mobile edge clouds for information-centric iot services · than host-centric. cloud computing and...

Mobile Edge Clouds forInformation-centric IoT Services

Eleonora Borgia, Raffaele Bruno, Marco Conti, Davide Mascitti, Andrea PassarellaInstitute of Informatics and Telematics (IIT) - CNR, Pisa, Italy{e.borgia, r.bruno, m.conti, d.mascitti, a.passarella}@iit.cnr.it

Abstract—The number and capabilities of IoT devices willexponentially grow over the next years. Together with thepervasive diffusion of smart personal mobile devices this opens upunprecedented opportunities for contextualised services providedto mobile users, based on their current interests and behaviours.In addition, most of these services will be content-centric ratherthan host-centric. Cloud computing and Information-CentricNetworking (ICN) are therefore two key technologies in thisperspective. In both cases, solutions are typically designed forglobal Internet platforms, while mobile nodes are seen as edgedevices from which data are fetched and sent back throughpervasive wireless networks (typically, LTE). However, it isquestionable whether such an approach will work as expected,e.g., due to data privacy concerns and expected bandwidthshortage of even last-generation cellular networks. In this paperwe present a general framework where global cloud and ICNplatforms are complemented in a totally synergic way by localclouds formed at the edge of the network by mobile devices,where service provisioning and data management functionalitiesare offloaded whenever possible (and appropriate). This resultsin a multi-layer, content- and service-centric approach to IoTdata management and service provisioning. We then presentperformance evaluation results from applying this frameworkto a specific case where data-centric services are jointly providedby edge devices and by a global cloud platform. Results showthat this approach is very promising, as it is able to drasticallycut the related cellular-network traffic, and, at the same time,improve the effectiveness of service provisioning to users.

I. INTRODUCTION AND MOTIVATION

There is a clear trend showing that the number of wirelessconnected devices is growing steadily, with IoT devices andusers’ personal devices representing the biggest share. Forexample, the latest CISCO VNI report [1] foresees that by2020 the total number of wireless connected devices willincrease beyond 10 billions worldwide, with smartphones andIoT devices collectively accounting for 66% of this amount.Interestingly, IoT devices show the largest percentage increaseacross all devices types, from 8% in 2015 up to 26% by 2020.

This results in an emerging scenario recently called im-mersed human [2], [3], which is represented in Figure 1. Inthis view, there is a tight and continuous interaction betweenthe human users, their personal devices, and the “things”embedded in the physical environment (sensory swarm). Theseinteractions are primarily information-centric, with IoT andpersonal devices constantly generating data that are transferredbetween sources (things and users’ devices), places where theycan be stored and elaborated, and sent back to the users. This isexpected to generate a huge increase in mobile traffic demand,

which is forecasted to grow at a compound annual growth rate(CAGR) of 53% between 2015 and 2020 [1]. The main reasonfor this increase, besides the sheer number of mobile devices,will be the development of mobile applications that will bemore and more data intensive, also in the IoT domain (e.g.,IoT multimedia applications [1]).

Fig. 1. The immersed human scenario

To fully exploit the potential of the data available in suchan environment, cloud computing and Information-CentricNetworking (ICN) are currently considered cornerstone tech-nologies. Cloud computing provides a scalable approach tostore data generated by things and users devices, and, mostimportantly, to extract information and knowledge from rawdata, providing value-added services to the users. On theother hand, ICN [4] is the most promising approach to turnthe Internet into an information-centric network that nativelysupports access to data, irrespective of where they are storedor generated, also overcoming possible disconnections of dataproviders.

In order to use cloud and ICN technologies to support theimmersed human vision, the fundamental assumption is thatdata generated by IoT and users’ devices (the outer layersof the scheme in Figure 1) can be transferred to cloud andICN platforms deployed in the global Internet, and that resultsof data elaboration can be easily transferred back to users.In fact, IoT and users’ devices are seen, from the cloudand ICN perspective, as edge devices, that generate data andreceive services, but are not included in the data managementand service provisioning processes. Most mainstream IoTarchitectures (including the reference ETSI architecture1) arebased on this assumption [2].

