INFORMATICA, 2015, Vol. 26, No. 1, 89 110 89 2015 Vilius Uiversity DOI: http://dx.doi.org/10.15388/iformatica.2015.40 A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices i Cloud Eviromet Chuli LI 1,2, Layua LI 2 1 Departmet of Computer Sciece, Wuha Uiversity of Techology, Wuha 430063, PR Chia 2 The Key Laboratory of Embedded System ad Service Computig, Miistry of Educatio Togi Uiversity, Shaghai 200092, PR Chia e-mail: chuli74@aliyu.com, wtu@public.wh.hb.c Received: Jauary 2014; accepted: September 2014 Abstract. Mobile cloud computig has emerged aimig at assistig mobile devices i processig computatioally or data itesive tasks usig cloud resources. This paper presets a optimizatio approach for utilizig cloud services for mobile cliet i mobile cloud, which cosiders the beefit of both mobile device users ad cloud dataceters. The mobile cloud service provisioig optimizatio is coducted i parallel uder the deadlie, budget ad eergy expediture costrait. Mobile cloud provider rus multiple VMs to execute the obs for mobile device users, the cloud providers wat to maximize the reveue ad miimize the electrical cost. The mobile device user gives the suitable paymet to the cloud dataceter provider for available cloud resources for optimize the beefit. The paper proposes a distributed optimizatio algorithm for utilizig cloud services for mobile devices. The experimet is to test covergece of the proposed algorithm ad also compare it with other related work. The experimets study the impacts of ob arrival rate, deadlie ad mobility speeds o eergy cosumptio ratio, executio success ratio, resource allocatio efficiecy ad cost. The experimet shows that the proposed algorithm outperforms other related work i terms of some performace metrics such as allocatio efficiecy. Key words: mobile cloud, cloud stadards, mobile device, eergy efficiecy. 1. Itroductio Mobile devices are icreasigly becomig the most effective ad coveiet commuicatio tools i our daily life. However, the mobile devices are facig may challeges i their limited resources e.g., battery life, storage, ad badwidth ad commuicatios e.g., mobility ad security, which sigificatly impede the improvemet of service qualities. The applicatio of cloud computig is ot limited oly to PC, but also to mobile devices ad mobile termials. With the rapid developmet of mobile Iteret, cloud computig services providig for mobile phoes ad other mobile termials have emerged. Curretly, mobile devices are becomig the popular istrumet for accessig the cloud eviromet. While roamig aroud with a mobile hadheld device, a user ca eoy * Correspodig author.
90 C. Li, L. Li the vast computatio power ad diverse capabilities with the support of the cloud eviromet. I mobile cloud eviromet, users access cloud services with mobile devices. Mobile cloud computig ca ehace user experiece because mobile devices have limited computig power, storage space, ad battery life. Storage ad computig capabilities of mobile devices ca be exteded by utilizig cloud resources Dih et al., 2007. Mobile cloud computig itroduces a variety of beefits over traditioal mobile services. With cloud computig, mobile applicatios ca trasfer some computatio task to be executed o the cloud ceter. May mobile applicatios are developed uder mobile cloud computig cocept icludig e-commerce, healthcare, ad computer games. Cloud computig with mobile devices eables may ew applicatios that were uavailable i the past. For example, a mobile cloud could be made of smart phoes to process realtime data e.g., video ad audio feeds, GPS coordiates. Other mobile cloud applicatios might ivolve real time cotrol/actuatio i wireless powered sesor etworks. Mobile cloud computig has emerged aimig at assistig mobile devices i processig computatioally or data itesive tasks usig cloud resources. This paper presets a optimizatio approach for utilizig cloud services for mobile cliet i mobile cloud, which cosiders the beefit of both mobile device users ad cloud dataceters. The paper proposes a distributed optimizatio algorithm for utilizig cloud services for mobile devices. The experimet is to test covergece of the proposed algorithm ad also compare it with other related work. The experimets study the impacts of ob arrival rate, deadlie ad mobility speeds o eergy cosumptio ratio, executio success ratio, resource allocatio efficiecy ad cost. The experimet shows that the proposed algorithm outperforms other related work i terms of some performace metrics such as resource allocatio efficiecy. The rest of the paper is structured as followigs. Sectio 2 discusses the related works. Sectio 3 presets a optimizatio approach for utilizig cloud services for mobile cliet i mobile cloud. Sectio 4 presets distributed optimizatio algorithm for utilizig cloud services for mobile devices. I Sectio 5 the experimets are coducted ad discussed. Sectio 6 gives the coclusios to the paper. 