Research Article Offloading Method for Efficient Use of Local Computational Resources in Mobile Location-Based Services Using Clouds

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1 Hidawi Mobile Iformatio Systems Volume 217, Article ID , 9 pages Research Article Offloadig Method for Efficiet Use of Computatioal Resources i Mobile Locatio-Based Services Usig Clouds Yusik So 1 ad Yagsu Lee 2 1 Departmet of Computer Sciece ad Egieerig, Dogguk Uiversity, 3-26 Pil-dog, Jug-gu, Seoul 1-715, Republic of Korea 2 Departmet of Computer Egieerig, Seokyeog Uiversity, 16-1 Jugeug-dog, Sugbuk-ku, Seoul , Republic of Korea Correspodece should be addressed to Yagsu Lee; yslee@skuiv.ac.kr Received 9 December 216; Revised 6 February 217; Accepted 2 February 217; Published 14 March 217 Academic Editor: Subramaiam Gaesa Copyright 217 Yusik So ad Yagsu Lee. This is a ope access article distributed uder the Creative Commos Attributio Licese, which permits urestricted use, distributio, ad reproductio i ay medium, provided the origial work is properly cited. With the developmet of mobile computig, locatio-based services (LBSs) have bee developed to provide services based o locatio iformatio through commuicatio etworks or the global positioig system. I recet years, LBSs have evolved ito smart LBSs, which provide may services usig oly locatio iformatio. These iclude basic services such as traffic, logistic, ad etertaimet services. However, a smart LBS may require relatively complicated operatios, which may ot be effectively performed by the mobile computig system. To overcome this problem, a computatio offloadig techique ca be used to perform certai tasks o mobile devices i cloud ad fog eviromets. Furthermore, mobile platforms exist that provide smart LBSs. The smart cross-platform is a solutio based o a virtual machie (VM) that eables compatibility of cotet i various mobile ad smart device eviromets. However, owig to the ature of the VM-based executio method, the executio performace is degraded compared to that of the ative executio method. I this paper, we itroduce a computatio offloadig techique that utilizes fog computig to improve the performace of VMs ruig o mobile devices. We applied the proposed method to smart devices with a smart VM (SVM) ad HTML5 SVM to compare their performaces. 1. Itroductio Advacemets i mobile techology ad locatio-based services (LBSs) have helped improve the quality of life of users adhavefosteredmaybusiessopportuities[1].withthe icreasig use of LBSs, their value has likewise icreased, ad this value exteds to the services to which the LBSs promote access. I its early stages, the LBS used oly simple locatio iformatio. It has recetly developed ito a itelliget system that employs multiple types of iformatio, such as the user s locatio, time, persoal iformatio, ad behaviors [2]. It is thus difficult to effectively ad efficietly perform services o a mobile device without cosiderable computig power. To solve this problem, a computatio offloadig techique ca perform certai tasks i a alterative eviromet, such as a cloud or fog, istead of executig i the mobile device. I additio, mobile services have the disadvatage of ruig various platform-depedet applicatios developed i differetlaguages,suchascc++,java,adobjectivec.the smart cross-platform is a program that eables applicatios developed with C/C++, Java, ad Objective C to ru o various mobile devices, smart devices, ad browsers that support HTML5 [3]. Nevertheless, owig to the characteristics of the virtual machie (VM), the performace of the hardware platformorbrowserowhichthevmoperatesisgreatly impacted by this approach, eve if optimizatio is performed at the iterpreter ad code level. Whe ruig VM applicatios o a low-performace hardware platform depedig o the cotet complexity it is difficult to esure the quality of service (QoS) i terms of executio. I this study, we strived to solve the above problems by usig fog computig ad a smart VM (SVM) platform to effectively ad efficietly provide LBSs o mobile ad smart devices. Ulike use of a cetralized cloud, i the proposed approach, a local uit is employed to eable smart LBSs to effectively operate o a variety of platforms. The remaider of this paper is orgaized as follows. I Sectio 2, we examie the features of a existig offloadig scheme, the smart cross-platform, ad the SVM. We aalyze