Unfortunately, this assumption might frequently not beverified, for several reasons. Firstly, cellular networks, and

1ETSI TS 102 690 v2.1.1 (2013-10). Available at:http://tinyurl.com/hemc3po

2016 IEEE Symposium on Computers and Communication (ISCC)

978-1-5090-0679-3/16/$31.00 ©2016 IEEE

primarily 4G/LTE, which are foreseen to be the referencenetwork to move data from where they are generated to cloudand ICN platforms, should have enough capacity to support theexpected data traffic, but it might not be the case. In front of anexpected exponential increase of traffic demand, the cellularcapacity is foreseen to increase only by a factor slightly higherthan 3 between 2015 and 2020, and only by a factor of 1.4 for4G technologies [1]. For example, each IoT device is foreseento generate a worth of traffic of about 2Kbps by 2020 [1]. Evenassuming a quite optimistic LTE upload capacity of 100Mbpsin a cell of 1 km2, one IoT device every 20 m2 will besufficient to saturate the LTE capacity (without counting therest of the mobile traffic generated by users’ applications).This means that in the medium term we might experiencebandwidth crunch problems, similar to those occurred for 3Gtechnologies around 2010 [5], thus questioning the possibilityto effectively transfer data back and forth between IoT devices,users’ personal devices, and cloud and ICN infrastructures.

Secondly, data privacy concerns might be a showstopper.For example, while the users would be interested in extractingknowledge from local data they generate, they might not bewilling to share these data with global cloud/ICN serviceproviders. Similar constrains may arise for the companies inthe Industry 4.0 scenario2. For example, confidentiality re-quirements of individual companies’ data might be in contrastwith a push towards centrally managed ICN/Cloud platforms.

Finally, moving all raw data to some global cloud or ICNinfrastructure might even not be needed. Often, data (and theservices built on them) will be of local value, and relevant onlyto the users close to where they are generated [6]. Aggregateinformation might be good enough, and even preferred, forusers distant from where data is generated.

These considerations are the basis for the framework wepropose in this paper (Section III). Specifically, we arguethat a much more effective solution with respect to state ofthe art in both ICN and cloud-based service provisioning(Section II) should be multi-level, and include mobile devices(including users’ personal devices) into both the ICN andservice provisioning processes. The approach we propose goesinto the direction of pushing intelligence towards the edge ofthe network, which, in the cloud computing area, is the mainargument for the Cloudlet architectures [7], the fog computingparadigm [8], and proposals integrating cloud functions intocellular base stations (i.e., LTE eNodeBs) [9]. However, allthese solutions require additional infrastructure elements (e.g.,gateways and femto-cell eNodeBs), and therefore need addi-tional infrastructure investments, planning and maintenance.Our proposal goes one step further by pushing intelligencedirectly to mobile devices, where we allocate both ICN andcloud computing functionalities. This is enabled by the op-portunistic networking and computing paradigms [10], [11],whereby mobile nodes exploit physical proximity to transferdata and provide services directly to each other. Through this

2Forshung Union & Acatech. Recommendations for imple-menting the strategic initiative INDUSTRIE 4.0, 2013. Available:http://tinyurl.com/jsbxlkz

approach, mobile nodes form mobile clouds at the edge ofthe network (also implementing standard ICN functionalities,e.g., for data forwarding and caching), without requiring anyadditional infrastructure. Data is collaboratively stored andelaborated inside the mobile clouds, and transferred to globalICN/cloud platforms only when useful. Local mobile clouds,therefore, support provisioning of contextualised, information-centric services of local relevance to the users, without bur-dening the cellular network with unnecessary traffic.

The proposed framework is a general paradigm for data-centric service provisioning in mobile environments, and canbe instantiated in several specific architectures and protocols.Moreover, it is complementary, and not a replacement, of theexisting ICN and cloud architectures. To exemplify both prop-erties, in this paper we consider an instance of the proposedframework, described in detail in [12] and summarised inSection III-C. We consider a scenario where services basedon local data generated by users’ devices can be executedeither on a global cloud platform, or locally on an edge cloudformed by mobile users’ devices. Our solution dynamicallydecides whether service requests should be provided by theglobal cloud platform or by the local mobile cloud. We showthat such a multi-level solution takes the advantages of bothglobal and local service provisioning, by avoiding saturationof the wireless cellular network and improving the quality ofservice provisioning to end users. Specifically, in Section IVwe present a sensitiveness analysis of this solution to showhow it dynamically splits service provisioning between theglobal and the local clouds, by varying the size of the dataon which services are generated, the popularity of servicerequested, the share of local devices providing the requiredservices, and the background traffic on the LTE network usedto transfer data between local devices and the global cloud.