2. Related Works ad Motivatios Curretly, may researches focused o cloud computig with mobile devices. Mobile cloud computig combies wireless access service ad cloud computig to improve the performace of mobile applicatios. Ferber ad Rauber 2012 propose a pipeliig task cocept o a sigle ecrypted chael betwee a mobile device ad a cloud resource to better use the 3G etwork coectios. Barbera et al. 2012 study the mobile software/data backup of both mobile computatio offloadig ad mobile software/data backups i real-life scearios. Zhag et al. 2012 ivestigate the schedulig policy for collaborative executio i mobile cloud computig. The desig obective is to miimize the eergy cosumed by the mobile device, while meetig a time deadlie. They formulate this miimum-eergy task schedulig problem as a costraied shortest path problem o a directed acyclic graph, ad adapt the caoical LARAC algorithm to solvig this problem approximately. Rahimi et al. 2013 study mobility-aware optimal service allocatio
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 91 i mobile cloud computig. They propose a framework to model mobile applicatios as locatio-time workflows LTW of tasks, here user mobility patters are traslated to a mobile service usage patters. Abolfazli et al. 2013 propose a market-orieted architecture based o SOA to stimulate publishig, discoverig, ad hostig services o earby mobiles, which reduces log WAN latecy ad creates a busiess opportuity that ecourages mobile owers to embrace service hostig. Group of mobile phoes simulate a earby cloud computig platform. Barbarossa et al. 2013 proposes oit allocatio of computatio ad commuicatio resources i multiuser mobile cloud computig. They propose a method to oitly optimize the trasmit power, the umber of bits per symbol ad the CPU cycles assiged to each applicatio i order to miimize the power cosumptio at the mobile side. Lee et al. 2013 propose a collaborative framework that lets the mobile device participate i the computatio of Cloud Computig system by dyamically partitioig the workload across the device ad the system. The obective is to mitigate the deterioratio of etwork performace ad improve the overall system performace. Park et al. 2012 study mobile devices as resources i mobile cloud eviromets. They propose a resource allocatio techique which offers reliable resource allocatio cosiderig the availability of mobile resources ad movemet reliability of mobile resources. Shiraz et al. 2014 study rutime partitioig of elastic mobile applicatios for mobile cloud computig. They ivestigate the overhead of rutime applicatio partitioig o SMD by aalyzig additioal resources utilizatio o SMD i the mechaism of rutime applicatio profilig ad partitioig. Ge et al. 2012 propose a game-theoretic approach to optimize the overall eergy i a mobile cloud computig system. They formulate the eergy miimizatio problem as a cogestio game, where each mobile device is a player ad the strategy is to select oe of the servers to offload the computatio while miimizig the overall eergy cosumptio. Wag et al. 2013 cosider a mobile cloud computig MCC iteractio system cosistig of multiple mobile devices ad the cloud computig facilities. They provide a ested two stage game formulatio for the MCC iteractio system. I the first stage, each mobile device determies the portio of its service requests for remote processig i the cloud. I the secod stage, the cloud computig facilities allocate a portio of its total resources for service request processig depedig o the request arrival rate from all the mobile devices. Zhag et al. 2013 model the resource allocatio process of a mobile cloud computig system as a auctio mechaism with premium ad discout factors. The premium ad discout factors idicate complemetary ad substitutable relatios amog cloud resources provided by the service provider. Makris et al. 2012 propose a deploymet scheme to offload expesive computatioal tasks from thi, mobile devices to the cloud dataceter, so that the proposed method prologs battery life for mobile devices; meawhile provide rich user experieces for such mobile applicatios. Shiraz et al. 2013a aalyzes the impact of VM deploymet ad maagemet o the executio time of applicatio i mobile cloud computig. They ivestigate VM deploymet ad maagemet for applicatio processig i simulatio eviromet by usig CloudSim. Hussai ad Hussai 2011 discuss ad formalize the issue of cloud service selectio i geeral ad
