My Thesis Defense Presentation

IntroductionOpportunistic Content Caching

Vehicular Mobility Role in Cooperative Content CachingConclusions and Future Work

Opportunistic and Cooperative Content CachingParadigms in Wireless Networks

Osama Gamal Mohamed Attia

Wireless Intelligent Networks CenterSchool of Communication and Information Technology

Nile University, Egypt

August 5, 2012

1 / 47 Osama Gamal M. Attia Content Caching Paradigms in Wireless Networks



Outline1 Introduction

BackgroundMain ContributionRelated Work

2 Opportunistic Content CachingSystem ModelDMT Framework for Opportunistic Content CachingBaseline RetrievalOpportunistic CachingResults and Insights

3 Vehicular Mobility Role in Cooperative Content CachingMotivationSystem ModelOutage Performance AnalysisPerformance Results

4 Conclusions and Future Work














Content Caching

Content caching has been introduced in the Internet, and later for wirelessextensions, to enhance user experience (retrieval time) and reduce networkload.It allows nodes to store a copy of the data it do request in a previous time slotfor a future use.Different caching paradigms emerged in MANETs:

Non-cooperative: nodes make independent decisions to cache data or paths.Cooperative: exploits the wisdom of the crowd and creates diversity.Opportunistic: utilizes the data sent in the network for future requests.














Main Contribution

In the first part:1 We introduce the novel concept of OCC whereby nodes cache overheard

content delivered by the content server (CS) to nearby nodes.2 We cast the OCC problem into a mathematical framework inspired by the

diversity-multiplexing tradeoff first introduced by David Tse.3 We characterize the diversity gain of OCC and quantify the improvement over

a baseline which does not leverage the inherent broadcast nature of wirelesstransmissions





Main ContributionContinue ..

In the second part:1 Introduce a definition for the Probability of Outage in the context of

cooperative content caching.2 Characterize, analytically, the outage probability under vehicular and random

mobility scenarios.3 Compare, using simulations, the outage performance under sample mobility

regimes.














Related Work

Content caching concept has been first introduced to the Internet, especiallyfor web [Wang ’99, Barish et al. ’00].Cooperative Content Caching in MANETs:

Yin et al., 2006: proposed three schemes for cooperative caching in ad hocnetworks with the objective of reducing the query delay.Fiore et al., 2009: introduced a new metric (presence index) deciding for howlong should a data chunk be cached.

El Gamal et al. 2010: introduced novel proactive resource allocation schemeand analyzed it under DMT framework.Fiore et al., 2007: studied the impact of highway and urban mobility onVANET routing protocols.




System ModelDMT Framework for Opportunistic Content CachingBaseline RetrievalOpportunistic CachingResults and Insights










Assumptions

Time slotted system of a single content server (CS)and multiple nodes.For any node i, let N average number of nodesrandomly dispersed within the CS radio range.A file is composed of m fixed number of chunks.Chunk requests arrive at an arbitrary node i in eachslot according to a Poisson process with rate λi = λ.Requests arrive at the beginning of a slot and eachchunk is retrieved in one slot using one resource(channel).Node i has wireless capacity with total number ofchannels C.Node’s cache size is M chunks, M >> C (supportedby Moore’s law).

i

Delivering requested content to node i

Cache overheard content delivered to node i for T slots





Opportunistic Content Caching Scheme

All nodes run in promiscuouspassive mode.Node i overhears and stores orupdates the new content sent fromthe Content Server to any of thenearby nodes.Node i caches new, or updated,overheard data chunks for T timeslots.Given the overlap in interests, p, aquery issued by node i may beserved from its own cache or fromthe content server (0 ≤ p ≤ 1).

Start

Overhear transmitted data chunks between the

Content Server (CS) and nearby nodes

Encounter a new data chunk?

No

Does it exist in current cache?

Update if newer

Cache for T time slotsNo

Yes

Yes














Diversity-Multiplexing Tradeoff Mathematical Framework

Originally proposed by David Tse et al. for multi-antenna wirelesscommunication.DMT allows analyzing the asymptotic decay rate of outage probability withthe system capacity C.We assume that the total request arrival rate per slot λ scales with capacity intwo different regimes:

Linear Scaling: λ = γCPolynomial Scaling: λ = Cγ

where γ serves as the bandwidth utilization factor, 0 ≤ γ ≤ 1As γ goes to 1, the system becomes critically stable and more subject to outageevents.