II. RELATED WORK

Work related to this paper falls primarily in the areas ofservice provisioning for IoT environments, and data-centricmobile networks.

With respect to the first area, the mainstream approach isto use centralised cloud platforms to enable easy developmentof vertical applications [2]. An example is the standard ETSIM2M architecture. However, to overcome the drawbacks ofthis approach, moving intelligence (i.e., service provisioning,in our case) closer to the edge of the network where dataare generated, is considered one of the alternative approaches.Fog computing [8] and Cloudlet [7] architectures go in thisdirection. Services are provided from gateways at the boundarybetween the access network and the Internet, but users’ andIoT wireless devices are only data producers and final users ofapplications services. Mobile edge computing goes one stepbeyond, by offloading service provisioning also on mobiledevices. This is complementary to mobile cloud solutions [9]that, instead, offload computation and storage from mobiledevices to the cloud, or to intermediate nodes such as gate-ways. The latter is motivated by the fact that for some tasksmobile devices might not have enough computing resources,


which are instead plentiful in cloud platforms. Coordinationof local mobile devices forming a mobile cloud has beenrecently proposed in FemtoCloud [13], where the controller(residing in a gateway) is entirely responsible to scheduleparallel tasks execution on mobile devices. In contrast, ourproposed framework relies on a hybrid service provisioningthat exploits the presence of a pervasive cellular infrastructureas a control channel and of a global cloud platform whenappropriate, as well as the use of local resources through op-portunistic computing, which can be considered as an extremeversion of mobile edge computing. Finally, the specific serviceprovisioning algorithm used in this paper was presented indetail in [12]. Here is it used as a specific instance of amuch more general framework that we originally present inthis paper.

As far as data-centric mobile networks, apart from conven-tional ICN architectures - deeply discussed in Section III-B,the most closely related approaches are data-centric wire-less sensor networks [14] and data-centric opportunistic net-works [15]. We differentiate from work on data centric wire-less sensor networks as we include in the picture data producedby mobile users’ devices, and we want to tolerate disconnec-tions due to mobility between nodes of the network. Withrespect to data-centric solutions in opportunistic networks,they partly fit our scenario, as they also support direct datatransfer between nodes during physical proximity. However,none of them is compliant with standard ICN architectures,which is one of the key features of our proposed framework. Inaddition, they are not designed to operate with a heterogeneousarchitecture where also global data management platforms(such as a conventional ICN infrastructure) are used.

III. MOBILE CLOUDS FOR DATA-CENTRIC IOT SERVICES

The framework proposed in the paper is described in Fig-ure 2. Conceptually it is made up of three levels. At the bottomlevel (raw data generation), individual devices generate rawdata. These can be either IoT devices (e.g., sensors) or users’personal devices (e.g., smartphones). Conventional protocolsfor moving data across such nodes, such as standard wirelesssensor network protocols, can be used at this level. The toplevel (global platforms) is composed by global ICN and cloudplatforms deployed over the Internet.

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Fig. 2. Local and global data management and service provisioning.

The core innovative aspect of our framework is to in-clude an intermediate level (mobile clouds), composed oflocal mobile clouds formed by devices available in specificphysical areas. Devices at this level are heterogeneous, andcan be users’ mobile devices or static nodes with sufficientstorage, computing and networking capabilities (e.g., fixedcameras integrating WiFi and computing capabilities). Notethat devices at this layer can also be generators of raw data,thus logically belonging also to the bottom level. Devices atthe mobile-clouds level communicate through direct contactsenabled by ad hoc enabling technologies (such as WiFi orBluetooth in ad hoc mode), according to the opportunisticnetworking paradigm (briefly explained in Section III-A). Inaddition, exploiting opportunistic computing techniques (see,again, Section III-A), during direct contacts they provideservices to each other, based on the data available on eachother.