92 C. Li, L. Li propose a multi-criteria cloud service selectio methodology, they cosider cost, pricig policy, performace etc as multiple criteria. Shiraz et al. 2013b propose distributed applicatio processig frameworks i smart mobile devices for mobile cloud computig. The author reviews existig Distributed Applicatio Processig Frameworks DAPFs for smart mobile devices i mobile cloud computig domai. The obective is to highlight issues ad challeges to existig DAPFs i developig, implemetig, ad executig computatioal itesive mobile applicatios withi mobile cloud. Gkatzikis ad Koutsopoulos 2013 advocate the eed for ovel cloud architectures ad migratio mechaisms that effectively brig the computig power of the cloud closer to the mobile user. They cosider a cloud computig architecture that cosists of a back-ed cloud ad a local cloud, which is attached to wireless access ifrastructure. Lu et al. 2013 itroduce the cocept of a Iteret-based Virtual Computig Eviromet ivce, which aims to provide Cloud services by a dyamic combiatio of data ceters ad other multi-scale computig resources o the Iteret. Li et al. 2009 propose Armada, a efficiet rage query processig scheme to support delay-bouded sigle-attribute ad multiple-attribute rage queries. Bessis et al. 2013 propose a model for supportig the decisio makig process of the cloud policy for the deploymet of virtual machies i cloud eviromets. They explore two cofiguratios, the static case ad the dyamic case to study deploymet of virtual machies. Choi et al. 2013 study proposes a method of desigig a scheme for applyig MapReduce of the FP-Growth algorithm. Our previous work Li ad Li, 2014 focuses o batch processig applicatios for mobile cloud computig eviromet. The mobile device s user requiremets arrive i batches ito the mobile cloud systems. For example, mobile device s users submit batch obs e.g., fiacial aalytics, scietific simulatios to mobile cloud system for fast processig. The paper of Li ad Li 2014 proposes a phased schedulig for batch processig applicatios i mobile cloud, which is divided ito mobile device s batch applicatio optimizatio ad mobile device s ob level optimizatio. The formulatio model ad method of this paper is differet from Li ad Li 2014. Li ad Li 2012 is orieted to SaaS cloud, it combies three perspectives: SaaS user, SaaS provider ad cloud resource providers, Li ad Li 2012 presets a efficiet cloud resource provisioig algorithm, which is beeficial for SaaS users, SaaS provider ad cloud resource provider. This paper presets a optimizatio approach for utilizig cloud services for mobile cliet i mobile cloud. It focus o mobile cloud service provisioig. The research obect ad formulatio model of this paper is differet from Li ad Li 2012. 3. A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 3.1. Problem Descriptio The proposed mobile cloud system icludes mobile devices, cloud data ceter ad mobile cloud proxy. The mobile cloud users ru the iteractive applicatio o their devices.
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 93 Table 1 The descriptio of otatios. Notatios C ram ed i ec v v ram u u ram p p ram B i EB pe T i ecost e i er i E i Meaigs The maximum capacity of CPU of cloud provider The maximum capacity of memory of cloud provider Eergy dissipatio of cloud resource for provisioig VM for mobile device user i Eergy cosumptio rate of cloud resource for provisioig VM CPU of cloud resource required by a VM for mobile device user i Memory of cloud resource required by a VM for mobile device user i Paymet of mobile device user i to the cloud resource for CPU required by a VM Paymet of mobile device user i to the cloud resource for memory required by a VM The price of CPU of cloud resource The price of memory of the cloud resource The expese budget of mobile device user i Upper limit of eergy expediture of cloud provider Electricity price Time limits give by mobile device user i to complete all obs The paymet for the electricity usage of cloud provider Eergy dissipatio caused by mobile device user s th ob Eergy cosumptio rate for the mobile device user i to complete th ob Eergy limit of mobile device user i Wireless access poits provide radio resource i.e., badwidth, while data ceters provide computig resources e.g., CPU, memory, ad storage to support differet mobile applicatios. Mobile device ca access the Iteret through a access poit AP that is either Wireless Local Area Network WLAN or cellular 3G etwork. The mobile cloud proxy is resposible for maagig all commuicatios betwee the mobile device ad the cloud dataceter. The otatios used i sectios are listed i Table 1. Let v, v ram deote the amout of CPU ad memory etwork required by a VM for mobile device i from cloud provider. VM ca be expressed as vm = {v,v ram }. Let S = {s 1,s 2,...,s } deote the set of cloud providers. Each cloud provider supplies a pool of resources to host VM for the mobile device applicatio. The cloud provider supply CPU capacity expressed i MHz, the memory capacity expressed i megabytes. The processig power of a cloud resource provider s is measured by the average CPU speed., C ram deote the maximum capacity of CPU ad memory, which cloud provider s ca support VM for the mobile device. B = b 1,b 2,...,b i deote the set of mobile devices. The cloud provider provides the VMs for executig mobile device user s applicatios. A mobile device estimates its eergy cosumptio rate er l for executig the applicatio set A = {A 1,A 2,...,A i }, ad the eergy cosumptio costrait is C l. Eergy cosumptio rate of each mobile device i the mobile cloud is measured by Joule per uit time. Let ei be eergy exhausted by mobile device user i s th ob, ti is the executio time of mobile device applicatio i -th ob o the mobile cloud dataceter.
