sterbenz, et al. ittc cross-layering and metrics resilinets and resumenet 07 october 2009 james p.g....

Sterbenz, et al. ITTCCross-Layering and Metrics

ResiliNets and ResumeNet

07 October 2009

James P.G. Sterbenz*†

Джеймс Ф.Г. Стербэнз 제임스 스털벤츠 司徒傑莫David Hutchison,

Abdul Jabbar, Radovan BrunčákJustin P. Rohrer, Egemen Çetinkaya

*Department of Electrical Engineering & Computer Science

Information Technology & Telecommunications Research Center

The University of Kansas†Computing Department, Infolab 21

Lancaster University

[email protected]

http://www.ittc.ku.edu/~jpgshttp://wiki.ittc.ku.edu/resilinets

© 2009 Sterbenz

Sterbenz, et al. ITTC

07 October 2009 Resilience, Survivability, Heterogeneity in Postmodern Internet 2

Cross-Layering and MetricsAbstract

Cross-layer theory: mechanisms and algorithms to control state transactions among several state machines within required level

Metrics: a notation to describe state transactions of particular state machine



ResiliNetsMotivation for Cross-Layering

• D2R2 strategy motivates…• Principles for resilient networking, e.g.

– Adaptability– State management– Multilevel– Context awareness– Autonomic (self-optimization)

• Requires cross-layering• Theory, algorithms, mechanisms and

methodologies to support resilience



Knobs and DialsFormalism

• Set of all knobs = K k– union of out-of-band K and in-band k

• Set of all dials = D d– union of out-of-band D and in-band d

• Set of knobs or dials between layers Li and Lj

– where i and j are either numbers, e.g. {1,1.5,2,2.5,3,4,7}or designators, e.g. {HBH, net, PoMo, E2E, app, …}

– Kij = KK Kij

– vertical when i≠j; horizontal when i=j

• Individual knob or dial between layers Li and Lj

– Kij(d) where d is a descriptor, e.g. BER



Knobs and DialsFormalism

• A protocol instance has– state at time t of s(t)

– context at time t of cn(t)

• At Ln, state is– s(t+1) = f (n+1n, nn–1, s(t), cn)

SM

kn+1n

Ln

Kn+1n

dn+1n

Dn+1n

knn–1 dnn–1

Knn–1 Dnn–1

K,Dnn

k,dnn

cn

s(t)



Cross-Layer ModelProtocol Instance Model

• Node state– state machine– memory

• Node inputs/outputs– vertical data– p2p virtual data– context– horizontal signalling– cross-layer signalling

SM

E2EE/H payloadHBH



ResiliNetsCross-Layer Model

• E2E and HBH layers

HBH HBH HBHHBH HBH

E2E E2E

E2EE/H payloadHBH

E2Econtext

HBHcontext

E2Econtext

HBHcontext

physicalenvironmentdials/knobs

applicationknobs/dials

cross-layerin-band

signallinghorizontal

explicit signalling

verticalexplicit signalling



End-to-End Communication Knobs and Dials

• Knobs and dials between upper layers and PoMo– support heterogeneous subnetworks

• e.g. lossy wireless vs. reliable wired

– explicit signalling of path diversity and multipath• geographic location of realms, nodes, channels

Knobs Dials Layer

application

E2E transport

PoMo internetwork

network realm

HBH link

service characteristics

path char., geography

realm characteristics

link characteristics

service classreliability mode

PoMo knobs, FD, motiv.

realm oper. parameters

link type and codingerror control type/strength


Goal

• Make progress in understanding fundamental principles of cross-layering

• Deliver cross-layer theory– Cross-layer formalisms

• State space, variables, operations, transitions– Cross-layering calculus

• Cost and benefit calculation• Theory to compare cross-layer mechanisms, reason about

costs and benefits• Mechanisms build up on standard building blocks (in-baud vs.

out-of-baud, open vs. control loop, E2E vs. HBH) – Cross-layer evaluation model

• Dynamics of the cross-layer system, avoid instabilities, oscillations, etc.