The mobile-clouds level interacts with the global platformslevel in multiple ways. First of all, the two levels interactto decide which data, available in a mobile cloud, shouldalso be transferred to the global platforms. This might bethe case of aggregate data, which for example would providea summarised representation of the status of the physicalenvironment in the area of the mobile cloud, or of particularlyimportant data, which are of interest globally (e.g., across mul-tiple mobile clouds in different physical locations). Similarly,the two levels interact to decide if services available locallyin the mobile cloud are sufficient to satisfy the requirementsof the users’ applications, or if services should be servedby the global cloud platform. The latter might be the case,for example, for services requiring data that are not availablelocally, or services that are more efficiently provided from theglobal cloud.

From a networking standpoint, we assume that mobiledevices and “things” both in the raw data generation andin the mobile cloud layers can communicate with globalICN and cloud platforms through a pervasive LTE cellularnetwork. Given that, a significant part of the data might stayinside the mobile clouds, our framework drastically cuts thetraffic over the cellular network, thus contributing to avoid thebandwidth crunch issues discussed in Section I. Moreover, theavailability of an LTE network allows for the implementationof lightweight control protocols for data management andservice provisioning in mobile clouds to overcome key limitsof pure opportunistic networking and computing.

Given this overall conceptual architecture, the main chal-lenges to be addressed are as follows. From the standpoint ofservice provisioning, we need to integrate service provisioningfrom the global and mobile clouds. This means designing so-lutions running on mobile nodes that understand dynamicallyif a given service has to be provided from the global cloudor from mobile devices in the vicinity. Moreover, this alsorequires to develop solutions such that mobile devices canprovide services to each other in a networking environmentcharacterised by mobility, disconnections, and network parti-tioning. From the standpoint of data management, we need

to design solutions whereby global ICN architectures can beextended in the mobile clouds and raw data generation layers.This also requires significant advancements, since the standardICN architectures are designed having in mind a fixed Internetnetworking environment in absence of mobility and wherebandwidth is plentiful. In both cases, we need techniques tocoordinate global and local operations, thus realising a hybriddata-centric service provisioning environment.

In the rest of the section we we briefly recall the mainfeatures of the key building blocks required to address thesechallenges, i.e., opportunistic networking and computing (Sec-tion III-A), ICN (Section III-B) and service provisioningapproaches in mobile clouds (Section III-C).

A. Opportunistic networking and computing

Opportunistic networks [11] are an evolution of self-organising networks, where the existence of a continuousmulti-hop path is not necessary for end-to-end data transfer.During direct contacts between each other, nodes evaluatewhether local messages should be transferred to the en-countered peer, so that data progresses towards the intendeddestination(s). Using this approach, opportunistic networksexploit mobility as a feature to move data across nodes, and donot require to maintain costly routing structures, which maychange too dynamically due to nodes mobility.

Opportunistic computing [10], [16], exploits the same con-cept, but supports general service provisioning across mobilenodes. During contacts, nodes not only transfer data betweeneach other, but also provide services to each other, accordingto the needs of the encountered peers. In opportunistic comput-ing, resources available at local nodes are abstracted as servicecomponents, and composed dynamically to offer to individualnodes additional functionalities with respect to what they havelocally.

The typical drawback of opportunistic solutions is thatnodes can reconstruct information about the other nodes inthe network only through direct contacts, which means thatthey may have a quite imprecise view of the status of thenetwork. Recent approaches in the opportunistic networkingdomain [17] overcome this limitation by jointly exploitingopportunistic networks and LTE infrastructures. For example,in [17] lightweight control agents are deployed in the Internet,which collect from nodes information about the status of a datadissemination process occurring in the opportunistic network.Agents thus acquire a global view about the status of thedissemination process, and complement purely opportunisticnetworking mechanisms by sending directly (through LTE)to mobile nodes data that would otherwise miss a deliverydeadline. To the best of our knowledge, such a lightweightcoordination approach has not yet been proposed for oppor-tunistic computing.

B. Information-centric mobile clouds

Information-centric approaches have been designed to sup-port the exponential increase of content generated in the Inter-net and the high demand of its distribution in a more efficient

and scalable way. In contrast with host-centric networks, ininformation-centric networks, the main objective is not todeliver packets to specific “locations” (i.e., hosts/destinationaddresses), but to retrieve content and to access services andapplications by name.