94 C. Li, L. Li The eergy cosumptio rate of the cloud dataceter is deoted by er i, which ca be writte as ei = er.ti. Service provisioig i mobile cloud computig is to maximize the mobile cloud utility is subect to the resource costraits of cloud dataceter ad QoS costraits of mobile device users. The service provisioig for mobile cliet i mobile cloud is formulated as the follows: MaxU MCC s.t. B i u + u ram, T i t i, i v, ecost EB, i v ram. 3.1 The service provisioig i mobile cloud computig aims to maximize U mcc subect to the costraits. v is CPU required by a VM for mobile device user i from the mobile cloud provider, v ram is the memory required by a VM for mobile device user i from the mobile cloud provider. The costrait implies that the provisioed CPU does ot exceed the total capacity of mobile cloud provider, the provisioed memory uits do ot exceed the total resource C ram of mobile cloud provider. Also there are some costraits related with mobile device user. Mobile device user should complete all obs uder the deadlie ad the budget. Mobile device user eeds to complete obs i the deadlie, T i, while the cost caot exceed the budgetb i. u, uram are the paymets of the mobile device user i to the cloud dataceter provider for CPU ad memory required by a VM respectively. B i is the budget of mobile device user i. For the mobile cloud dataceter providers, cloud dataceter providers ca t pay for the electricity cost more tha EB, which is the electricity expediture of mobile cloud dataceter providers. MaxU MCC = B i u + u logv + T i ti + u ram + + u ram E i logv ram + EB ecost N =1 e i s.t. B i u + u ram, C i v, i v ram, T i t i, ecost EB 3.2
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 95 Mobile cloud utility fuctios are maximized with specific parameter costraits of mobile cloud provider ad mobile device user. u logv +u ram logv ram is the reveue of cloud provider obtaied from mobile device user i. E i N =1 ei represets remaiig eergy of mobile device users. We ca apply the Lagragia method to above problem. Let us cosider the Lagragia form of service provisioig optimizatio i mobile cloud: L = EB ecost + + B i + β i u T i t i + u ram + + u logv E i v ram + ω B i N =1 e i u + u ram logv ram + λ i + u ram v + ρ T i t i + θeb ecost. 3.3 Sice the Lagragia is separable, MaxU MCC ca be decomposed ito the subproblems P md ad P cp which are separately coducted by mobile device user ad cloud dataceter provider as follows: MaxP cp = Max EB ecost + u s.t. ecost EB, MaxP md = Max s.t. T i t v i, { B i B i i u u v, + u ram + logv + u ram i E i N =1 logv ram v ram, 3.4 e i + T i t i } + u ram. 3.5, Problem P cp is coducted by the mobile cloud provider, differet mobile cloud providers provisio optimal resource allocatio to ru VMs for mobile device users. The obective of mobile cloud providers is to maximize the reveue ad miimize electrical cost ecost uder the costraits of electrical expediture ad physical resource capacity. EB ecost represets the surplus of cloud provider which is obtaied by electrical expediture subtractig actual cost for ruig VMs. The mobile device user pays the cloud dataceter provider for available cloud resources to ru VMs. Mobile device user i submits the paymet u, uram by VMs. B i u to cloud dataceter provider for CPU ad memory required + u ram represets the surpluses of mobile device user s budget. Problem P md is coducted by mobile device users; the mobile device user gives the uique optimal paymet to cloud dataceter provider uder the deadlie costrait to maximize
96 C. Li, L. Li the mobile device user s QoS satisfactio. T i t i represets the deadlie subtractig actual processig time. E i N =1 ei is remaiig eergy of mobile device users. So, the obective of problem P md is to save the eergy ad the cost, ad complete task for mobile device user as soo as possible. 3.2. Solutio for Mobile Cloud Optimizatio Problem I mobile device user optimizatio problem, the mobile device user gives the uique optimal paymet to the cloud dataceter provider uder the deadlie, budget ad eergy costrait to maximize the mobile device user s satisfactio. Mobile device user optimizatio problem is writte as follows. MaxP md = Max s.t. T i t i, { B i B i u u + u ram + + u ram. E i N =1 e i + T i t i }, u = [u 1,...,u ] represets all paymets of mobile device users for cloud dataceter provider s CPU, u ram = [u ram 1,...,u ram ] represets all paymets of mobile device users for cloud dataceter provider s memory. Let f i = u +u ram, f i is the total paymet of the ith mobile device user. N mobile device users compete for cloud dataceter provider s resources with fiite capacity. The cloud dataceter resource is provisioed usig a market mechaism, where the cloud dataceter resource allocatio depeds o the relative paymets of the mobile device users. sq i is the storage requiremet of ith mobile device user s th ob, ad cq i is the processor requiremet of ith mobile device user s th ob. qi = sq i + cq i, q i is total service requiremet of ith mobile device user s th ob. Let p deote the price of CPU of cloud dataceter resource, p ram deote the price of memory of the cloud dataceter resource. p = p 1,p 2,...,p deote the set of CPU prices of cloud dataceter providers, p ram = p1 ram,p2 ram,...,p ram deote the set of memory prices of cloud dataceter providers. The ith mobile device user receives the cloud resources proportioal to the paymet. Let v, v ram be the fractio of CPU ad memory provisioed to mobile device user i by cloud dataceter provider. v ram = qi uram p ram, v = qi u p. The problem of mobile device user ca be reformulated as { MaxP md = Max B i u + u ram + T i + E i N =1 q i p ram er u ram + q i p u }, q i p ram u ram + q i p u
