Challenges in the Design and Evaluation of Content -Centric Networks

Challenges in the Design and Evaluation of Content-Centric Networks

Jim KuroseDepartment of Computer ScienceUniversity of MassachusettsAmherst MA USA

‘

Visiting Scientist, Technicolor Paris LabProfesseur Invite, LINCS

Sigcomm ICN Workshop, Aug 2013

Overview

architecture, system, prototype networks of caches

challenges approximation algorithms network calculus for cache networks

workload: logical mobility among networks

Architecture, System, Prototype

architecture

system

prototype(realization)

high-level design/structuring principles, service/function modularity

instantiated set of interoperating protocols, mechanisms, platforms conforming to design principles

Implemented (sub)set of protocols, platforms in particular existing technologies

guides, informs, inspires, constrains

“here’s what it does (function), you tell me how”

*

* ack: D. Clark, J. Wroclawksi


architecture

system

prototype

Minimalist – principles, modularities

Maximalist – protocols, mechanisms go here


architecture

system

prototype

Internettelephony ICN

end-end circuit, stateless endpoints, stateful core, QoS, single service

SS7, ESS, MSC, VLR, HLD

many .. over the years

datagram, stateful endpoints, best-effort stateless core, multiple services, IP

TCP, UDP, DNS, BGP, IS-IS, OSPF


content, naming, stateful core, caching

Ongoing(routing, congestion control, caching, name resolution, search,…)

Ramping up …


architecture

system

prototype








content, stateful core, caching


Ramping up …

OK .. but why care?


architecture

system

prototype

Internet ICN





Architecture research/evaluation more difficult, more “fundamental” (?), more impactful (?)

Research/evaluation mostly here: mechanism, protocols


architecture

system




datagram, stateful endpoints, best-effort stateless core, multiple services




Kleinrock 64Cerf, Kahn 74 Clark 1988 McCanne 1998

?

?Blocking networks

Queueing networksDelay calculusEffective bandwidthsTCP, NUM optimization

Evaluation

?

ICN: architecture vs protocol/mechanism elucidation, value-proposition of design principles,

service/function modularities evaluation tool: networks of caches

Challenges

Interlude ….

Overview




Cache networks

miss

content

miss

miss

miss

miss

consumer requests content request routed (e.g., shortest

path) to known content custodian

en-route to custodian, caches inspect request hit: return local copy miss: forward request

towards custodian during content download,

store in caches along path content requests from different users

interact: cache replacementrequest

contentcustodian

contentcustodian

missmiss

Challenge: networks of caches network effect: interaction among content

request/reply flows from different users: content replacement: requested content by one user

replaces content previously requested by others

x

Circuit-switching:blocking networks(Erlang, 1917, Kelly 1986)

Packet-switching:queueing networks(Kleinrock, 1963)

Content-caching:cache networks

Modeling a network of cachescontent node i: exogenous (external)

arrivals for content j: l(i,j) node i: internal arrivals (miss

stream) for content j from downstream neighbors h: m(j,h)

r(i,j): aggregate rate of arrival requests at i for content j

ZDD: zero download delay assumption

l(i,j)

i

x

w m(j,w)

m(j,x)

r(i,j) = l(i,j) + m(j,h)Sall downstreamneighbors, h

y

m(j,i)

j

Modeling a network of caches SCA: standalone cache i approximation algorithm: given

r(i,j), compute miss rate for all content j Independence Reference Model (IRM) of incoming

requests:

SCA approximation algorithm for LRU: [Dan 1985]

r(i,1)

cache i

Pr(Xt = fj | X1,..,Xt-1) = Pr(Xt=fi)

r(i,n)

m(i,1)

m(i,n)

But we need {(ri,j, mi,j)} for a network of caches

Fixed-point iteration

Set r(i,j)=λ(i,j)

Compute miss rate m(i,j)

Compute arrival rate r(i,j)

Return r(i,j), m(i,j)Using Routing Matrix

Using SCA algorithm

Repeat untilconvergence

Note: tree-network (feed-forward) require single iteration

15

Using SCA algorithm

Quality of a-NET

a-NET: approximation error

line topology with 9 nodes

errors decrease in networks with high node degree

0 1 2 3 4 5 6 7 8 9Cache ID

1.14

1.12

1.10

1.08

1.06

1.04

1.02

1.0

0.98

Sim

/ A

ppro

x M

isse

s

sources of approximation errors in a-NET?SCA algorithm inaccuraciescascading errors

approximated output rates of one iteration is input to next iteration

violating IRM assumed by SCA algorithm miss process for file j negatively correlated

a-NET: error factor analysis

Quality of a-NET Cascade Err. removed Non-IRM removed Quality of SCA

a-NET: error factor analysis

• Factor analysis reveals that non-IRM input to SCA is main cause of error

0 1 2 3 4 5 6 7 8 9Cache ID

1.14

1.12

1.10

1.08

1.06

1.04

1.02

1.0

0.98

Sim

/ A

ppro

x M

isse

s

“Approximate Models for General Cache Networks,” Elisha J. Rosensweig, Jim Kurose, Don Towsley, 2010 IEEE INFOCOM

Overview

• architecture, system, prototype• networks of caches

– challenges– approximation algorithms– network calculus for cache networks

• workload: logical mobility among networks

(σi,ρi) analyses of cache networks

(σi,ρi) bounds # requests for fi over [t1,t2]:

where ri(t) = request rate for fi at time t

iiti tt(t)dtr )( 12

2t

1

Goal: a network calculus for cache networks:(σ1

in,ρ1in)

(σnin,ρn

in)

(σ1out,ρ1

out)

(σnout,ρn

out)

t0t1 t0t1

f1 requests

fnrequests

(σi,ρi) cache networks: observations

not all requests arriving at cache will leave (unlike queue)

t0t1 t0t1

f1 requests

f2 requests

stream of input requests for one file only generates no output

“burst” of request for same file generates one output

interactions among files in cache critical

different intuition (from queues) about “performance damage” of bursts

Building block: miss set, Mi

miss set for fi: set of requests for c unique files, other than i

x1 requests for f1

Mi(x1, …., xn,c): max number miss sets for file fi, given {xiin}

arrivals, cache of size c.properties:

Mi = min(xiin, M)

xiout < Mi, and this bound is achievable

c: cache sizexn requests for fn

w

. . .