2 2 Mobile Iformatio Systems SIL code (.sef) SEF loader SVM Rutime eviromets SVM library Iterpreter Native iterface Exceptio hadler Memory maagemet Thread scheduler Debuggig iterface Profilig iterface Result Native platform Figure 1: System cofiguratio of the smart virtual machie. the limitatios of these respective techiques. I Sectio 3, we describe the proposed offloadig scheme for SVMs. I Sectio4,theperformaceoftheproposedmethodisverified through experimets. Fially, Sectio 5 cocludes the paper. 2. Related Works 2.1. Smart Virtual Machie Model. The SVM is a stackbased VM solutio that is loaded o smart devices. It eables dyamic applicatio programs to be dowloaded ad ru idepedetly of the platform. The SVM is desiged to employ the Smart Itermediate Laguage (SIL), which ca accommodate both procedural ad object-orieted laguages. It thus ca accommodate multiple laguages, such as C/C++ ad Java, as well as the Objective C laguage used i ios. These laguages are ow widely used by developers [3, 4]. The SVM system cosists of three parts: a compiler that compiles applicatio programs to create a Smart Assembly Format (SAF) file from SIL code, a assembler that coverts thesaffileitoasmartexecutableformat(sef)file,ada VM that receives the SEF file ad rus the program. The SVM cofiguratioisshowifigure HTML5 Smart Virtual Machie. HTML5 SVM is a VMbased solutio that provides a itegrated eviromet i developmet ad executio phases by supportig both a webbased executio eviromet ad multiple programmig laguages [4]. Because separate developmet ad executio eviromets exist i smart devices, separate developmet work must be performed based o the target device ad respective platform to provide a specific type of cotet to multiple smart device types. HTML5 SVM ca provide various cotets o heterogeeous target devices with web browsers. However, it requires adequate device performace (e.g., may frames per secod) to eable the smooth productio of results. Thus, the performace ad QoS of the give cotets deped o the target device s computig power. Figure 2 shows the system cofiguratio of the HTML5 SVM. It basically has two layers: SVM core ad SVM adaptatio layer. The SVM core performs cotet executio. It is comprised of four compoets: the SEF loader, iterpreter, rutime eviromet, ad built-i library. The secod layer is the SVM adaptatio layer, which has six compoets for iterfacig with target HTML5-based web browsers Computatioal Offloadig. Mobile devices provide servicesiaapproachthatdiffersfromthatoftraditioal persoal computers (PCs) because the mobile device computig power processig speed, memory, storage space, ad battery life is limited. I particular, the LBS requires cosiderable processig ad battery power because it provides services based o locatio iformatio collected usig the global positioig system (GPS) or WiFi. Moreover, i recet years, use of LBS has icreasigly required more complex computatios ad eergy to expad its services to collect ad provide more complex iformatio. Computatioal offloadig is a cloud computig techiquethatisusedtoruprogramsadprovidecotetwhe there is a computig-powered-restricted eviromet [5 8]. Complex tasks require higher computig power. If the target device has isufficiet computig power, the QoS of the provided cotets/programs decreases. I this case, the offloadig techique is a possible solutio. Accordigly, the target device delegates complex tasks to a cloud server istead of directly executig them [9]. Figure 3 shows the proposed offloadig cocept. Methods for selectig computatio offloadig operatios are largely classified ito static ad dyamic approaches.

