How Mobility Increases Mobile Cloud ComputingProcessing Capacity

Anh-Dung Nguyen∗†, Patrick Senac∗† and Victor Ramiro‡

anh-dung.nguyen@isae.fr, patrick.senac@isae.fr, vramiro@niclabs.cl∗ISAE/University of Toulouse, Toulouse, France

†LAAS/CNRS, Toulouse, France‡NIC Chile Research Labs, Santiago, Chile

Abstract—In this paper, we address a important and stillunanswered question in mobile cloud computing “how mobilityimpacts the distributed processing power of network and com-puting clouds formed from mobile ad-hoc networks ?”. Indeed,mobile ad-hoc networks potentially offer an aggregate cloud ofresources delivering collectively processing, storage and network-ing resources. We demonstrate that the mobility can increasesignificantly the performances of distributed computation in suchnetworks. In particular, we show that this improvement can beachieved more efficiently with mobility patterns that entail adynamic small-world network structure on the mobile cloud.Moreover, we show that the small-world structure can improvesignificantly the resilience of mobile cloud computing services.

Index Terms—Mobile cloud computing, mobility, quality ofservice

I. INTRODUCTION

“Cloud computing” has recently appeared as a buzz word inmany medias in which the term refers both to the technologyadvancement and also to the business model behind. The ideais not new but roots from already developed technologiessuch as distributed computing, autonomic computing, hard-ware virtualization and web services. It’s the maturation andconvergence of all these technologies that makes cloud com-puting viable today. By virtualizing the aggregated computingresources in order to offer to users the on-demand utility(e.g. computing, storage, software as service) in a pay-as-you-go fashion, much like the power distribution grid system,cloud computing appears as a main actor of informationindustry today. This can be seen through the explosion of cloudcomputing services deployed the Internet in recent years.

Besides, with the advances of electronic technologies, mo-bile wireless devices have gradually become more and morepowerful in terms of processing, storage and communicationcapacity. This potentially leads to the emergence of mo-bile ad-hoc networks that deliver, without any infrastructure,computing resource complementary to the existing infrastruc-tured networks. These “mobile clouds”, which leverage onopportunistic contacts between users, can potentially deliverfree communication, storage and processing services sharedbetween users according to peer to peer resource sharingpolicies.

Although the application perspective sounds interesting, theunderlying technology challenges are not negligible due tothe difficulties raised by dynamic networks. The first obstacle

comes from the mobile nature of such network and raisesthe question of “how the mobility impacts the distributedprocessing performances of the mobile clouds?”. Indeed, theunstable network topology makes that continuous end-to-endcommunication unguaranteed and hence the service deliverymay be disrupted. Indeed, in the context of spontaneous andinfrastructureless networks, a kind of delay tolerant network,nodes must rely on intermittent contacts leading to use thestore-carry-and-forward communication paradigm for inter-node communication. Therefore, if the role of mobility oncommunication performances such as end to end delay andbandwidth has been already studied, the impact mobilityschemes on the global processing power delivered by a mobilenetwork cloud has not been studied yet.

In this paper, we address this issue and show that themobility can enhance significantly the computing capacityof network clouds composed of mobile nodes. Consideringa dynamic network as an aggregate distributed computingresource, we use Particle Swarm Optimization (PSO) - anoptimization method based on distributed autonomous agents-coupled with a generic mobility model to assess the impactof node mobility on distributed processing. The questions onservice resilience against network churn are also discussed.

The rest of the paper is structured as follows. First, SectionII discusses the state of the art of mobile cloud computing. InSection III, we study the impact of mobility on the qualityof mobile cloud computing services. Section III studies ofthe impact of dynamic network structures on mobile cloudcomputing. In Section V, the question of service resilience isdiscussed. Finally, Section VI concludes the paper.

II. STATE OF THE ART

Mobile cloud computing is still a young field and thereis still discussion on its definition. In its infancy, mobilecloud computing has been considered as a derived branch ofcloud computing with two schools of thought (see [7] for asurvey). The first refers to performing computing activities(data storage and processing) in infrastructured cloud and letmobile devices be simple terminals to access to service. Thiscentralized approach has the advantage that mobile devicesdon’t need to have a powerful computing capacity but thedrawback is that users depend strongly on the infrastructurenetwork and on its performances.

