Dynamic Mobile Cloud Computing: Ad Hoc and Opportunistic Job Sharing

Transcription

1 2011 Fourth IEEE Internatonal Conference on Utlty and Cloud Computng Dynamc Moble Cloud Computng: Ad Hoc and Opportunstc Job Sharng Nroshne Fernando 1, Seng W. Loke 2 and Wenny Rahayu 3 Department of Computer Scence and Computer Engneerng, La Trobe Unversty, Australa { 1 tnfernando@students., 2 S.Loke@, 3 W.Rahayu@}latrobe.edu.au Abstract Despte ncreasng usage of moble computng, explotng ts full potental s dffcult due to problems such as resource sparseness. In ths paper, we explore the feasblty of a moble cloud computng framework to use local resources to solve these problems. The framework ams to determne a pror the usefulness of sharng workload at runtme. The results of experments conducted n Bluetooth transmsson and an ntal prototype are also presented. Furthermore, we dscuss a prelmnary analytcal model to determne whether or not a speedup wll be possble n offloadng. Keywords-moble cloud computng; offloadng I. INTRODUCTION Moble computng allows the user to be locatonally ndependent by provdng a tool when and where t s needed. However, problems arse when tryng to support moblty n computng devces: resource sparseness, hazardousness, fnte energy source, and low connectvty [18]. Offloadng moble applcatons to remote clouds such as Amazon EC2 has been dscussed [13] where servces are provded from the resource rch cloud to the moble devce. But ths type of offloadng depend on the connecton to the remote cloud, and the system fals n low connectvty scenaros [19]. A soluton would be to explot the capabltes of the moble cloud where a moble cloud s defned as a cloud of local resources utlzed to acheve a common goal n a dstrbuted manner. In ths paper, we refer to moble cloud n a dfferent vew than tradtonal cloud computng. Typcally, most of the local cloud resources would be moble (moble phones, PDAs, laptops etc) and would be owned by dfferent ndvduals. A moble cloud computng framework needs to be dynamc such that t can handle runtme resources and connectvty changes, proactve such that the costs are preestmated, opportunstc such that t explots resources as they are encountered, and cost effectve such that ts task dstrbuton s based on a cost model wth benefts to all partcpatng nodes. Furthermore, such a cloud wll not be lmted to hgh end moble devces, but wll be able to cater to low end devces as well. The future computng cloud that surrounds a user wll not just be smartphones and PCs, but also computers n shoes, watches, jackets, furnture, cups etc, and such a collecton of devces around the user wll change as he moves from one envronment to another, callng for greater opportunstc behavour and ad hoc set up, wth graceful falng. Although the concept of offloadng to local hardware has been explored n a number of frameworks, these do not satsfy all the requrements of a moble cloud as defned above. In ths paper, we dscuss the vson towards such a framework and explore the possbltes based on related work and some prelmnary experments. Challenges n such a framework nclude moblty, job parttonng, job dstrbuton n the cloud, recognzng a potental cloud devce, connectvty optons, cost estmaton and fault tolerance. Ideally, networkng would be accomplshed va a common exstng short-range protocol such as Bluetooth/WF etc, rather than specal protocols and we expermentally nvestgate ths possblty n ths paper. II. MOTIVATION Consder the case of Jane travelng on a tran n a foregn country. She needs help plannng her trp and wshes to ask from the many locals on the tran. To solve the language problem, she starts up a speech recognton and synthess applcaton n her phone. However, such a program would requre much computatonal power and dran her battery. Fortunately, Jane s able to use her moble phone to ntate settng up a vrtual cloud of local computatonal resources comprsed