3 Mobile Iformatio Systems 3 SVM core SEF loader Header sectio loader RO sectio loader RW sectio loader Iterpreter (executio egie) Built-i library ANSI-C lib GNEX lib SVM system lib Rutime eviromet Memory maager Thread scheduler Evet hadler HTML5 base SVM adaptio layer File API XMLHttpRequest Evet listeer Cavas Web worker Mouse evet Keyboard evet Figure 2: System cofiguratio of the HTML5 smart virtual machie. Cloud Compute A Offload B Compute B Retur B Hybrid computatio fiished Compute A Compute B Mobile computatio oly fiished Figure 3: Computatioal offloadig cocept model. La ad Kim [1] classified offloadig techiques as show i Figure 4. Thestatictechiquereducestheexecutioloadbyselectig the part to be offloaded durig program developmet. Thestaticmethodhastheadvatageofalowloaditerms of cost aalysis at rutime. However, the cost aalysis is possible oly by usig predictable variables [11]. Meawhile, the dyamic method selects the part to be offloaded with cosideratio of the fluctuatio factors, such as the etwork state ad remaiig battery power, durig executio. The dyamicmethodcaaccuratelyreflectthecurretstateofthe mobile device. Nevertheless, it is difficult to desig a model that reflects all variables, ad the required workload for the cost aalysis is sigificat [12, 13]. Partial offloadig is a method of submittig some of the work to the cloud. Whe a specific task is frequetly used ad caot be performed i parallel, the commuicatio costs ad waitig times are icreased. The full offloadig method, o the other had, addresses oly the iteractio with the user o the mobile device; it defers the executio to the cloud. Whe frequet iteractio with a user occurs, sychroizatio problems likewise occur. Therefore, it is ecessary to

4 4 Mobile Iformatio Systems Partial offloadig Static decisio Full offloadig Computatio offloadig Partial offloadig Dyamic decisio Full offloadig Figure 4: Classified computatio offloadig methods. selectively assig a operatio that is suitable for offloadig to the cloud. I the proposed approach, it was difficult to reflect realtime fluctuatig factors, such as mobility ad commuicatio scearios. Therefore, the offloadig decisio is based o the Mobile Augmetatio Cloud Service (MACS) model [14], which estimates the code trasmissio cost, memory, ad CPU usage for each fuctio usig the profiler at the cotet compile time. The offloadig object determies the uit of work based o the fuctio. 3. Offloadig Module for Smart Locatio-Based Services The overall system architecture for supportig smart LBs is show i Figure 5. Each mobile device ca use a appropriate fog based o its locatio whe offloadig is eeded to perform the service. We desiged ad implemeted a SVM offloadig module. Offloadig ca improve the performace of utilizig the computig power of the cloud but may result i overhead; thus if the offloadig gai is greater tha the overhead cost without offloadig a give task ucoditioally this should be doe selectively. The offloader proposed i this paper automatically determies to offload by statically offloadig through profilig of source code i the compilatio step ad automatically geerates offloadig code ad trasfers selected fuctio uit worktothefogbasedothelocatio. I the proposed approach, fog computig is employed. It is a localized service of cloud computig. It processes large amouts of data at the poit of data origi, rather tha at a remote server, such as cetralized cloud server. Moreover, it operates o the basis of locatio iformatio, similar to LBSs. Therefore, services ca be provided more effectively by usig fog computig whe offloadig complex operatios i LBSs. The overhead value icludes the trasmissio time of the data over the etwork, as well as the serializatio/deserializatio time of data trasmitted locally ad from the server. Aalysis of the overhead is very importat because the offloadig performace based o the overhead ca be lower tha whe the local operatio is performed. Figure 6 shows the offloadig module structure of the SVM proposed i this paper. First, the SVM is divided ito themobiledeviceadcloudserver.exceptforthethread scheduler i the rutime eviromet ad the adaptatio layer required by the actual host mobile platform, the two VMs are equivalet. I terms of cotet, the local SVM directly loads ad executes the dowloaded cotet. Meawhile, the cloud