A number of different information-centric architectures havebeen proposed in the past years (e.g., Data Oriented NetworkArchitecture (DONA), Content Centric Networking (CCN),SAIL - see [4] for a survey). Among them, CCN and its suc-cessor Named Data Networking (NDN) [18] are the most pop-ular and fully-fledged information-centric architectures. NDNnodes maintain three main data structures: (i) the Content Store(CS), which acts as a data cache, (ii) the Pending InterestTable (PIT), which stores interests (i.e., data requests) thathave arrived at the node, and have not yet been satisfied, and(iii) the Forwarding Information Base (FIB), which tells howto route interest packets towards the required data (analogousto an IP forwarding table). When a node generates an Interestpacket, this is forwarded from node to node according to thecontent of the FIBs, until it finds the required content in a CS(or at the source). Interest packets install a reverse route whilebeing forwarded, which is used to send the data back to therequesting node.

As discussed in Section I, the ICN idea is very suitablefor IoT environments, as in many IoT applications the mainemphasis is on data access, irrespective of the location where acertain data is stored. Therefore, in our framework we considerto include ICN functions also at the level of mobile clouds,so that they can natively interoperate with ICN architecturesin the global platforms level. However, implementing ICNfunctions in mobile clouds is a challenge. Indeed, the referenceICN architectures have been conceived for fixed networks andassume that information stored in the data structures, and inparticular in FIB, is subject to rare changes because the wirednetwork is quite stable. Furthermore, mobility is considereda rare event, and mobile devices are handled as simple edgedevices that either generate or consume data, but are not partof the ICN infrastructure.

On the other hand, in the framework we propose, mobilenodes act as content routers, and adopt ICN-based forwardingand caching strategies in order to manage data in the localcloud. The advantage is that content can be managed and keptcloser to where it is most likely of interest, but modificationsto the standard ICN algorithms are needed to cope with thetotally different networking environment. While opportunisticnetworking can provide algorithms to implement data-centricfunctions in mobile clouds [15], an open challenge is howto make such algorithms compatible with the standard ICNprotocol mechanisms. A first attempt in this direction is arecent proposal for an IETF draft on the adoption of ICNfor IoT applications [19].

C. Service provisioning in mobile clouds

Service provisioning in mobile clouds is mostly built onthe opportunistic computing concept. This is conceptuallyclose to standard service-oriented architectures, insofar ser-


vices (abstractions of resources) are dynamically composedbased on application needs. However, in a mobile cloud,communications occur via opportunistic contacts, and there-fore the mechanisms to implement service provisioning andcomposition need to be completely re-designed.

One key challenge is how to decide whether and how tocompose services available on different nodes, upon an appli-cation request at a given node (usually named seeker). Thisproblem has been recently explored, among others, in [20],[21]. Typically, nodes need to build a local view of the servicesavailable at other nodes in the mobile cloud. This is typicallyorganised in a service composition graph, where vertices arenodes, and edges represent the cost of obtaining a certainservice from that node. This information is collected eitherthrough direct contacts, or through advertisements of availableservices that are epidemically diffused across the mobile cloud.Cost of obtaining a service typically include both an estimateof the time needed to send a service request (and the neededinput data) to the service provider, and the expected serviceexecution time based on the load of the providers. Resultsin [20], [21] show that it is possible to efficiently maintainsuch knowledge with limited overhead, and provide servicesdirectly between nodes.

Another challenge is how to effectively spread ser-vice execution requests, in presence of multiple possibleproviders [10]. Intuitively, the more service executions arecreated in parallel, the better, because the seeker will receivethe service from the provider that completes the serviceprovisioning process first. On the other hand, too aggressivereplications would result in saturation of the providers’ re-sources, thus increasing the service provisioning time. Resultsin [10] show that an optimal operating point exists, and thiscan be computed in a distributed way by each node in the mo-bile cloud. Other challenges still requiring valuable solutionsinclude, for example, effective service advertisement, optimal“duty cycling” of service provisioning, optimal execution forcomposed services.