of the many moble devces on board. The aforementoned scenaro s only one example demonstratng the need for a moble cloud computng framework. In wearable computng, two major challenges are to reduce bulkness and low battery power [1]. Ths could be solved by offloadng the computatonal jobs to the local moble cloud, whle sensors and perpherals facltate the pervasve experence. In augmented realty, t has been suggested usng cloud resources [15] to solve smlar problems. Other areas nclude but are not lmted to resource demandng rch meda applcatons, vdeo edtng, facal recognton and mage search. We propose comprsng the local cloud wth other moble devces, enablng moblty wthout addtonal nfrastructure. Consderng the trends for smart phones, whch shows they are gettng more powerful each year, a local moble cloud wll be able to provde suffcent resources for ntensve moble apps. Also, snce most moble devces have sensng abltes, a cloud made up of moble devces wll be able to provde the users wth context aware servces. In the comng sectons, the devce that has a job to be completed and ntates the sharng/offloadng process wll be /11 $ IEEE DOI /UCC

2 referred to as master and the other devces dong a share of the job would be referred to as slaves/clents. III. PROPOSED ARCHITECTURE The man components of the framework can be gven as Resource handler, Job handler and Cost handler as n Fgure 1. The Resource Handler would be responsble for Fg. 1. Man components of a Moble cloud framework resource dscovery, establshng connectons, and exchangng meta data wth clents. Meta data ncludes devce detals such as CPU, avalable battery, prcng etc. The Context Manager senses and records contextual nformaton about clents, such as ther locaton, movement, and acceleraton whch s used by the Resource Montor to keep track on clent nodes. Context nformaton s mportant to regster f new devces are comng n, or f current devces are movng away, resultng n dsconnecton and fault tolerance mechansms. Cost handler would need to estmate costs and select sutable clent devces based on the devce meta data,user s specfc requrements. A mcropayment module should also be ncluded to handle the monetary transactons between clent devces and master devce. Job handler would be n charge of dynamcally parttonng the applcaton, creatng a job pool and managng a work dstrbuton mechansm. The basc steps are as follows: 1) Resource dscovery: The master devce needs to perform a dscovery procedure to search for potental clents wthn range. 2) Calculate costs: A comparson of potental clents, user prortes, requrements and constrants, s needed to estmate the cost of sharng. Clents can be selected based on ths calculaton. 3) Dstrbute jobs: Snce clent devces wll most probably dffer n ther resources and capabltes, a mechansm to decde whch job s assgned to whom s needed. Lessons could be drawn from [9]. 4) Run the jobs: Once the jobs have been dstrbuted, the clents would proceed to execute ther job/s. 5) Collect the results: When the clent devces fnsh ther job/s, results are sent back to the master, and reassembled. 6) Cleanup: Once the job s fnshed, a method to clean up the clent devces of data and/or code that were part of the job s needed to ensure data prvacy and securty. 7) Handle mcropayments: Payments to the clent nodes could be processed before or after the completon of the job. IV. COST MODEL, PROTOTYPE IMPLEMENTATION AND EXPERIMENTATION We conducted experments on Bluetooth to determne ts sutablty for connectvty on a moble cloud and the results of these tests were helpful n the constructon of our prototype. Our reasons for conductng the prelmnary tests on Bluetooth are due to ts advantages such as low radaton, low energy cost[6], and wde spread avalablty as opposed to other protocols such as WF [3] and 3G. Also, accordng to ts specfcatons, future versons wll be faster up to 24Mbps and consume less energy. 