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 97 s.t. B i u + u ram, Ti t i. 3.6 We take derivative ad secod derivative of P md with respect to u ram : P md P md uram q u ram i pram = u ram 3 er q i pram u ram 3 < 0 is egative. The extreme poit is the uique value miimizig the mobile device user s paymet uder the deadlie ad the budget. The Lagragia for the mobile device user s utility is L md u ram,u. L md = E i N =1 + T i + B i q i p ram er u ram q i p ram u u ram + q i p u + q i p u + u ram + η B i u + u ram + ψ T i q i p ram u ram + q i p u, 3.7 where η, ψ is the Lagragia costat. From Karush Kuh Tucker theorem we kow that the optimal solutio is give L md u ram,u / u = 0 for η, ψ > 0. L md u = q i p η + ψ Let L md u ram,u / u u = u 2 1 + q i p u 2 = 0 to obtai. q i p er u 2 1 + er + ψq i p 1/2 1 + η. 3.8 Usig this result i the costrait equatio, we ca determie θ = 1+er+ψ 1+η θ 1/2 = T i N=1 q i p. 1/2 as
98 C. Li, L. Li We obtai u u = q i p N=1 q p i 1/2 1/2. 3.9 T i It meas that mobile device user i wats to pay u to cloud dataceter resource for CPU to ru VMs uder the deadlie ad the budget. Usig the similar method, we ca solve optimal paymet u ram of cloud dataceter provider. Let L md u ram,u / uram = 0 to obtai u ram = q i p ram N=1 q pram 1/2 i 1/2 C ram. 3.10 T i It meas that mobile device user i pay u ram to cloud dataceter resource for memory to ru VMs uder the deadlie ad the budget. A mobile cloud provider ru multiple VMs to execute the obs for mobile device users, the cloud dataceter providers pay for the electrical cost depedig o the electricity price pe. The cloud dataceter provider optimizatio aims to maximize the reveue for provisioig VMs to mobile device users ad miimize the electrical cost. The reveue optimizatio of cloud dataceter provider is to maximize the beefit fuctio P cp without exceedig the electrical expediture of cloud provider ad physical resource capacity MaxP cp = Max EB ecost + u s.t. ecost EB, i v, logv + u ram i logv ram v ram. For the mobile cloud provider s optimizatio problem, the mobile cloud provider aims to maximize their reveue ad miimize the electrical cost for provisioig VMs to mobile device user. The reveue of cloud dataceter provider is affected by the paymets of mobile device users ad the cost of the electricity. The cloud dataceter provider reveue icreases whe provisioed resources for ruig VMs icrease, paymets icrease, ad the electricity cost decreases. u logv + u ram logv ram presets the reveue obtaied by cloud provider from mobile device users. The obective of mobile cloud dataceter providers is to maximize the reveue ad miimize electrical cost ecost. EB ecost represets the electrical expediture subtractig actual cost for ruig VMs. Mobile device user adaptively adusts cloud resource demad based o the cloud dataceter s resource supply coditios, while the mobile cloud provider adaptively provisios cloud resource for the mobile device user. The iteractio betwee mobile device user demad ad the supply of cloud dataceter is cotrolled through price sets p, p ram, which are charged from mobile device users by cloud dataceter. For the cloud dataceter provider s
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 99 optimizatio problem, differet mobile cloud providers compute optimal resource allocatio for maximizig the beefit uder the limit of eergy expediture N ecost = pe ed i, 3.11 i=1 pe deote electricity price. The eergy cosumptio rate of cloud provider for hostig VMs to mobile device users is deoted as ec. The eergy cost of mobile cloud provider for hostig VMs ca t exceed EB. The electrical usage of mobile cloud provider for providig VM to mobile device i ca be preseted as ed i = ec v ram + v. Mobile cloud provider s optimizatio ca be preseted as MaxP cp = Max EB pe ec N + u logv + u ram i=1 v logv ram + v ram We take derivative ad secod derivative with respect to v :. 3.12 P cp u v = v pe ec, P cp u v = v 2 P cp v < 0 is egative due to 0 < v. The extreme poit is the uique value for mobile cloud provider optimizatio. The Lagragia for P cp is N L cp = Max EB pe ec v + ξ C ram i + α i=1 v ram EB pe ec N + κ i=1 v + v ram + u i v logv + u ram logv ram + v ram, 3.13 where α, ξ, κ is the Lagragia costat. From Karush Kuh Tucker theorem we kow that the optimal solutio is give Lv / v = 0 v u = pe ec 1 + α. Usig this result i the costrait equatio, we ca determie γ = 1 + α as γ = u EB.