One of the main challenges specific to the frameworkpresented in Figure 2 is how to integrate local and globalservice provisioning. Similarly to what presented in [17] fordata dissemination, it is possible to deploy, at the interfacebetween the mobile and the global cloud, an agent that collectsinformation about the status of service provisioning in themobile cloud, and assists mobile nodes in deciding whereto execute services. Specifically, through this approach theservice composition graph can be built more efficiently, as thisagent would exploit more precise information about the statusof nodes in the mobile cloud. In addition, it is also possibleto decide if a certain service should be provided by nodesin the mobile cloud or if the global cloud platform shouldbe preferred. This overcomes another short-coming of pureopportunistic computing, i.e., the need for a minimal densityof nodes for enabling service provisioning. In hybrid solutions,like those we consider in our framework, at low density in themobile cloud, services would be provided automatically fromthe global cloud platform.

An initial solution along those lines has been presentedin [12]. In the following section, we focus on the practicalapplicability of our proposed general framework for whatconcerns data-centric service provisioning in an integratedglobal cloud and mobile edge computing, and we show asensitivity analysis with respect to key parameters.

IV. PERFORMANCE RESULTS

In the system we have designed to realise data-centricservice provisioning in an integrated global cloud and mobileedge computing environment (see details in [12]), any mobiledevice requiring a service decides whether it should be ob-tained from the local or the global cloud (assuming servicesare available on both). This decision is based on estimates ofthe time required to obtain the service results in either case. Weassume the presence of an agent at the interface between themobile and the local cloud (e.g., running on the LTE eNodeBcovering the local cloud), which assists nodes in taking thisdecision. Estimates of service execution times are computedbased on the contact patterns between mobile devices, thestate of the service execution queue of the providers, theavailable LTE bandwidth for communicating with the globalcloud platform, and the capacity of direct transfers duringopportunistic contacts with the other nodes

We tested the hybrid service provisioning solution usingTheOne simulator3. Hereafter, we present a sensitiveness anal-ysis. We tested 30 mobile nodes moving in a 500mx500marea following the Random WayPoint mobility model. Duringthe simulated time (400000s per run), the interval betweenthe generation of two requests is uniformly distributed in theinterval (i.e., [10s,15s] - higher load, [20s,30s] - lighter load).The request is associated with a node (seeker) chosen withuniform probability. The requested service is picked among aset of 15 possible services either with uniform probability, orwith a Zipf probability with parameters α equal to 1 or 3, totest the system under more or less skewed preferences towardsspecific services (the higher α, the more preference is skewedtowards a small set of very popular services). We vary thepercentage of providers for each service between 10%, 25%and 50% of nodes, to test the performance with higher orlower “service capacity” of the mobile cloud. At each point intime, an additional number of (background) nodes is supposedto be actively generating data traffic over the LTE network.In different experiments, this number ranges in the intervals[0,20], [0,40], [0,80], [0,160], respectively, corresponding tofewer or more background nodes possibly competing for theLTE capacity. The number of competing nodes is generatedaccording to a standard birth/death process. The total LTEcapacity (300Mbps for download and 75Mbps for upload) isshared equally among all active nodes (the nodes request-ing/providing services and the additional, background nodes).Finally, service execution time when the global cloud is usedis assumed to be 5s long, half the average mobile serviceexecution time. Simulations are replicated 5 times, keepingexactly the same evolution of the background active nodesprocess across replicas. This guarantees that the congestion

3https://akeranen.github.io/the-one/


TABLE ISERVICE PROVISIONING PARAMETERS

Service parameters size 40, 80, 160 MBMax. background nodes 20, 40, 80, 160Request generation interval [10s,15s], [20s,30s]Percentage of providers 10%, 25%, 50%Popularity of services uniform, Zipf α = 1, Zipf α = 3

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on the LTE network due to the additional nodes is the samewhen we vary the other simulation parameters. The tests arerepeated changing the amount of data that has to be transferredas input and output of the services, from 40MB to 80MB and160MB. All the results shown are the average results of 5independent simulation runs, with 95% confidence intervals.

Preliminary results in [12] indicate already that hybridservice provisioning outperforms using global cloud only,in particular when background traffic or the size of serviceparameters increase. Specifically, we have found that the“global” service provisioning time is between 1.2 and 100times higher than “hybrid” service provisioning time for therange of input/output service parameters sizes considered[12]. Hereafter, we better analyse the behaviour of hybridservice provisioning with respect to the described simulationparameters. Table I recaps the parameters values used in thesimulations (bold indicates default values).