1 Therefore, when dealng wth low end devces Bluetooth would be a better opton, although not the only one. Ideally, the system should be able to swtch to other protocols dependng on the clent capabltes and energy stuaton. Two phones: Noka X6 and Noka 6500, and a PC were used n experments.these three devces were used snce they represent a range (low end moble, hgh end moble, resource rch PC) of devces. The PC used had Mcrosoft Wndows 7 Enterprse wth Intel(R) Core(TM) Duo CPU 2.66 GHz 2.67 GHz as processor, 2 GB RAM and Bluecove on Wnsock as Bluetooth drver. To mplement Bluetooth communcaton for J2SE n the PC, we used the thrd party lbrary BlueCove. Accordng to Noka s devce specfcatons 2, X6 runs Symban OS v9.4 wth 434 MHz whle 6500 runs Noka OS. Although the CPU detals for Noka 6500 are not avalable there, other sources such as Softpeda 3 gves t to be 170 MHz. A. Cost Formulae for Work Dstrbuton As the prmary phase n developng a framework for job sharng, a master-slave system consstng of moble devces on a pconet was desgned usng Bluetooth and J2ME. The master node ntates contact and seeks other clent nodes advertsng job sharng servces. Once the master fnds clent nodes and forms a pconet, t can proceed wth sharng/offloadng. 1) Sharng jobs: The tme for a master node to complete a job s gven n equaton 1 where T m s the total tme to complete the job n master and T pt s the tme to establsh the pconet. T d s the tme to partton and dstrbute jobs to slaves, T s s the tme to complete the master s job and T w&r s the tme the master spends watng for the slaves. Snce computng the master s job and recevng the results are done n separate threads n parallel, the greater value of T s and T w&r s used. The pseudo code for the master s gven n Lstng 1. T m = T pt + T d + Max(T s,t w&r ) (1) The tme to complete a job n the slave s gven n equaton 2 where T sl s the total tme to complete the job, T cn s the tme to connect to the master, T rcv s the tme to receve the job parameters from the master, T cp s the tme to complete specfcatons/x6-00/

3 start master // (1) do devce & servce dscovery untl slave servces are found nt numofdevs = numofservces + 1 long [] readwattmes= new long [ numservces ]; IF servces are found FOR each servce dscovered Open a Connecton Open an outputstream END FOR ELSE Ext app END I F // (2) T pt = (2) (1) FOR each slave generate & dstrbute parameters END FOR // (3) T d = (3) (2) S t a r t t h r e a d ownwork for dong own job Thread tread [] = new Thread [ numofservces ]; FOREACH tread Open nputstream // (4) Wat for ncomng transmssons from slave Read r e s u l t // (5) T w&r [] = (5) (4) END FOREACH Jon all slave threads tread and thread dong own job wth man thread // (6) T s = (6) (3) // (7) T w&r = add all values n readwattmes array // (8) T m = (8) (1) Lstng 1. Pseudo code for master slave s job and T sr s the tme to send the completed results back. Lstng 2 gves the pseudo code for the slave. start slave // (1) set devce as dscoverable start servce and wat for connectons open nputstream // (2) T cn = (2) (1) start recevng job params (3) WHILE NOT end of nputstream read params END WHILE close nputstream // (4) T rcv = (4) (3) run job // (5) T cp = (5) (4) open output stream // (6) Send completed result // (7) T sr = (7) (6) close connecton // (8) T sl = (8) (1) Lstng 2. Pseudo code for slave devce T sl = T cn + T rcv + T cp + T sr (2) 2) Offloadng on a slave: Equaton 3 gves the parameters for offloadng n the master s perspectve. Here, T moff s the total tme to complete the job n the master node, T oj s the tme to send job parameters to slave/s and T w&r s the tme the master spends watng tll the slave starts sendng results and the tme to complete recevng the complete result. The pseudo code for the master s gven n Lstng 3. The offloaded slave s tme s gven n equaton 4 where T soff s the total tme t takes