Definition of Outage

DefinitionWe define the probability of outage at any arbitrary node as the probability of notbeing able to serve a request within a time slot.

In this case, Opportunistic Caching, an outage event takes place when a node isnot being able to retrieve a requested data chunk, in a given time slot, from thecontent server, or the cached data overheard from chunk retrievals of nearbynodes.





Diversity Gain

Overhearing and caching data chunks retrieved by nearby nodes from theContent Server yields multi-user diversity.Overlapping requests may be resolved locally using the overheard data cachedfrom prior deliveries to the N nearby nodes, at no cost versus download fromthe content server at a delay and delivery cost.We define diversity gain under as follows:

1 Linear Scaling:

d(γ) = limC→∞

− log P(O)C

2 Polynomial Scaling:

d(γ) = limC→∞

− log P(O)C log C














Retrieval from Content Server Only (Baseline)

All the requests will be served by the content server with no provisions forcaching or cooperation among the nodes.The probability of outage, P(O) , will be only the outage at server:

P(O) = Pcs(O)

Let Q(n) be the number of requests at a node in the time slot n. We can expressthe probability of outage as follows:

P(O) = P(Q(n) > C)

=

∞∑k=C+1

e−λλk

k!





Diversity Gain of Baseline Retrieval

From the previous equations, we can rewrite the diversity gain in case of linearcapacity scaling as follows:

dbl(γ) = − limc→∞

1C

log P(Q(n) > C)

Based on the analysis by El Gamal et al., it can be shown that the diversitygain of the baseline no caching system, in case of linear capacity scaling, isgiven by,

dbl(γ) = γ − 1− log γ

Also, in case of polynomial capacity scaling we can write the diversity gain as:

dbl(γ) = − limc→∞

1C log C

log P(Q(n) > C)

= 1− γ














Opportunistic Content Caching (OCC)

In this paradigm, each node has a cache storage that hosts data overheard fromnearby nodes within the past T time slots.The outage probability is the probability of not finding the requested chunk inthe cached overheard data and not being able to retrieve it from the ContentServer due to the limited wireless capacity, C, that is,

P(O) = Pcs(O)Poh(O)N

Poh(O) is the probability of not being able to resolve the query from thecached overheard data.The outage probability Poh(O) equals to the probability that a node of the Nnearby nodes didn’t make any overlapping requests within the last T time slots.





Opportunistic Content Caching (OCC)Continue ..

Poh(O) can be written as follows

Poh(O) =[P(O|Q(n) ≤ C)P(Q(n) ≤ C) + P(O|Q(n) > C)P(Q(n) > C)]T

We know that, the outage probability given the number of requests is less thanor equal C equals to e−pλ which is the probability of not finding overlappingrequests.Also, the probability of outage given the number of requests greater than C isguaranteed to be equal to 1. Hence,

Poh(O) =[e−pλP(Q(n) ≤ C) + P(Q(n) > C)

]T=[e−pλ + (1− e−pλ)P(Q(n) > C)

]T





Diversity Gain of Opportunistic Content CachingContinue ..

By substituting in the outage definition and taking the logarithm:

log P(O) =TN log[e−pλ + (1− e−pλ)P(Q(n) > C)

]+ log P(Q(n) > C)

Simplifying and solving for the linear scaling, we find that,

dopp(γ) = TN min(pγ, dbl(γ)) + dbl(γ)

So, if there is no overlapping requests between nodes (i.e. p = 0), we find outthat dopp(γ) = dbl(γ).However, at the total overlap between nodes’ requests (i.e. p = 1), it is clearthat dopp(γ) = (TN + 1)dbl(γ).Also, solving for the polynomial scaling case show that no improvement overbaseline-retrieval:

dopp(γ) = dbl(γ) = 1− γ














Results and Insights

We show the result of the probability of outage under the linear andpolynomial capacity scaling cases.WLOG, We plotted the curves at an arbitrary values listed in the table below inorder to show the improvement of the opportunistic content caching over thebaseline scenario.