Figure 3 shows the variation over time of the fraction ofrequests served by the local mobile cloud during specificsimulation runs with varying service input/output parameterssizes. We plot it together with the fraction of additionalbackground devices in case the maximum number is 40. Ingeneral, the fraction of requests served by the local cloudfollows quite closely the background traffic. In addition, thereis a sort of shift due to the amount of data required for serviceexecution. Specifically, in the 40MB scenario (blue line) thereare time intervals where all requests are assigned to the globalcloud (where the curve drops to 0% in the graph). Instead inthe 160MB scenario (red line) the Hybrid approach alwaysoffloads some requests to the mobile cloud even with lownumbers of background devices.

In Figures 4(a) and 4(b) we analyse the sensitivity withrespect to the network load. Specifically, points in the xaxis are in the form XXM YY-ZZ where XX represents thesize of input/output service parameters, while YY and ZZ arethe extremes of the time interval between service requestsgenerations. Therefore, moving from left to right on the x axiswe constantly reduce the network load. Finally, each curvein the plots corresponds to a different maximum number of

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Fig. 4. (a) Service provisioning times and (b) % of requests served by themobile cloud for different network loads

background nodes competing for LTE bandwidth. Figure 4(a)shows the average service provisioning time, while Figure 4(b)shows the percentage of requests served by the mobile cloud.

It is interesting to note that service provisioning timeincreases gradually as the traffic load increases. Specifically,in Figure 4(a) we do not have signs of saturation such asan exponential increase of service provisioning time. FromFigure 4(b), the main reason is the fact that the percentage ofrequests served by the mobile cloud increases as the cellularnetwork becomes more and more congested. Interestingly,from Figure 4(a) we see that service provisioning time doesnot vary much for a varying number of background nodes,while (Figure 4(b)) the fraction of service requests served inthe local cloud does increase quite significantly. Therefore, asthe LTE network becomes more and more congested, moreservices are provided locally, and this limits the effect ofhigher congestion on the cellular network. Finally, note that incase of 20 maximum background nodes and high load, serviceprovisioning time is the highest (with respect to cases withgreater numbers of background nodes), which is unexpected.From more detailed analysis of the various components ofthe delay, we found that this is due to the higher percentageof requests served by the global cloud in this case. Thiscreates, at seekers, longer queues of messages to be sentto the eNodeB, and, in turn, this generates significant delayfor seekers to get from the eNodeB the initial estimate ofthe service provisioning time using the global cloud. Witha higher background load, fewer requests are selected to beserved using the global cloud, which results in fewer messagesqueued for transmission towards the eNodeB. Paradoxically,seekers receive the estimates for global service provisioningtimes quicker.

Figures 5(a), 5(b) and 5(c) show the service provisioningtime for varying skewness of service popularity and totalservice capacity in the mobile cloud. Again, it is confirmedthat hybrid service provisioning provides graceful degradationof performance under significant loads. Specifically, signs ofsaturation appear only for a very unfortunate combination ofparameters, i.e., when (i) service capacity is very low (10%of nodes are providers); (ii) network load is very high; and(iii) service requests are very skewed towards specific services(Zipf popularity with α = 3).

V. CONCLUSIONS

In this paper we have presented a general framework toexploit mobile clouds built atvalue the edge of the networkby local nodes to support information-centric IoT services.


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(c)

Fig. 5. Service provisioning time - varying popularities - (a) 10% providers, (b) 25% providers, and (c) 50% providers.

The proposed approach is motivated from several standpoints,from the trends of growth of cellular capacity against datatraffic demands, to privacy and confidentiality requirements. Inthe proposed framework, local nodes forming mobile cloudsmanage most of the data generated locally, and provideservices and data management functionalities to each otherbased on these data. These local service provisioning and data-management functions are integrated with global cloud andICN platforms. We have tested the performance of an instanceof our framework, focusing specifically on hybrid data-centricservice provisioning on both global and local mobile clouds.In general such a solution outperforms one where services areonly provided by a global cloud. In addition, our solution isable to tolerate high variability in network traffic load, servicepopularity and availability of services in the mobile cloud,without showing signs of saturation even in very congestedscenarios, generated over different ranges of parameters.