to complete the job, T cn s the tme to connect to the master, T cp s the tme to complete the computaton and T sr s the tme to send the completed results back to the master. The pseudo code for the offloaded slave s the same as for the job sharng slave as gven n Lstng 2. start master // (1) do devce & servce dscovery untl a slave s found IF a servce s found open connecton open outputstream // (2) //T pconetoff = (2) (1) generate & dstrbute parameters for slave // (3) //T oj = (3) (2) open InputStream // (4) read offloaded job results from slave close nputstream close connecton to slave END IF // (5) //T w&r = (5) (4), T moff = (5) (1) Lstng 3. Pseudo code for master offloadng code on slave T moff = T pconetoff + T oj + T w&r (3) T soff = T cn + T rcv + T cp + T sr (4) 3) Analytcal model : Offloadng to n slaves: We now provde an analytcal model generalzng offloadng to n slaves, showng the condtons under whch such offloadng results n speedups. We assume that a job s unformly parttoned such that all slaves get an equal szed job so that a Job = n =1 J, there s no dependency among each job, and that all n slaves n the pconet would be dedcated nodes such that ther computatonal power would be predomnantly avalable for the job. Snce J 1 = J 2 =... J = J = J n for n slaves, Job = nj. Therefore, f s the tme to do Job sequentally on master, and f the tme t takes the master to run a job J sequentally s T, then, = nt (5) If the computatonal tme for one slave slave s Tc, then Tc would be the computatonal tme to do job J. T Tc,, T = k Tc (6) Where k s a constant dependng on each slave. Therefore, from (5) and (6), we see that snce T = T0 = k Tc n, n and T0 Tc = nk.ifnk = K where K s a constant, then Tc = K. = K Tc (7) If the Speedup s S, and T w&r s tme to wat for and receve all results from slaves, then = T0 T m S = T master T m = T pt + T oj + T w&r T S = 0 T pt+t oj+t w&r (8) For n devces and a fxed job sze of J, T p and T oj would depend on the bluetooth stack/drvers of the devce. Hence, for a gven devce, let us assume that T p + T oj wouldbea constant α. Then, S = KTc α +T w&r (9) If t s the tme the th thread on master spends watng and recevng from the th slave, then the total wat and receve tme s equal to the maxmum t value of the n threads. 283

4 Ths can be represented as: T w&r = max{t 1,t 2,...t..., t n } (10) If the communcaton overhead s δ, t = Tc +Tsr +δ, T w&r = max{(tc + Tsr + δ )}, K S = Tc α+max{(tc +Tsr +δ )} (11) Snce the job sze s fxed, Tsr solely depends on the devce Bluetooth,.e, the communcaton cost Ccomm. Snce δ s also related to communcaton overhead, Tsr + δ s proportonal to Ccomm. Therefore, f β K = Tsr + δ, then from (IV-A3), S = Tc α+max{(tc. +β )} Here, α depends on the master devce, K s the rato of computatonal tmes between the master devce and the th slave, Tc s the computatonal tme on th slave, max{(tc + β )} s the maxmum value of 1 to n slaves sum of computatonal tme and send result tme. Therefore, for S>1, K > α+max{(tc+β)} Tc. By maxmzng the rght hand sde, snce α s related to master s communcaton cost, the range of α falls between the maxmum and mnmum values of MasterComm. The same apples for Tc and β whch are related to the computaton tme and communcaton tme of t h slave respectvely. So, MasterComm mn α MasterComm max SlaveComp mn Tc SlaveComp max β SlaveComm max SlaveComm mn Therefore, the maxmum value of from MasterCommmax SlaveComm mn related to SlaveComp max (12) α Tc, max[ α Tc ] comes and max[max{(tc + β )}] s + SlaveComm max (13). Therefore, K > MasterCommmax +max{cp +Cm } Cp mn Where Cp and Cm represent SlaveComp and SlaveComm. From (6) and (13), Tc = K. Snce Tc rato s dependent on the computatonal capabltes of the devces, f K CPUslave CP Umaster then, consderng (13), n order to get a speedup greater than one from offloadng on n devces, CPUslave (14) Based on ths dervaton, f the rato between