SVM loads the same cotet as the userexecuted cotet from the SVM applicatio database i the cloud. This cloud refers to both the existig cetralized cloud ad the fog eviromet. TheSVMotheserverhasthesamecofiguratioad operatio method for both cloud ad fog eviromets. By performig the loadig equivalet of VMs ad cotet o the mobile device ad the cloud, it is easy to offload the fuctios defied by the profiler without requirig additioal work or implemetig a server iterface. The process of performig the fuctio uit offloadig is as follows. First, the local SVM delegates the fuctio uit job to the server whe loadig the cotet ad callig the fuctio (desigated as offloadig) durig the commad executio. I this process, the required cotext iformatio for executig the correspodig fuctio is extracted, serialized, ad trasmitted to the server. The cotext data refer to geeral data, such as the program couter, commad iformatio, ad stack iformatio used durig cotet operatio i the iterpreter (the drivig egie of the SVM). The offloadig module sychroizes the cotext betwee the device ad server by sedig the cotext data of the SVM to the server. The server parses the correspodig data to extract the cotext iformatio required for executig the fuctio. It performs the task of the requested fuctio o the cloud SVM through cotext switchig. Whe the fuctio uit is fiished, the chaged cotext iformatio is serialized ad trasferred to the local SVM, which reflects the chaged cotext iformatio to its ow cotext to esure that the state aligs with the result of directly executig the fuctio. 4. Experimetal Results We applied the proposed offloadig techique to SVM for a smart device ad a HTML5 SVM to verify the improvemet ofthecotetexecutiospeed.tomeasuretheoverhead caused by offloadig, the local SVM was used with RaspberryPi B+, which has a Quad-Core ARM Cortex-A7 9 Mhz processor, 512 MB of memory, ad the Raspbia operatig system. Figure 7 depicts the compariso of executio times before ad after applicatio of offloadig through the SVM i various mobile devices ad web browsers. The SVM for the smart devices was tested o a ipad2 ad a Galaxy Tab 1.1. The algorithms used were the prime umber, -quee, ad perfect umber. HTML5 SVM was tested o a PC, ipad2, ad Galaxy Tab 1.1. The prime umber ad -quee algorithms were used. The experimetal results showed that the algorithm performace improved by offloadig as a whole, as show i

5 Mobile Iformatio Systems 5 Cloud Iteret Locatio iformatio Fog eviromet Fog eviromet Fog eviromet area 1 area 2 area 3 Figure 5: The system architecture to support smart LBS. SVM offloadig diagram Wireless coectio SVM Cloud SVM SEF loader cotext maager C 1 C 2 Cloud cotext maager C 2 C 1 SVM applicatio database SVM applicatio (.Sef) EMF Built-i library Iterpreter (executio egie) Offloadig module Cotext collector Iterpreter hadler Cotext seder Cotext receiver Cotext receiver Cotext seder Cotext collector Iterpreter hadler Iterpreter (executio egie) Load applicatio Built-i library.sef Evet hadler Rutime eviromet Thread scheduler Memory maager Thread scheduler Rutime eviromet Memory maager ios/adroid adaptio layer APIs OpeGL Views Media Touch evet Evet listeer Hardware evet HTML5 adaptio layer File API XMLHttpRequest Evet listeer Mouse evet Keyboard evet Cavas Web worker Figure 6: Proposed computatioal offloadig modules for smart locatio-based service. Figure 8. The executio time of the SVM with offloadig was higher tha those of the algorithm with a lower complexity ad the algorithm with a higher complexity. The executio time of the SVM before offloadig icreased as the complexity of the algorithm icreased. No sigificat differece was evidet i terms of processig time. Therefore, the higher the algorithm complexity was, the greater the performace improvemet rate was before ad after offloadig, as show i Figures 7 ad 8. However, i some experimets, the offloadig results showed a performace deterioratio due to offloadig overhead, which was icurred durig the algorithm executio i the HTML5 SVM, as show i Figure 9. The offloadig overhead could be geerally divided ito types, such as cotext serializatio ad parsig time o the cliet, cotext serializatio ad parsig time o the server, trasferrig cotext data ad cliets, additioal time icurred due to cotext switchig, ad fuctio loadig time at the server.