Parameter ValueMultiplexing gain (γ) 0.75

Interest overlap probability (p) 0.6Number of neighbor (N) 3 nodes

Caching time (T) 6 slots





Outage probability performance

0 10 20 30 40 50 6010

−40

10−30

10−20

10−10

100

Capacity (C)

Outa

ge P

roba

bili

ty P

(O)

Baseline

Opportunistic

(a) Linear Scaling Case

0 10 20 30 40 50 6010

−50

10−40

10−30

10−20

10−10

100

Capacity (C log C)

Outa

ge P

roba

bili

ty P

(O)

Baseline

Opportunistic

(b) Polynomial Scaling Case





Diversity-Multiplexing Tradeoff

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Div

ers

ity

Multiplexing Gain (γ)

Baseline

Opportunistic

(c) Linear Scaling Case

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Div

ers

ity

Multiplexing Gain (γ)

Baseline

Opportunistic

(d) Polynomial Scaling Case

No improvement in terms of diversity gain for the polynomial scaling case. Thiscould be justified since the content caching scheme under polynomial scaling withan overlapping factor that grows as e−pCγ

is very slow.27 / 47 Osama Gamal M. Attia Content Caching Paradigms in Wireless Networks




The effect of interest overlap probability

0 2 4 6 8 10 12 14 16 18 2010

−25

10−20

10−15

10−10

10−5

100

Capacity (C)

Outa

ge P

robabili

ty P

(O)

Baseline

Opportunistic (p = 0.1)



Opportunistic (p = 1)

(e) Linear Scaling Case

0 10 20 30 40 50 6010

−60

10−50

10−40

10−30

10−20

10−10

100

Capacity (C log C)

Outa

ge P

robabili

ty P

(O)

Baseline




Opportunistic (p = 1)

(f) Polynomial Scaling Case




MotivationSystem ModelOutage Performance AnalysisPerformance Results










Motivation

Vehicular Ad hoc Networks (VANET) is a promising emerging networkingparadigm.VANETs are envisioned to improve the driving experience and save lives onthe roads.Cooperative content caching (CCC) is a plausible technology for contentdelivery in VANETs.Content delivery to mobile platforms, e.g., vehicles, from infrastructure isresource- and time-consuming. Hence, cooperation presents an opportunity.Is there a performance gain for the vehicular mobility over random mobility.If there is a performance gain, how to quantify it?














System Model

We assume toy model of two nodes (adequate tocapture the problem).Users are interested in items where each informationitem consists of multiple chunks.Nodes starts with empty caches.Chunk requests arrive at node i according to aPoisson process with rate λ.Fixed transmission power which translates to acircular range of radius r.If the requesting node gets a query resolved, it cachesa copy of the chunk for an arbitrarily long time.

r

n1 n2

θ

x

x

l

X

Y

x

Direction ofMovement





Mobility Models

Random Mobility:x: Distance between the twovehicles, x ∼ Uni[−r, r].v: Relative velocity,v ∼ Uni[vmin, vmax].θ: Direction of movement,θ ∼ Uni[θmin, θmax].

Vehicular Mobility:x ∼ Uni[−r, r].v ∼ Uni[vmin, vmax].Direction of movement isdeterministic, θ = π/2 for astraight freeway segment.

rn1 n2

θ

x

x

l

X

Y

x

Direction ofMovement














Probability of Outage

DefinitionWe define the probability of outage, Pn1

o , as the probability of not finding a datachunk at a single-hop neighbor within time period (t, t + τ).

Pn1o can be defined as the complement of the probability of node n1 finding a

chunk, denoted Pn1f .

The event of finding a data chunk happens when 3 independent events jointlytake place:

n2 requests at least a chunk within the period τ .There is an interest overlap with probability γ.The two nodes are within the communication range (Pneigh).

Pn1o = 1− Pn1

f

= 1− γ(1− e−λτ )Pneigh





Quantifying Pneigh in Random Mobility

n2 will stay within the radio range of n1 aftertime τ iff if vτ is less than or equal todistance l.

l =√

1− x2 sin2 θ − x cos θ

Hence,

Pneigh = P(vτ ≤ l)

= P(vτ ≤√

1− x2 sin2 θ − x cos θ)

=

∫∫∫x,u,θ∈Dr

f (x, u, θ)dx du dθ

The integration is solved numerically due toits complexity.