100 C. Li, L. Li We obtai v = u EB. 3.14 pe ec u It meas that cloud dataceter providers allocate optimal CPU v for hostig VM for mobile device i while maximizig its ow profit. Usig the similar method, we ca solve memory allocatio optimizatio v ram of cloud dataceter provider. v ram = uram EB pe ec u ram It meas that cloud dataceter providers allocate optimal memory v ram VM for mobile device user i. 3.15 for hostig 4. Optimizatio Algorithm for Utilizig Cloud Services for Mobile Devices The distributed optimizatio algorithm for utilizig cloud services for mobile devices Algorithm 1 is executed by mobile device users ad the cloud dataceter providers. I mobile device optimizatio problem, the mobile device user gives the uique optimal paymet to the cloud dataceter provider uder the deadlie, the budget ad eergy costrait to maximize the mobile device user s satisfactio. I mobile device user optimizatio problem, mobile cloud provider rus multiple VMs to execute the obs for mobile device users, the cloud dataceter providers pay for the electrical cost. 5. Experimets 5.1. Eviromet Descriptio We simulate a mobile cloud eviromet with a 2 dimesio area of 500 m 500 m to study mobile device s behavior. Each mobile device i the simulated eviromet has a maximal radio rage of 100 m, ad moves followig a radom walkig mobility model. The average speed of each mobile device is 5 meters per secod. Each mobile device s battery capacity is iitialized with a radom value i the rage of [700, 800], ad reduced automatically by a radom value i the rage of [0, 5] i each iteratio. The iitial price of electrical eergy for cloud dataceter is set from 1 to 100 dollars. The iitial price of VM is set from 10 to 500 dollars. There are 40 mobile devices ad 12 cloud resource provider, all of which cotribute resources to the mobile cloud eviromet. Wi-Fi iterfaces operate at a rate of 11 Mb/s. All Etheret iterfaces operate at a rate of 10 Gb/s. Eergy cosumptio is represeted
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 101 Algorithm 1 Optimizatio Algorithm for Utilizig Cloud Services for Mobile Devices OAUCS cloud dataceter provider Iput: ew price p, p ram from cloud dataceter provider Step 1: Calculates u +1 to cloud dataceter resource for eeded CPU to ru VMs for mobile device user, ad maximize the mobile device user s beefit fuctio P md u +1 Step 2: If B i u = MaxP md u,uram + u ram ; The Retur paymet u +1 to cloud dataceter resource ; Else Retur Null; Step 3: Calculates optimal paymet u ram +1 to cloud dataceter resource for eeded memory u ram +1 = MaxP md u,uram ; Step 4: If B i u + u ram The Retur u ram +1 to cloud dataceter resource ; Else Retur Null Output: u +1, u ram +1 to cloud dataceter resource. mobile device user Iput: optimal paymets u, u ram from mobile device user i Step 1: Calculates v +1 to host VM for mobile device user i, ad maximize the v +1 cloud dataceter provider s beefit fuctio P cp = MaxP cp v,v ram ; Step 2 Computes ew price of CPU p +1 = max{ε,p + η i v p }; // η > 0 is a small step size parameter, is iteratio umber Step 3: Retur CPU price p +1 to all mobile device users; Step 4: Calculates v ram+1 to host VM for mobile device user i v ram+1 = MaxP cp v ram,v ; Step 5: Computes ew price of the memory p ram+1 p ram = max{ε,p ram + η i vram }; // η > 0 is a small step size parameter, is iteratio umber Step 6: Retur memory price p ram+1 Output: price p +1, p ram+1 to all mobile device users; to mobile device users
102 C. Li, L. Li Simulatio parameter Table 2 Simulatio parameters. Value Total umber of mobile device users 40 Total umber of cloud providers 12 Mobility model Radom-walkig mobility Average speed of mobile device 5 m/s Trasmissio rage [10 m, 250 m] Total umber of odes 30 Traffic type CBR Iitial price of VM [10, 500] Deadlie [100 ms, 400 ms] Expese budget [100, 1500] Electrical eergy [0.1, 1.0] Badwidth [100 kbps, 1000 kbps] Computig power [100, 1000] RAM [100 MB, 2000 MB] Eergy price [1, 100] Job arrival rate [0.1, 0.6]!" '*+,%+'+ $-+,.-$/!D X G V " D D&! D&!F D&" D&"F D&U D&V D&F D&G 23.,,$*.4,.