the CPU of the th slave and the master devce s greater than the sum of the communcaton cost of master and the maxmum sum of computaton and communcaton costs out of slaves 1 to n, there would defntely be a speedup. The rght hand sde of the nequalty gves the maxmum possble value and we have an assumpton that the CPU clock rates of the th slave and master are nversely proportonal to ther calculaton tmes. An applcaton of ths relaton wth actual data s gven n Secton IV-C3. CP Umaster > MasterCommmax +max{cp +Cm } Cp mn B. Bluetooth transmsson testng The experments on Bluetooth were focused on the effect of message length, dstance, protocol and buffer sze. Buffer sze refers to the sze of a message buffer. 1) Message length and Buffer sze: Messages of varyng lengths were transmtted from Noka X6 to 6500 for protocols RFCOMM and OBEX for buffer szes from 256 to 900 B. The trends for all buffer szes were almost lnear, wth 500 B gvng the best tme. When comparng these two protocols, t could be seen that RFCOMM was faster. 2) Dstance: Our tests showed that OBEX has more range than RFCOMM, and as data lengths ncrease, the maxmum range of transmsson decreases and so does the consstency. 3) Computatonal power and heterogenety: APCwas used to send and receve data to and from Noka X6 to nvestgate the effects of computatonal power and heterogenety. Fgure 2 shows the trend of tme aganst data lengths up to 2 MB n moble-to-moble, moble-to-pc, and PC-to-moble. Surprsngly, PC to Noka X6 has the lowest performance, Fg. 2. Bluetooth transmsson experments whle Noka to PC gves the best tme suggestng that although the PC s more powerful than X6, transmsson also depends on the recever s capabltes. It could be that use of thrd party lbrary to nterface wth the natve Bluetooth stack of the PC (Bluecove) mght be addng an overhead. Accordng to the Bluecove speed tests on the web, n some cases moble to PC s faster, whle n other cases, the opposte s true. C. Intal Experments wth Dstrbuted Mandelbrot Set Generaton As a frst step for a framework for job sharng, we mplemented a prototype that parttons a predefned problem and dstrbutes the job among moble and statonary devces. It was developed usng Java, and was executed on the same two moble phones and the PC that was used n the Bluetooth tests. RFCOMM was used snce t had proved to be faster than OBEX n prevous experments. As a sample applcaton, we mplemented a dstrbuted Mandelbrot set generaton and compared the results wth ts sequental mplementaton on the ntatng master devce. Although t s not a typcal canddate for a moble applcaton, Mandelbrot generaton was selected to study the feasblty and ssues n the prelmnary tests due to ts ease of job parttonng. 1) The Mandelbrot set: The Mandelbrot set s a set of ponts stuated n the complex plane and the boundary of these ponts forms a fractal. Iteratng the formula gven n Equaton (15) gves ths set of ponts. Z 0 =0,Z n+1 = Zn 2 + C (15) Where C s a constant number on the Complex plane, and Z n s the current pont. As the number of teratons per pont ncreases, the accuracy of the mage ncreases. 2) Parallelzng the Mandelbrot set generaton: For a gven pont, the Mandelbrot teraton feeds the result of the prevous step nto the next one. Hence, the smplest method of parttonng ths algorthm s to assgn each devce a 284