07 October 2009 9



Case study: error control

• Functional alternativesN noneO open loop (FEC)C closed loop (ARQ)

• S&W, GB-N, SelRep

• Location– HBH– E2E

• App requirements– unreliable– quasi-reliable– reliable

None

OFEC

CARQ

NoneO

FECC

ARQ

E2E

HBH


Approaching problem

• Cross-layer control: between E2E and HBH error control • One hop

– Select error control (ARQ,FEC,NONE) – Setup strength– Local decision only– Inputs: state, context, knob, dials

• Hop-by-Hop– Calculating dials

• End-to-End– Selecting error control and setup strength– Calculating knobs

• Cost contrasting methodology07 October 2009 Resilience, Survivability, Heterogeneity in Postmodern Internet 11


Selecting ER control: Reasoning about costs

• Variables: Capacity, Speed, BER, BER interval, dynamics of BER

• Capacity: High => FECLow => ARQ

• Latency: High => ARQLow => FEC

• BER: HIGH => FEC, ARQLOW => FEC, ARQ

• BER interval: HIGH => FEC, ARQLOW => FEC, ARQ

• BER dynamics: HIGH =>ARQLOW => FEC


€

cos tFEC = cos ti

i=1

n

∑

cos tARQ = cos ti

i=1

n

∑


Cost-Contrasting Methodology


BER interval

BER dynamics

HIGHLOW

ARQFEC

BER


Selecting ER control: Reasoning about benefits

• Guarantee: – HIGH because there is feedback => ARQ – LOW because there is not feedback => FEC

However, it can be HIGH, if estimation of the linkcharacteristics is good. However we need to deal withProbabilities

• Latency– Global remedy (E2E) is slow– Local remedy (HBH) is quick



Setup strength

p – error rate, proportional to BERs – secure constant – depends on state, context (can go even to negative

numbers)m – mode, (unreliable ->0, quasi-reliable -> 0.5, reliable -> 1)?


€

strenghtmechanism = fmechanism = f (knob,state,context,dial) = m(pΔber

Δt+ s)


Dial calculation: possible approaches

• Max BER value• Significant BER change • Average BER value• Number of unmanaged packets



Knob calculation: possible approaches

• Knob– Reliable– Quasi-reliable– Unreliable

• Calculation?



Dynamics of cross-layer system

• Knob can be change changed in real-time• How to describe the dynamics of the underline

system? • How can we describe the state transitions?• How can control theory be applied?



Control theory in cross-layering?



Control theory in cross-layering?

• Dynamics of the cross-layer system?• How knobs can change state of the underline

system ?



Cross-layering calculus

• Decomposition?• Towards a cross-layer calculus:


€

min f (si − oi)i=1

d

∑



Resilience and HeterogeneityEvaluation Methodology: Simulation

• Resilience strategy and principles• Postmodern Internet Heterogeneity• Example realms

– WDTN– highly mobile airborne ad-hoc networking

• Evaluation methodology– simulation– experimentation



Evaluation MethodologyFlexible and Realistic Topology

Generation • Hierarchical topology generation

– evaluation of PoMo mechanisms– network engineering for resilience

• Level 1: backbone realms– nodes distributed based on location constraints– links generated using various models under cost constraints

• Level 2: access network realms– distributed around backbone nodes– access network connectivity: ring, star, mesh

• Level 3: subscribers– distributed around access network node



Evaluation MethodologyChallenge Simulation Module

• Separate challenge from network simulation• Simulate challenges to any network over time interval

– natural disaster destroy network infrastructure (e.g. Katrina)or large-scale grid failure (e.g. Northeast US 2003)

– attack: {node|link} down, wireless link attenuated



Evaulation MethodologyExample: Sprint Actual Topology



Evaluation MethodologyExample: Sprint Synthetic Fragile

Topology




# of nodes down

agg

reg

ate

pa

cke

t de

live

ry r

atio




• KU-CSM Challenge Simulation Module– challenge specification describes challenge scenario– network coordinates provide node geo-locations– adjacency matrix specifies link connectivity– input to conventional ns-3 simulation run– generates trace to plot results

adjacencymatrix.txt 0 0 1 0

simulation description.cc

challenge specification.txt

node coordinates.txt

ns-3 simulator

(C++)

plotted trace

KU-LoCGen



Resilience MeasureTowards a Resilience Metric

• Need: analyse and understand level of resilience• Problem: how to measure

– ideal: = [0,1]– but too many metrics to combine into a single number

• perhaps a small set of objective functions

– need to separate service spec. from operational parms.