6 6 Mobile Iformatio Systems Offloadig Offloadig (a) SVM (ipad2), prime (N) (b) SVM (ipad2), N-quee (N N) Offloadig Offloadig (c) SVM (ipad2), perfect (N) (d) SVM (Galaxy Tab 1.1) prime (N) Offloadig Offloadig (e) SVM (Galaxy Tab 1.1), N-quee (N N) (f) SVM (Galaxy Tab 1.1), perfect (N) Figure 7: Comparisos of the performace evaluatio usig offloadig i SVM o smart devices. As a result of ruig the algorithm with offloadig i each device, the serializatio ad parsig time of the server were costat. Meawhile, the serializatio ad parsig time of the cliet differed depedig o the device performace. I additio, sice the additioal overhead i the cotext switch ad fuctio loadig durig the operatio of the cliet SVM depeded o the device performace, it was cofirmed that the time varied depedig o the device. 5. Coclusios LBSs have recetly become highly available ad valuable, ad they ow ivolve a variety of applicatios. However, with their icreasig sophisticatio, more complicated operatios arerequired,whichcausesissuesothemobiledevice the mai executio eviromet of the LBS. A complex computatio load requires high computig power; thus, it is difficult

7 Mobile Iformatio Systems Offloadig Offloadig (a) SVM (PC), prime (N) (b) SVM (Galaxy Tab 1.1), prime (N) Offloadig Offloadig (c) SVM (ipad2), prime (N) (d) SVM (PC), N-quee (N N) Offloadig Offloadig (e) SVM (Galaxy Tab 1.1), N-quee (N N) (f) SVM (ipad2), N-quee (N N) Figure 8: Performace evaluatio of the offloadig i HTML5 SVM for same algorithms with differet target devices. to perform tasks that require high computatios o devices with low computig power. Offloadig addresses this issue. With offloadig, the device delegates to the server a task with a high computatioal complexity. It seds to the server the data required for the task or the ruig-program cotext iformatio. Because the server receives the job executio result ad the chaged cotext iformatio, the resource cosumptio of the computig job ca be drastically reduced basedothejobcharacteristics.suchoffloadigcaovercome low performace by providig high computig power of the server. Nevertheless, additioal overhead is icurred o accout of the commuicatio cost of trasmittig ad receivig data betwee the device ad server. The average commuicatio cost i this study was 8 ms for the prime umber algorithm (N), 7 ms for the -quee algorithm (N N), ad1msfortheperfectalgorithm(n). Therefore, the problem could be mitigated by leveragig fog computig, which ca perform itermediate processig for each regio usig locatio iformatio. Through the offloadig techique preseted i this paper, the SVM provides high computig performace from the server. It ca thus perform tasks that require high computig

8 8 Mobile Iformatio Systems PC N-quee (N N) algorithm Executio time PC prime (N) algorithm Executio time Galaxy Tab 1.1 N-quee (N N) algorithm Executio time Galaxy Tab 1.1 prime (N) algorithm Executio time ipad2 N-quee (N N) algorithm Executio time ipad 2 prime (N) algorithm Executio time Nooffloadig Offloadig, ot icludig overhead Offloadig, icludig overhead Nooffloadig Offloadig, ot icludig overhead Offloadig, icludig overhead Figure 9: Offloadig overhead o HTML5 SVM. operatios, eve o low-performace platforms. Offloadig icludes cotext serializatio ad parsig time, server serializatio ad parsig time, ad data trasmittig ad receivig time. Thus, because additioal overhead, such as server drive time,isicurred,theoffloadigshouldbeperformedoly whe the executio time of the job to be offloaded is larger tha the overhead icurred i offloadig. The SVM offloadig module curretly uder study has a structure for delegatig a task to a server