5 contnuous block of the mage. An example of how an mage of 300 X 350 pxels s splt s gven n Fgure 3. Fg. 3. Splttng up the Mandelbrot mage among three devces 3) Executon results: We tested the applcaton for an mage of pxel sze 300 X 300 for varyng number of teratons from 200 to The dstrbuted verson was tested on two to three devces and the results were compared wth the performance of the monolthc verson. Fgures 4(a,b) shows the results of the Mandelbrot mage generaton usng Noka 6500 and X6 as the Master. Fg. 5. Processng tmes Local vs Shared executon where the tme for Shared executon s gven as a percentage of the Local tme. Wth regards to Secton IV-A3, expermental results above, we dentfy that the CPU rato for phones X6 and 6500 as When we apply average values from an nstance of a seres of experments where the master was 6500 and job was offloaded to X6, the value of 342, , ,369 MasterComm max +max{cp 1+Cm 1} Cp mn 1 334,369,gvng the followng nequalty: > s (1) thereby satsfyng the relaton n (IV-A3). Fg. 4. Iteratons vs Speedup (a,b)for dfferent confguratons. Here, Speedup s defned by the followng formula n equaton (16). Speedup = T 1 /T m (16) Where T 1 s the executon tme of the sequental algorthm on master devce, and T m s the dstrbuted verson measured on master devce. As can be seen from Fgure 4(a), sharng/offloadng wth Noka 6500 as master gves speedups from 1.29 up to However, the results obtaned wth Noka X6 as master show that the dstrbuted verson faled to produce a speedup except when t was offloaded and shared wth the PC, snce X6 s more powerful. When work s shared wth a devce that s substantally weaker (Noka 6500), the weak devce degrades the overall performance, snce the total outcome cannot be gven untl all the devces complete ther work. Fgure 5 shows the processng tmes n local executon and the shared verson n, (A)Noka 6500 as master and (B)Noka X6 as master. X axs shows the results of executons for varyng Mandelbrot teratons from 500, 1000, 2000 and Here, 1(A) represent the results of executon for 500 teratons n Mandelbrot generaton n whch Noka 6500 was the master and X6 was the slave. Here, the computatonal tme measured on 6500 when t shared the workload wth the X6 was only 73% that of ts monolthc executon. 1 (B) shows the results for 500 teratons when X6 was master and 6500 was slave. In ths case, exeuton tme on X6 was 84% that of when the Mandelbrot generaton was performed on the devce (X6) tself. Lkewse for results represented n 2,3, and 4. In all cases, though only (A) gave a speedup, t s apparent that the processng tme has lessened, suggestng an energy savng [12], gven the energy cost for Bluetooth communcaton s also taken nto account. V. RELATED WORK Two common methods relatng to offloadng and/or sharng work from moble devces are, parttonng applcatons and VM mgraton. These nclude the approach of cyber foragng by Satyanarayan [19] for avalable resources and offload the work through VM synthess to a local cloudlet. The Scavenger framework [11] also employs cyber foragng.clonecloud [4] uses VM mgraton, whle MAUI [5] uses a combnaton of VM mgraton and code parttonng.kemp s [10] Cuckoo framework offloads moble applcatons onto local and remote cloud servers such as the Amazon EC2. Marnell [16] s Hyrax based on Hadoop 4 presents a central server that coordnates data and jobs on connected moble devces. The Moble Message Passng Interface (MMPI) framework [7] s a moble verson of the standard MPI over Bluetooth. Huerta-Canepa and Lee [8] present a framework for vrtual moble cloud focusng on common goals Enablng moble clouds wth context awareness has been dscussed n [14] and [17]. The system proposed n [8] also uses context to dentfy common goals. VI. SUMMARY AND FUTURE CHALLENGES Based on our experments, we surmse that Bluetooth s one of the vable optons as the mode of transmsson n a moble cloud. However, our future experments wll not be confned to Bluetooth and would explore other protocols such as WF and 3G as well. Also, our experments show that Bluetooth transmsson tme s lnear aganst data length and dstance and the buffer sze plays a substantal role, and RFCOMM outperforms OBEX n terms of speed, but OBEX has more range and computatonal power alone s not the decdng factor n transmsson speed