The second school of thought defines mobile cloud com-puting as performing computing activities on mobile platform.Therefore a mobile cloud network is an infrastructureless ex-tension of the traditional infrastructure based cloud networks.Mobile devices are clients of service but are also part of thecloud, providing hardware and software resources. The benefitof this distributed approach is the omnipresence and the speedof service accessibility, the support of mobility and locality, thefreedom of deployment and use of new services as well as thereduced hardware maintenance costs. Although the approachis promising, its main challenge resides in the dynamic ofnetwork which poses difficulties in communication and henceservice access. In this paper, we focus on this definition ofmobile cloud computing.

To the best of our knowledge, very few contributions havebeen proposed for mobile cloud computing. Hyrax [2] is amobile-cloud infrastructure that enables smart-phone applica-tions that are distributed both in terms of data and computation.Hyrax allows applications to conveniently use data and executecomputing jobs on smart-phone networks and heterogeneousnetworks of phones and servers. Its implementation is basedon Hadoop and tested on Android platform. But since An-droid doesn’t support ad-hoc network yet, the phones have tocommunicate through a WIFI central router.

Satyanarayanan et al. [4] present the cloudlet concept.In this approach a mobile client is seen as a thin clientwith respect to a service which is customized over a virtualmachine in the wireless LAN. Hence the cloudlet is a proxyrepresentation of a real service enhanced for the mobile device.The main motivation is how bandwidth limits and latency overwireless networks impacts over users services.

III. IMPACT OF MOBILITY ON MOBILE CLOUDCOMPUTING

In this section, we evaluate the impact of mobility on mobilecloud computing. Let us consider that a mobile cloud networkcreated by several human portable devices offers a distributedprocessing service such as optimizing a function via a ParticleSwarm Optimization (PSO) algorithm. According to PSO,each node in the network has a local solution to the optimiza-tion problem. Through intermittent contacts, mobile nodeslearn others’ solution to improve their local optimum andhence accelerate the convergence towards the global optimum.For the sake of simplicity, we make the following assumptions:

1) Each node in the network knows in advance the goalfunction and its solution.

2) An external system (e.g. WIFI hot-spots) is responsiblefor results retrieval from mobile nodes.

3) The service is considered delivered when the globaloptimum reaches a goodness predefined by user.

In practice, the goal function as well as its solution is usuallyunknown in advance and therefore we have to rely on adiffusion technique to disseminate the information of the goalfunction into the network. The obtained global optimum andstopping condition in that case will depend on the currentsolutions found by nodes (e.g. a node stops the computation

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Fig. 1. STEPS markovian mobility model

when its local solution no longer changes for a while). In thistheoretical work, we focus only on the impact of mobility oncomputing delay and therefore the previous assumptions seemreasonable.

A. Mobility model

In order to reproduce at the simulation level realistic humanmobility patterns, we use the STEPS mobility model [3]. Aswe have shown in a previous work, this flexible parametricmodel can express a large spectrum of mobility patterns: fromhighly nomadic ones to localized ones. Therefore, STPESmakes it possible to evaluate the impact of different mo-bility contexts on mobile cloud computing. In STEPS, thenetwork area is modeled as a torus divided in several zones.The model implements the notion of preferential attachmentusually observed in human mobility in which each node isattached to one or several preferential zones. Inside zones,mobile nodes move according to the Random Waypoint model.The movement of nodes between zones follows a Markovchain of which the transition probability is given by a powerlaw distribution. This distribution is driven by a parameter ofthe STEPS model which allows the nodes nomadism to beenforced or reduced (i.e. the probability that a mode movingoutside his preferential zone has to return to that zone).Figure 1 illustrates the underlying Markov chain of STEPSwith 4 zones.

B. Particle Swarm Optimization

The Particle Swarm Optimization (PSO) algorithm [1] is anoptimization method based on swarm intelligence - a sub-fieldof artificial intelligence which studies the collective intelligentbehavior emerging from the interactions between individu-als of a swarm of autonomous agents. Swarm intelligenceconsiders intelligence as the combination of the knowledgeacquired by individuals through experiences in the past andthe knowledge acquired from the others through social in-teractions. In PSO algorithm, a set of candidate solutionscalled particles move around in the search space accordingto a simple mathematical formula over the particle’s positionand velocity. Each particle’s movement is influenced by itslocal best known position and also by the global best knownposition found by other particles. The swarm is expected to

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Fig. 2. Typical static neighborhood topologies vs dynamic neighborhoodtopology generated by STEPS

move collectively towards the optimal solution. Besides, thismethod is able to solve a multimodal optimization problem.

