCP UDP

HTTP FTP

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Modeling competition

• If c = 3/5• u competes with

q and w• q does not

compete with w q u w

• Let C(u) be set of competitors of u• Node w competes with u if

• c: competition threshold

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When does a protocol “die”?• Protocols can become extinct due to

competition with other protocols• For example, HTTP services cover the set

of services provided by FTP

• Competition from HTTP has led to FTP’s demise

HTTP FTP

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Modeling protocol deaths• A node u dies if its value is significantly less than the value of

its strongest (i.e., maximum value) competitor.

• z: mortality parameter

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Cascade deaths• u is w’s competitor• Suppose that w dies due to competition

with u (r=3/7)

2 1

4

2

7

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3

1

1 1 1 11 1 1

q u w

If a node w dies, its products also die if their only substrate is w. This can lead to cascade deaths.

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Protocol births• Basic birth process

Number of new nodes at given time is a small fraction of total number of nodes in network at that time.

New nodes assigned randomly to layers• Death-regulated birth process

The birth rate at a layer is regulated by the death rate in that layer

Discussed later

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Summary of EvoArch• Discrete-time model– Time advances in rounds

• Each round includesbirth of new nodescompetition among nodes at the same layerpotentially, death of some nodes

• Key parameters– Generality vector s– Competition threshold c– Mortality parameter z

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Outline• Motivation• Model Description• Results–Emergence of hourglass structures –Controlling the location/width of the waist–Evolutionary kernels– Protocol differences

• Conclusions

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Hourglass shape

• The network forms an hourglass structure over time• The waist usually occurs at layer 5 or 6.

L = 10c = 3/5z = 1s(l) = 1-l/L

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Hourglass Resemblance Metric

w(l) : width of layer l Minimum occurs at layer b X = {w(l), l = 1, . . . b} Y = {w(l), l = b, . . .L} Mann-Kendall statistic for

monotonic trend on the sequences X and Y: coefficients τX and τY

H = (τY – τX)/2

• H=1 when widths first decrease and then increase (monotonically) Width

Laye

r num

ber

w(1)w(2)

…

w(b)

w(b+1)

…W(L)

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Robustness

• High hourglass scores under a wide range of parameters

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Why does EvoArch generatehourglass-shaped networks?

Small generality Low competition (local)Low death probability

Large generalityFrequent competitionProtocols have similar substrates & valuesLow death probability

Generality close to 50%Few protocols with many productsMost other protocols die

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How can we get a wider waist?

• As γ increases– Location of the waist

moves to higher layers– Width of waist

increases

• γ is the layer at which the generality is 50%

Layer numberγ

0.5

s(l)

Evolutionary kernels

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How can a kernel die?• Normalized value of a node: value divided by

maximum possible value at that round

• If several nodes appear at the next higher layer, andkernel fails to quickly acquire those new possible products,someone else may do so..

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Death-regulated birth process?• What if the birth probability in a layer is regulated

by the death probability in that layer?

• It becomes practically impossible to replace kernels

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What if protocols differ in term of a “quality factor”?

• The “quality factor” can be interpreted broadly Performance, Extent of deployment, Reliability or security, Incremental improvements, etc

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Effects of quality factor• We still get an hourglass• Lower part of hourglass is smaller in size

– only high quality nodes survive at the lower part• Kernels are often NOT the highest quality

protocols

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Outline• Motivation• Model Description• Results• Concluding remarks

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What does this mean for the Internet architecture?

• New way to think about (and teach) Internet’s hourglass architecture

• New way to think about “ossification” of protocols at the waist

• Parameterized model for TCP/IP stack: – Two protocols compete when their service

overlap is more than 70%– A protocol survives only if its value is more

than 90% of its strongest competitor’s value– Death-regulated births

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What does this mean for IPv4 vs IPv6?

• IPv6 has same products but lower extent of deployment (i.e., lower “quality factor”)

• IPv6 would find it easier to compete w IPv4 if:– It had some distinct products that IPv4 does

not have– Unfortunately, it only offers more addresses

• IPv6 would face easier adoption if it was not presented as “IPv4 replacement” but as “the second network-layer protocol”

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What does this mean for future Internet architectures?

• Hourglass structures should be expected if these new architectures evolve/compete

• Designers should strive for wider waist– More diverse waist -> more evolvable

architecture• EvoArch: as the waist moves higher, it also

becomes wider– How to push the waist to a higher layer?– See highly relevant paper:

• L. Popa, A. Ghodsi, and I. Stoica. HTTP as the Narrow Waist of the Future Internet. In ACM SIGCOMM HotNets, 2010

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From Networking to Network Science

• Hourglass effect in development of embryos

• Hourglass effect in organization structures• Hourglass effect in innate immune system

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Thanks to Todd Streelman (School of Biology, Georgia Tech)

Soojin Yi (School of Biology, Georgia Tech)

National Science Foundation (NSF)