. . .

!!

From (σiin,ρi

in) to (σiout,ρi

out): (σiin,ρiin)

(σjin,ρjin)

(σiout,ρiout)

(σjout,ρjout)fj requests

. . .

fi requests

If {(σiin,ρi

in)}ni=1 and {(σ’iin,ρ’iin) }n

i=1 are globally tight and ρi

in = ρ’iin for all i

then ρiout = ρ’iout

Theorem:

iout independent of {σi

in}

. . .

From (σiin,ρi

in) to (σiout,ρi

out): (σiin,ρiin)

(σjin,ρjin)

(σiout,ρiout)

(σjout,ρjout)fj requests

. . .

fi requests

For a cache of size c:

ρiout = min(ρi

in, Mi(ρ1in, … , ρn

in,c ))

Theorem:

Can calculate ρiout from {ρi

in}

. . .

Can compute as welliout

Numerical example

f0,f2

4 files, uniform popularity distribution

f1,f3

cache size = 2 at each node

homogeneous IRM arrivals, exponential interarrival times

Numerical example: bounding results

bound

simulation

mis

s ra

te fo

r f3

Cache ID

ICN: architecture vs protocol/mechanism elucidation, value-proposition of design principles,

service/function modularities modeling networks of caches: develop analytic

models for cache networks (ICN) equivalent of blocking networks (circuit-switched), or

queueing networks (packet switched)? exact in asymptotic regimes? ergodicty?

Challenges

Overview




Characterizing mobility among networks Historic shift from PC’s to mobile/embedded devices

• ~5B cell phones (`1B smart) vs. ~1B Internet-connected PC’s • Mobile data growing exponentially, surpassing wired user

traffic by 2012 [Cisco] any evaluation/model (ICN or otherwise) must consider

mobility “not your father’s mobility:” characterize mobility

among networks• distinctly different from physical mobility, models• physically mobile users may be stationary (from network

transition POV); stationary users may move among networks (multi-homing, multiple devices)

Characterizing mobility among networks Measure mobility among networks via IMAP logs

• online users periodically “push” (background login, check) email, and/or intentionally read mail- e.g., [email protected] generated 7,482 IMAP entries 4/14/13 –

6/4/13• track network location from which IMAP accessed• 70 users, 4/14/13 – 7/5/13, resident in 183 unique AS

numbers

Where do users (in aggregate) spend time?

Mobile: Verizon, AT&T, sprint)Work: 5 college Home: Comcast, Verizon, Verizon, Hughes

Misc: 172 other networks in trace

Users spend most of their time in small number of networks

Where do users (individually) spend time?

Users individually spend most of their time in small number of networks

Multihoming

COMCAST

VERIZON

Multi-homing

Q: How often are users multi-homed? • 15-min subinterval has IMAP access from two different AS’s

Characterizing mobility among networks

other characterizations of mobility among networks generative mobility-among-network model:

parsimonious Markov chain model for individual user transitioning among networks

ICN: architecture vs protocol/mechanism• elucidation, value-proposition of design principles, service/function

modularities modeling networks of caches: develop analytic models for

cache networks (ICN) • equivalent of blocking networks (circuit-switched), or queueing

networks (packet switched)• exact in asymptotic regimes?• ergodicty

traffic models (for ICM and otherwise), with mobile users, content• mobility the norm

Challenges

Conclusion


• challenges• approximation algorithms• network calculus for cache networks


End

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Interesting reading• J. Wroclawski, “All hat no answers: Some issues related to the

evaluation of architecture,” March 2013 NSF FIA meeting, Salt Lake City UT

• D. Clark, “The Design Philosophy of the DARPA Internet Protocols,” ACM Sigcomm 1988, Revised with extensive commentary 2013

• E. Rosensweig, D. Menasche, J. Kurose, “On the Steady-State of Cache Networks,” IEEE Infocom 2013.

• E. Rosensweig, J. Kurose, “A Network Calculus for Cache Networks,” IEEE Infocom Mini-conference 2013.

• E. Rosensweig, J. Kurose, D. Towsley, “Approximate Models for General Cache Networks,” 2010 IEEE Infocom, pp. 1100-1108

• S. Yang, S. Heimlicher, J/.Kurose, A. Venkataramani, “User Transitioning Among Networks - a Measurement and Modeling Study”, submitted, 2014.

Ergodicity of cache networks does steady state performance

depend on initial conditions (ergodicity)? shown existence of non-

ergodic cases (replacement policy, topology, cache size)

derived sufficient conditions for ergodicity topology (single-

custodian trees) from individual ergodicity

to system ergodicity

A B

Requestsfor A

Requestsfor B

A BB A

ergodicty (continued): showed random

replacement: ergodic defined class of non-

protective policies (including LRU): all ergodic

content

Ergodicity of cache networks

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Challenges in the Design and Evaluation of Content -Centric Networks