6 The results wth prototype for dstrbuted Mandelbrot set shows that merely dstrbutng a task regardless of devce capabltes wll not always gve a speedup. In case of equal job sharng, a speedup could only be acheved n cases where the slave node/s were of hgher performance than master. Therefore, the devce capabltes of the master and potental slaves need to be compared before a job s shared. Tests wth offloadng show that offloadng gves better performance than sharng. These factors pont out the need for a cost model, and we am to explore work stealng [2] as a way of effectve load balancng. [16] E. E. Marnell. Hyrax: Cloud Computng on Moble Devces usng MapReduce. Carnege Mellon Unversty, Masters thess, [17] P. Papakos, L. Capra, and D. S. Rosenblum. Volare: context-aware adaptve cloud servce dscovery for moble systems. In Proceedngs of the 9th Internatonal Workshop on Adaptve and Reflectve Mddleware, ARM 10, pages 32 38, New York, NY, USA, ACM. [18] M. Satyanarayanan. Fundamental challenges n moble computng. In Proceedngs of the ffteenth annual ACM symposum on Prncples of dstrbuted computng, PODC 96, pages 1 7, New York, NY, USA, ACM. [19] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Daves. The case for vm-based cloudlets n moble computng. Pervasve Computng, IEEE, 8(4):14 23, REFERENCES [1] O. Amft and P. Lukowcz. From backpacks to smartphones: Past, present, and future of wearable computers. Pervasve Computng, IEEE, 8(3):8 13, [2] R. D. Blumofe and C. E. Leserson. Schedulng multthreaded computatons by work stealng. J. ACM, 46(5): , [3] S. Cherry. Update: Wf takes on bluetooth. Spectrum, IEEE, 45(8):14, [4] B.-G. Chun, S. Ihm, P. Manats, M. Nak, and A. Patt. Clonecloud: elastc executon between moble devce and cloud. In Proceedngs of the sxth conference on Computer systems, EuroSys 11, pages , New York, NY, USA, ACM. [5] E. Cuervo, A. Balasubramanan, D.-k. Cho, A. Wolman, S. Sarou, R. Chandra, and P. Bahl. Mau: makng smartphones last longer wth code offload. In Proceedngs of the 8th nternatonal conference on Moble systems, applcatons, and servces, MobSys 10, pages 49 62, New York, NY, USA, ACM. [6] J.-j. Dong and H.-c. Xu. A Dstrbuted Onlne Test System Based on Bluetooth Technology. In Proceedngs of the Second World Congress on Software Engneerng (WCSE), volume 1, pages 15 17, [7] D. C. Doolan, S. Tabrca, and L. T. Yang. Mmp a message passng nterface for the moble envronment. In Proceedngs of the 6th Internatonal Conference on Advances n Moble Computng and Multmeda, MoMM 08, pages , New York, NY, USA, ACM. [8] G. Huerta-Canepa and D. Lee. A vrtual cloud computng provder for moble devces. In Proceedngs of the 1st ACM Workshop on Moble Cloud Computng & Servces: Socal Networks and Beyond, MCS 10, pages 6:1 6:5, New York, NY, USA, ACM. [9] K. A. Hummel and H. Meyer. Self-organzng far job schedulng among moble devces. In Self-Adaptve and Self-Organzng Systems Workshops, SASOW Second IEEE Internatonal Conference on, pages , [10] R. Kemp, N. Palmer, T. Kelmann, and H. Bal. Cuckoo: a computaton offloadng framework for smartphones. In Proceedngs of The Second Internatonal Conference on Moble Computng, Applcatons, and Servces, MobCASE 10, [11] M. Krstensen. Scavenger: Transparent development of effcent cyber foragng applcatons. In Proceedngs of the IEEE Internatonal Conference on Pervasve Computng and Communcatons (PerCom), pages , aprl [12] M. Krstensen and N. Bouvn. Usng w-f to save energy va p2p remote executon. In Pervasve Computng and Communcatons Workshops (PERCOM Workshops), th IEEE Internatonal Conference on, pages , aprl [13] K. Kumar and Y.-H. Lu. Cloud computng for moble users: Can offloadng computaton save energy? Computer, 43(4):51 56, [14] H. J. La and S. D. Km. A conceptual framework for provsonng context-aware moble cloud servces. In Proceedngs of the IEEE 3rd Internatonal Conference on Cloud Computng (CLOUD), pages , [15] X. Luo. From augmented realty to augmented computng: A look at cloud-moble convergence. In Ubqutous Vrtual Realty, ISUVR 09. Internatonal Symposum on, pages IEEE,