In its simplest form, let ~xi be the multidimensional vectorof the particle i position, the position of the particle is updatedaccording to the formula

~xi(t+ 1) = ~xi(t) + ~vi(t) (1)

where ~xi(t) is the position of particle i at time t.The velocity of the particle is updated according to the

formula

~vi(t) = ~vi(t− 1) + φ1 [~pi − ~xi(t− 1)] + φ2 [~pg − ~xi(t− 1)](2)

where• φ1, φ2 are uniform random variables taking values in

[0, 1]. These variable represent the relativity between theeffect of individual experience and of social influence.

• ~pl denotes the best known position of particle i (“l” forlocal).

• ~pg denotes the best known position of i’s neighbors (“g”for global)

This formula entails wider and wider oscillations of particlesin the search space. One solution to this issue is based onvelocity damping, that is, if vid > Vmax then vid = Vmax

else if vid < −Vmax then vid = −Vmax where vid is thedimension d of ~vi. In consequence, the particles move only ina restricted search space.

In PSO, individual can be connected to one another ac-cording to a great number of neighborhood topologies (Figure2 illustrates the most used schemes). Each neighborhoodtopology, traditionally considered as static conversely to ouranalysis, results in different behaviors and performances forthe PSO algorithm.

In this work, since node moves, the neighborhood topologyis no longer static but dynamic. Indeed, when the mobilitydegree is low, links between nodes is stable and the networkis nearly static. On the contrary, when the mobility is high,links change rapidly over time and so does the neighborhoodtopology. Therefore the goal of this experiment is to evaluatethe effect of mobility on the convergence delay of the algo-rithm.

C. Simulation Results

We implemented the mobility model and the PSO algorithmon MATLAB. At the beginning of each simulation, 100 nodesare uniformly distributed over the network area which is

Number of nodes/particles 100Number of zones 10× 10

Network size 100× 100 m2

Radio range 10 mNode speed 3− 5 km/h

De Jong function SphereNumber of dimensions 2

Stopping condition error <= 10−6

TABLE ISIMULATION SETTINGS

divided in 10× 10 zones representing preferential attachmentaccording to STEPS model. The movement of node betweenzones is driven by the locality degree parameter (α) of STEPSmodel. We vary α between 0 and 8 to obtain a large spectrumof mobility patterns. When α = 0 nodes are highly nomadic,moving from a zone to one another in a random manner thatmakes the network highly dynamic. On the contrary, whenα = 8, nodes are highly localized (i.e.sedentary) and thereforethere are less information exchange between distant zones.

The PSO algorithm is implemented in every mobile nodesso that each node contains 1 particle. The position of particleis randomly initialized, taking values in range [xmax,−xmax].Particle’s position is updated at each contact with another nodeaccording to the formula 2.

As goal function, we used the Sphere function from theDe-Jong test suite. This suite consists of goal functions withdifferent difficulties to measure the performances of optimiz-ers. The Sphere function is the first and easiest function of thesuite. It is symmetric, unimodal and is often used to measurethe general efficiency of optimizers. That is

f(~x) =

D∑i=1

xi2

where D is the number of elements of ~x. The Sphere functionhas a global optimum f(~x) = 0 at ~x = (0, 0, 0, . . . , 0).

We used root-mean-square error to measure the goodness ofthe solution. The algorithm stops when the error is smaller thana predefined threshold. The simulation settings are summarizedin Table I

Figure 3 shows the optimization convergence delay accord-ing to nods’ locality degree. These results is are averagedover 10 simulation runs. On the figure, we can see that themore mobile nodes are, the smaller convergence delay is. Thisresult shows that nodes mobility can increase dramatically theprocessing capacity of mobile cloud networks.

IV. IMPACT OF NETWORK STRUCTURE ON MOBILECLOUD COMPUTING

In this section, we evaluate the processing capacity ofmobile cloud computing under various dynamic networkstructures. With the same approach as introduced in SectionIII, we measure the convergence delay of a PSO algorithmimplemented on a mobile cloud network and show that thisdelay can be significantly minimized if the network has adynamic small-world structure.

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Fig. 3. Impact of mobility on the convergence delay of PSO algorithm

The small-world phenomenon introduced by Watts and Stro-gatz [6] refers to static graphs with high clustering coefficientand low shortest path length. Through a process which consistin rewiring randomly edges of a graph, by varying the rewiringratio, the authors showed that for an interval a rewiring rationthe resulting static graph, exhibit a small world structure whichcumulate short path observed in random graphs with highclustering coefficient intrinsic to regular lattices. In a previouswork [3], we have shown that this small world behavior canbe observed in dynamic graphs too. We have shown that ina dynamic networks, the analog of the rewiring process instatic graph is done from varying the ratio and intensity ofnomadic nodes. Moreover, we have shown that the STEPSmodel is capable of exhibiting this small-world phenomenonin dynamic networks.