Quantifying Pneigh in Vehicular Mobility

In this case θ = π/2, and,l =√

1− x2. Hence,

Pneigh = P(τvmin ≤ u ≤ min(τvmax, l))

=

∫∫x,u∈Dv

f (x, u)dxdu

=

∫ umax

umin

√1− u2

umax − umindu

Dv is the region over which x and usatisfy the inequality:

τvmin ≤ u ≤ min(τvmax,√

1− x2)














Simulation Settings

We develop Matlab simulations to verify the analytical results.Analytical and simulation results are generated using the following systemparameters:

Parameter ValueOverlap ratio (γ) 0.7

Requests arrival rate (λ) 3 requests/secRadio range (r) 150 m

Minimum relative speed (vmin) 5 km/hrMaximum relative speed (vmax) 50 km/hr





Performance Results

0 20 40 60 80 100 1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

τ (sec)

Pro

babili

ty o

f bein

g in r

each (

Pn

eig

h)

Random Mobilty (Analysis)

Vehicular Mobility (Analysis)

Random Mobility (Simulation)

Vehicular Mobility (Simulation)

(g) Probability of being in reach

0 20 40 60 80 100 120

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

τ (sec)

Pro

babili

ty o

f O

uta

ge

Random Mobilty (Analysis)

Vehicular Mobility (Analysis)

Random Mobility (Simulation)

Vehicular Mobility (Simulation)

(h) Outage Probability

For the range of Po of practical interest, vehicular mobility has lower probability ofoutage than random mobility.





Performance ResultsContinue..

Comparing random mobility to road width-limited vehicular mobility (5-lanefreeway with 4 meters lane width).

0 20 40 60 80 100 120

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

τ (sec)

Pro

ba

bili

ty o

f O

uta

ge

Random Mobility

Vehicular Mobility

Confirms the superiority of vehicular mobility especially in the practical range ofinterest.




ConclusionsFuture WorkPublications

Conclusions

In the first part: Then, w Follows,We proposed a new paradigm for content caching that involves exploiting theprior resolved queries of the neighbor users for future requests.We formally set forth the definition of outage event in lights of a plausiblesystem model.We conducted diversity-multiplexing tradeoff analysis (diversity as chances ofresolving queries in terms of number of nodes and time slots).We evaluated, mathematically, the outage probability and diversity gains of thesystem under different settings.Finally, numerical results that validate our claims are shown and insights aredrawn.





ConclusionsContinue ..

In the second part:We introduced a formal definition for the probability of outage in the contextof cooperative content caching.Then, we characterized, analytically, the outage probability under vehicularand random mobility.We verified the analytical results using simulation studies which exhibitcomplete agreement.Results confirm the opportunity created by the structured vehicular mobilitywhich would inspire future cooperative caching schemes.The numerical results demonstrate up to 32% improvement in the outageperformance (and 16% on the average) for the studied plausible scenarioswhere the probability of outage is below 0.5.





Future Work

Our work in the first part could be oriented as follows:Implement a distributed algorithm that makes use of the main characteristics ofOCC paradigm.Analyzing on the effect of mobility patterns on the opportunistic cachingparadigm.Extend the opportunistic content caching scheme considering the privacy andanonymity issues.Develop a distributed and cooperative algorithm to calculate the optimumcaching time for a specific data chunk in order to utilize the node’s storage.





Future WorkContinue ..

The second part of this work can be extended along the following researchdirections:

Generalize the model to relax few assumptions of practical relevance (N, γ,Tc).Model and quantify the diversity gains attributed to nodes’ cooperation.Quantify the outage performance for other vehicular mobility models.Quantify the cooperation diversity gain that is above and beyond the mobilitygains explored here.Develop novel cooperative caching schemes that capture the structured natureof vehicular mobility.





Publications

Osama Attia, Tamer ElBatt, "On the Role of Vehicular Mobility in CooperativeContent Caching", accepted in IEEE WCNC 2012, Vehicular Workshop, April,2012.

Osama Attia, Tamer ElBatt, "Opportunistic Content Caching in WirelessNetworks", under submission.





Thank You!

Any Questions?


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