REFERENCES

[1] CISCO, “Cisco visual networking index: Global mobile data trafficforecast update 2015–2020 white paper,” CISCO, Tech. Rep., 2016.

[2] E. Borgia, “The internet of things vision: Key features, applications andopen issues,” Comp. Comm., vol. 54, 2014.

[3] A. Sangiovanni-Vincentelli, “Let’s get physical: adding physical dimen-sions to cyber systems,” Rome, Italy, July 2014, internet of EverythingSummit.

[4] G. Xylomenos, C. Ververidis, V. Siris, N. Fotiou, C. Tsilopoulos,X. Vasilakos, K. Katsaros, and G. Polyzos, “A survey of information-centric networking research,” Communications Surveys Tutorials, IEEE,vol. 16, no. 2, 2014.

[5] Ars Technica, “iPhone overload: Dutch T-Mobile issues refund after 3Gissues,” July 2010.

[6] P. G. Lopez, A. Montresor, D. Epema, A. Datta, T. Higashino,A. Iamnitchi, M. Barcellos, P. Felber, and E. Riviere, “Edge-centriccomputing: Vision and challenges,” ACM Sigcomm Computer Commu-nication Review, 2015.

[7] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The case forvm-based cloudlets in mobile computing,” IEEE Perv. Com., vol. 8,no. 4, 2009.

[8] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and itsrole in the internet of things,” in ACM MCC, 2012.

[9] O. Munoz-Medina, A. Pascual-Iserte, and J. Vidal, “Optimization ofradio and computational resources for energy efficiency in latency-constrained application offloading,” IEEE Tran. Veh. Tech., 2014.

[10] A. Passarella, M. Kumar, M. Conti, and E. Borgia, “Minimum-delayservice provisioning in opportunistic networks,” IEEE TPDS, vol. 22,no. 8, 2011.

[11] L. Pelusi, A. Passarella, and M. Conti, “Opportunistic networking: dataforwarding in disconnected mobile ad hoc networks,” IEEE Com. Mag.,vol. 44, no. 11, 2006.

[12] M. Conti, D. Mascitti, and A. Passarella, “Offloading service provision-ing on mobile devices in mobile cloud computing environments,” inProc. Euro-Par, 2015.

[13] K. Habak, M. Ammar, K. A. Harras, and E. Zegura, “FemtoClouds:Leveraging Mobile Devices to Provide Cloud Service at the Edge,” inIEEE 8th Int. Conf. Cloud Comput., 2015.

[14] K. Ahmed and M. A. Gregory, “Techniques and challenges of datacentric storage scheme in wireless sensor network,” Journal of Sensorand Actuator Networks, vol. 1, no. 1, p. 59, 2012.

[15] C. Boldrini and A. Passarella, Data Dissemination in OpportunisticNetworks. John Wiley & Sons, Inc., 2013, pp. 453–490.

[16] C. Shi, M. H. Ammar, E. W. Zegura, and M. Naik, “Computing in cirrusclouds: The challenge of intermittent connectivity,” in ACM MCC, ser.MCC ’12. New York, NY, USA: ACM, 2012, pp. 23–28.

[17] F. Rebecchi, M. D. de Amorim, and V. Conan, “Droid: Adaptingto individual mobility pays off in mobile data offloading,” in IFIPNetworking, 2014.

[18] L. Zhang, A. Afanasyev, J. Burke, V. Jacobson, k. claffy, P. Crowley,C. Papadopoulos, L. Wang, and B. Zhang, “Named data networking,”ACM CCR, vol. 44, no. 3, 2014.

[19] A. Lindgren, F. B. Abdesslem, B. Ahlgren, O. Schelen, and A. Malik,“Applicability and tradeoffs of information-centric networking for effi-cient iot,” IETF, ICN Research Group Internet Draft, Tech. Rep., 2015.

[20] U. Sadiq, M. Kumar, A. Passarella, and M. Conti, “Service compositionin opportunistic networks: A load and mobility aware solution,” IEEEToC, 2014.

[21] D. Mascitti, M. Conti, A. Passarella, and L. Ricci, “Service provisioningthrough opportunistic computing in mobile clouds,” Proc. ComputerScience, 2014.


mobile edge clouds for information-centric iot services · than host-centric. cloud computing and...

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