-+ Fig. 1. Covergece iteratios uder various ob arrival rate. as a percetage of the total eergy required to meet all ob deadlies. Jobs arrive at each cloud ode s i, i = 1, 2,..., accordig to a Poisso process with rate α. The eergy cost ca be expressed i the dollar that ca be defied as uit eergy processig cost. The iitial price of eergy is set from 10 to 500 cloud dollars. The deadlies of mobile device user are chose from 100 ms to 400 ms. The budgets of mobile device users are set from 100 to 1500 dollars. Simulatio parameters are listed i Table 2. 5.2. Covergece of the Algorithm The experimet is to test covergece of optimizatio algorithm for utilizig cloud services for mobile devices OAUCS. Covergece iteratios are tested uder varyig ob arrival rate a. I Fig. 1, whe ob arrival rate is low a = 0.1, the covergece iteratio of OAUCS is oe. Whe ob arrival rate icreases to 0.25, the covergece iteratios
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 103 Algorithm 2 MuSIC algorithm Step 1: computig the ceter of mobility lcm uk of each user u k Step 2: uses the service selectio fuctio Fid service Codidote service = Fid service Wu k,lcm uk Step 3: Util 0 = Compute Fmobile Codidote service Step 4: For i = 1 to maxiter do Step 5: Codidote service = Fid service Wu k,lcm uk Step 6: Util 1 = Compute Fmobile Codidote service Step 7: = Util 0 Util 1 Step 8: If > 0 Step 9: Util 0 = Util 1 Step 10: Ed IF Step 11: Ed For Retur Codidote service, Util 0 of OAUCS is three. Whe ob arrival rate icreases to 0.5, the covergece iteratios of OAUCS is four. Whe ob arrival rate icreases to 0.6, the covergece iteratios of OAUCS icrease to five. As the demad of cloud dataceter resource is more tha the supply, the price of the cloud resources icreases. Whe ob arrival rate icreases quickly, the cloud resource supply is ot eough to be allocated to mobile device user, the price of the cloud dataceter resource will icrease, ad some mobile device users will ot afford the cloud resources. 5.3. Compariso Experimet The ext experimets aimed at comparig our distributed optimizatio algorithm for utilizig cloud services for mobile devices OAUCS with mobility-aware optimal service allocatio i mobile cloud computig MuSIC proposed by Rahimi et al. 2013. Rahimi et al. 2013 develop MuSIC Mobility-Aware Service Allocatio o Cloud, a efficiet heuristic for tiered-cloud resource allocatio which takes ito cosideratio user mobility iformatio. The MuSIC algorithm is a greedy heuristic that geerates a ear-optimal solutio to the tiered cloud resource allocatio problem usig a simulated aealig-based approach. MuSIC uses simulated aealig as the core approach i selectig ad refiig service selectio; custom policies have bee desiged to make it efficiet for the 2-tiered cloud architecture with mobile applicatios. Give a cloud service set, MuSIC starts by computig the ceter of mobility of each user by LTWs locatio-time workflows. The geeral goal is to select services ear that locatio. MuSIC the uses the service selectio fuctio that returs the list of services ear the user ceter of mobility, which ca realize the LTW ad satisfy the required costraits. The pseudo code for the MuSIC algorithm is show i Algorithm 2. To evaluate the performace of our optimizatio algorithm for utilizig cloud services for mobile devices OAUCS agaist mobility-aware optimal service allocatio i mobile
104 C. Li, L. Li!++ ]Z[X# ^4#X %J%<4$=1. @4<<%@@ 28$=1T '+ S+ Y+ M+ +!++ M++ MH+ "++ "H+ Y++ \%8/9=.% B;@C Fig. 2. Executio success ratio uder various deadlie.!++ ]Z[X# ^4#X %.%2?D <1.@4;&$=1. 28$=1T '+ S+ Y+ M+ +!++ M++ MH+ "++ "H+ Y++ Fig. 3. Eergy cosumptio ratio uder various deadlie. cloud computig MuSIC Rahimi et al., 2013, we adopt the metrics: eergy cosumptio ratio, executio success ratio, resource allocatio efficiecy ad cost. The experimets study the impacts of deadlie, mobility speeds, ad the ode umber o performace metrics. How the deadlie effects o eergy cosumptio ratio, executio success ratio, resource allocatio efficiecy ad cost were illustrated i Figs. 2 5 respectively. Figure 2 is to show the effect of the deadlie o executio success ratio. Whe the deadlie is low, the executio success ratios of MuSIC ad OAUCS are low. Whe icreasig deadlie, executio success ratios of two algorithms become higher. Because uder low deadlie, some