Indeed, starting from the same network configuration asin Section III, we divide mobile nodes in 2 categories. Thefirst consists in highly localized nodes which stay almost alltheir time in their preferential zones. The second categoryconsists in highly nomadic modes which move constantly fromzone to zone. At the beginning of simulation, the nodes aredistributed over the network area so that nodes in differentpreferential zones cannot communicate each other (Figure 4shows the node spatial distribution). We vary the fraction ofmobile node pm from 0 to 1. When pm equals to 0, thenetwork consists in disconnected islands with only intra-zonecommunications that entails a regular structure similar to theone in static graphs. On the contrary, when pm equals to1, the inter-zones movement of highly mobile nodes makesthat the network topology changes constantly which entails arandom network structure. Figure 5 shows the evolution of theclustering coefficient and shortest path length according to thefraction of highly mobile nodes.

We processed the PSO algorithm over all these networkstructures and then measured the resulting convergence delay.Figure 6 shows the results averaged over 10 simulations. Thesesimulation results show that the convergence delay of PSOdecreases rapidly down to an asymptotic part started when the

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Fig. 6. Mobile cloud computing in small-world networks

network exhibits a small world structure. This original result issignificant because the small-world structure, which as shownin this paper improves distributed processing, was shown toemerge naturally in the great majority real dynamic networks[5].

V. RESILIENCE OF MOBILE CLOUD COMPUTING SERVICE

Nowadays, mobile devices still have limited energy capacityand communication as well processing are two importantsources of energy waste. Therefore nodes’ churn is intrinsicto dynamic netwoks clouds. Nodes running out of batterycannot contribute to distributed processing anymore and inconsequence, mobile cloud networks may suffer impredictiblenodes failures. Besides, mobile cloud networks may be thetarget of attacks, for instance DDOS, which can potentialymake unavailable parts of the network. In this section, weevaluate, under various mobility contexts, the resilience ofdistributed services deployed on such networks.

First, we assume that the evolution of number of inactivenodes (i.e. attacked or out of battery) follows a Poissonprocess. Therefore, the number of inactive nodes during a timeinterval τ is distributed according to a Poisson distribution

P [(N(t+ τ)−N(t)) = k] =exp−λτ(λτ)k

k!

where k = 0, 1, 2, . . . and λ is the arrival rate of inactivenodes.

With the same simulation settings as introduced in Sec-tion IV, we perform simulations with various values of λ(1/15, 1/12.5, 1/10, 1/5) and under various mobility contexts(i.e. by varying the fraction of mobile nodes). In these simula-tions, we stop the PSO algorithm when 95 % nodes reach theoptimum. If this threshold is not reached before all the nodesbecome inactive, as there is no recovery possible in this case,the service will never be delivered and hence we assign thesimulation duration time to the convergence delay.

Figure 7 shows the results averaged over 20 simulations.These simulations show that with dynamic small-world net-works (Figure 7(b) and 7(c)), the distributed service resistsmuch better to departed nodes compared highly localizednetwork (Figure 7(a)) and offers approximately the sameresilience level than random networks (Figure 7(d)). Theseresults suggest that a small-world structure not only contributeto enhancing distributed performances but also offers goodresilience properties.

VI. CONCLUSION

In this paper we not only show that nodes’ mobility enhancethe processing capacity of dynamic network cloud but wealso showed how mobility impacts the performance and theresilience of these mobile clouds. In particular, we have shownthat significant performance improvement can be obtainedwhen dynamic networks exhibit a small-world structure andmoreover, this particular structure can improve the resilienceof the network against inactive nodes. This means that byintroducing even a small percentage of highly mobile nodes

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Fig. 7. Resilience of mobile cloud network distributed services under variousmobility contexts

in a high localized network, we can improve significantly theprocessing capacity and resilience of mobile cloud computing.These results open the way to adaptive strategies that wouldaim to adapt dynamic network topology and behavior accord-ing to their processing load and constraints. Moreover thesestrategies have to consider also storage and energy consump-tion which are critical in the context of handheld systems. Ourcurrent work investigates these research directions.

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