through offloadig durig a specific fuctio call. This call is made usig precalculated profile iformatio while the cotet is ruig. Eve though idiscreet offloadig is a simple task with less time tha overhead, its applicatio to offloadig ca be used for lower performace tha the existig oe. To solve this problem,theoverheadcausedbytheoffloadigmoduleofthe SVM is miimized through a decisio model that determies i advace the efficiecy of offloadig by calculatig the performace differece before ad after applyig the offloadig applicatio. We ited to perform research to improve the performace accordigly. Coflicts of Iterest The authors declare that there are o coflicts of iterest regardig the publicatio of this paper. Ackowledgmets This research was supported by Basic Sciece Research Program through the Natioal Research Foudatio of Korea (NRF) fuded by the Miistry of Sciece, ICT ad Future Plaig (o. 216R1A2B48392). Refereces [1] E. Kaasie, User eeds for locatio-aware mobile services, Persoal ad Ubiquitous Computig, vol.7,o.1,pp.7 79, 23. [2]A.Pigley,Y.Wei,Z.Na,F.Xiwe,adZ.Wei, CAP:A cotext-aware privacy protectio system for locatio-based services, i Proceedigs of the 29th IEEE Iteratioal Coferece o Distributed Computig Systems Workshops (ICDCS 9), pp , Motreal, Caada, Jue 29. [3] Y.S.LeeadY.S.So, Astudyothesmartvirtualmachiefor executig virtual machie codes o smart platforms, Iteratioal Joural of Smart Home,vol.6,o.4,pp.93 16,212. [4]Y.So,S.Oh,adY.Lee, Desigadimplemetatioof HTML5 based SVM for itegratig rutime of smart devices ad web eviromets, Iteratioal Joural of Smart Home, vol. 8, o. 3, pp , 214. [5] K. Yag, S. Ou, ad H.-H. Che, O effective offloadig services for resource-costraied mobile devices ruig heavier

9 Mobile Iformatio Systems 9 mobile iteret applicatios, IEEE Commuicatios Magazie, vol.46,o.1,pp.56 63,28. [6] K.Kumar,J.Liu,Y.-H.Lu,adB.Bhargava, Asurveyofcomputatio offloadig for mobile systems, Mobile Networks ad Applicatios, vol. 18, o. 1, pp , 213. [7] C. Shi, K. Habak, P. Paduraga, M. Ammar, M. Naik, ad E. Zegura, COSMOS: computatio offloadig as a service for mobile devices, i Proceedigs of the 15th ACM Iteratioal Symposium o Mobile Ad Hoc Networkig ad Computig (MobiHoc 14), pp , Philadelphia, PA, USA, August 214. [8] B. G. Chu, Cloecloud: elastic executio betwee mobile device ad cloud, i Proceedigs of the 6th ACM Coferece o Computer Systems, pp , Salzburg, Austria, April 211. [9] H. T. Dih, C. Lee, D. Niyato, ad P. Wag, A survey of mobile cloud computig: architecture, applicatios, ad approaches, Wireless Commuicatios ad Mobile Computig,vol.13,o.18, pp , 213. [1] H. J. La ad S. D. Kim, A taxoomy of offloadig i mobile cloud computig, i Proceedigs of the 7th IEEE Iteratioal Coferece o Service-Orieted Computig ad Applicatios (SOCA 14),pp ,Matsue,Japa,November214. [11] C. Wag ad Z. Li, A computatio offloadig scheme o hadheld devices, JouralofParalleladDistributedComputig, vol. 64, o. 6, pp , 24. [12] H.-Y. Che, Y.-H. Li, ad C.-M. Cheg, COCA: computatio offload to clouds usig AOP, i Proceedigs of the 12th IEEE/ ACMIteratioalSymposiumoCluster,CloudadGridComputig (CCGrid 12), pp , IEEE, Ottawa, Caada, May 212. [13] T.-Y. Li, T.-A. Li, C.-H. Hsu, ad C.-T. Kig, Cotext-aware decisio egie for mobile cloud offloadig, i Proceedigs of the IEEE Wireless Commuicatios ad Networkig Coferece Workshops (WCNCW 13), pp , Shaghai, Chia, April 213. [14] D. Kovachev, T. Yu, ad R. Klamma, Computatio offloadig from mobile devices ito the cloud, i Proceedigs of the IEEE1thIteratioalSymposiumoParalleladDistributed Processig with Applicatios,pp ,212.

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