• Model resilience as two-dimensional state space– operational state– service level



Resilience MetricsMultilevel Approach

• Resilience defined at any layer boundary– operational state describes system below layer boundary– service states represents behaviour above boundary

• Operational range arbitrarily divided into 3 regions– normal operation– partially degraded– severely degraded

• Service delivered arbitrarily divided into 3 regions – acceptable service– impaired service– unacceptable service



Resilience MetricsStates

• State of the system represented by (N, P) tuple– multivariate operational state N– multivariate service state P

• Initial state described by– acceptable service– during normal operations

• Challenges perturb N and P– resilience trajectory



Resilience State SpaceOperational Resilience

NormalOperation

PartiallyDegraded

SeverelyDegraded

Operational State N

S

• Operationalresilience– minimal

degradation– in the face of

challenges

• Resilience state– remains in

normal operation



Resilience State SpaceService Resilience

NormalOperation

PartiallyDegraded

SeverelyDegraded

Acceptable

Impaired

Unacceptable

Operational State N

Ser

vice

Par

amet

ers

P

S

• Serviceresilience– acceptable

service– in the face of

degraded operation

• Resilience state– remains in

acceptable service

S



Resilience State SpaceResilience Trajectories

• Choose scenario– network– application

• Metrics– choose– aggregate

• Observe– under

challenge

Unacceptable

Impaired

Acceptable

P

[del

ay, p

kt d

eliv

ery

rati

o]

Normal operation

Partially degraded

Severely degraded

S5

S1

S4

p1 > 10 secp2 > 0.85

p1 < 10 secp2 > 0.50

p1 < 1 secp2 < 0.50

n1 ≤ 1.5ρ0 n2 ≥ 3.0

n1 ≤ 3ρ0 n2 ≥ 1.0

n1 > 3ρ0 n2 < 1.0

N [load, node degree]

S3

S2



Resilience EvaluationBasic Approach

• Define service boundary and metrics– boundary Bij between two adjacent layers Lij

– k operational metrics characterize network below Bij

– Nij = {N1, N2, … Nk}

– l service parameters define service across Bij

– Pij = {P1, P2, … Pk}

• Evaluate resilience– operational and service space divided in to regions

– resilience Rij at the boundary Bij is evaluated as ….

… the transition of the network through the state space

– Rij = f (Nij,Pij)



Resilience EvaluationMultilevel

• Mapping of service to operations – service parameters become operations at layer

above• Ni+1,j+1=Pij

– resilience can be evaluated at any arbitrary layer boundary

– resilience as seen by the application is at the L67 boundary



Resilience EvaluationTime Scale

• Steady state analysis– long term view of resilience– theoretical evaluation or simulation study to

understand• impact of perturbations on operations to service being

provided

– best, worst, and mean case type of studies are possible

• Transient analysis– based on instantaneous state of the network– plot service parameters vs. operational metrics in

real time– resilience is characterized by state transitions – shows the impact of resilience mechanisms

• defense, remediation, and recovery



Resilience EvaluationPreliminary Results

• Resilience study at boundary B23

– operational state of network is characterized by• vertices, edges, and edge failures

– service provided across the boundary is a connected graph

– steady state analysis based on simulations

• Sprint case study– operational metric:

• link failures f (fixed nodes and links)

– service parameters: (preliminary, proof of concept)• avg. node degree d

• relative size of largest connected component |cmax|– normalised to number of nodes in graph



Resilience EvaluationPreliminary Results: Sprint Case Study

• 1000 runs• Conventional plot

– limited to1 metric/axis

• Resiliencestate space– discrete

approach– map into

N, P regions



Resilience EvaluationMultilevel Operational vs. Service State

Operational State Ni

Ser

vice

Par

ms.

Pi+

1

Operational State Ni+1 Pi+1

Bi,i+1

Bi+1,i+2

Ser

vice

Par

ms.

Pi+

2



Resilience EvaluationMultilevel Operational vs. Service State

Layer Service Operational State

Application(7)

application behaviour E2E performance (delay, goodput, …)

E2E Transport(4)

E2E data transfer stable E2E path

Inter-realm path(3.5 PoMo)

E2E inter-realm path(routing and forwarding)

stable intra-realm path

Subnetwork path(3)

e2e intra-realm path(routing and forwarding)

topology

Topology connected graph #link cuts, nodes out

HBH(2)

stable linkHBH data transfer

channel conditions




• Conclusions– shows an example of resilience characterization at

L23

– best case scenario, the topology is highly resilient– worst case, single link failure results in unacceptable

service

• Future work– evaluate resilience R34 at boundary B34,

• d and |cmax| become the operational metrics at B34

– compare multiple topologies against each other• Sprint, GÉANT2, synthetic topologies generated with

KULocGen

– conduct transient analysis • see the impact of D2R2



End



End-to-End Communication Knobs and Dials in PoMo Header

• In band: knobs and dials in data packet• Explicit signalling: knobs and dials in signalling packet

realm realm realmrealm

E2E E2E

realminstrumentation

dials/knobs

applicationknobs/dials

cross-layerin-band

signallinghorizontal

explicit signalling

verticalexplicit signalling

sterbenz, et al. ittc cross-layering and metrics resilinets and resumenet 07 october 2009 james p.g....

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