obs of mobile device user ca t be completed o time. Whe deadlie is 200 T = 200, executio success ratio of MuSIC falls to 60%, executio success ratio of OAUCS falls to 73%. Figure 3 shows the eergy cosumptio ratio varies uder differet deadlies. From the results i Fig. 9, whe the deadlie is low, there is itesive demad for the cloud resources i short time, some mobile device user eed choose more eergy exhaustig resource to process the obs, eergy cosumptio ratio is high. However, whe the deadlie chages to higher, it is likely that mobile device user s obs ca be completed before the deadlie, so mobile device user cosiders usig the eergy savig resources to complete
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 105!++ ]Z[X# ^4#X %J%<4$=1. @4<<%@@ 28$=1T '+ S+ Y+ M+ +!++ M++ MH+ "++ "H+ Y++ \%8/9=.% B;@C Fig. 4. Allocatio efficiecy uder various deadlie.!++ ]Z[X# ^4#X %.%2?D <1.@4;&$=1. 28$=1T '+ S+ Y+ M+ +!++ M++ MH+ "++ "H+ Y++ Fig. 5. Cost Vs deadlie. obs to maximize the beefit, eergy cosumptio ratio is low. Whe the deadlie is 250, eergy cosumptio ratio of MuSIC is 67%; eergy cosumptio ratio of OAUCS is 73%. Compared with MuSIC, OAUCS cosume more eergy. For the resource allocatio efficiecy uder differet deadlie costraits, from the results i Fig. 4, whe icreasig the deadlies, the impact o the resource allocatio efficiecy is obvious. A larger deadlie values brigs out higher resource allocatio efficiecy. Whe the deadlie is 400, the allocatio efficiecy of OAUCS is 20% higher tha MuSIC. Uder the same deadlie, OAUCS has higher resource allocatio efficiecy tha MuSIC. Figure 5 represets the impact of differet deadlie costrait o the retig cost. Whe the deadlies are small, OAUCS speds more paymet for obtaiig cloud resources to complete obs, because the mobile cloud user eed buy expesive cloud service from cloud dataceter provider. Whe the deadlie is 400, the cost of OAUCS is 27% less tha the deadlie is 200. Whe icreasig deadlie by T = 400, the cost of MuSIC is as much as 39% more tha OAUCS. Whe T = 400, the cost of MuSIC decreases to early 34% compared with T = 100. I Figs. 6 7, we study the impact of the differet average speeds of mobile cloud users o allocatio efficiecy ad executio success ratio. Figures 8 ad 9 are to measure the effect of differet cloud resource odes o executio success ratio ad allocatio efficiecy
106 C. Li, L. Li +N + 87 ;/6 0 378 8+33 5,;/6! #$' #$& #$" #$% # <=>?@ A7@B?! '!#!%!!' %# 26M/./;: 3L++- 12W34 Fig. 6. Executio success ratio Vs. the mobility speeds.,..68,;/60 +99/8/+08: +! #$' #$& #$" #$% # <=>?@ A7@B?! '!#!%!!' %# 26M/./;: 3L++- 12W34 Fig. 7. Allocatio efficiecy Vs. the mobility speeds. respectively. Figure 6 shows how the executio success ratio is affected by mobility speed. The executio success ratio of OAUCS ad MuSIC decreases whe the mobility speed icreases. Whe s = 20, executio success ratio of OAUCS is as much as 35% lower tha that by s = 1. Uder the same mobility speed s = 20, OAUCS has 17% less executio success ratio tha MuSIC. This is because MuSIC is the tiered-cloud resource allocatio which takes ito cosideratio user mobility iformatio. OAUCS do ot take ito accout the movemet characteristics of the mobile device. Figure 7 shows the effect of mobility speed o the allocatio efficiecy. Whe the mobility speed icreases to 15, the allocatio efficiecy of OAUCS is as much as 24% less tha that with s = 1. The allocatio efficiecy is larger whe the mobility speed is smaller. Whe the mobility speed icreases, some mobile cloud users requiremets ca t be processed o time. The allocatio efficiecy of OAUCS decreases slowly tha MuSIC whe the mobility speed icreases. Whe the mobility speed is 20, the allocatio efficiecy of MuSIC decreases to 51%, the allocatio efficiecy of OAUCS decreases to 64%. Figure 8 shows whe the umber of cloud resource ode icreases to 10 N = 10, executio success ratio of OAUCS is as much as 21% less tha that with N = 2. The executio success ratio is larger whe the umber of cloud resource ode is smaller. The executio success ratio of OAUCS decreases slowly tha MuSIC whe the umber of cloud resource ode icreases. Whe the umber of cloud resource ode is 12, the executio success ratio of MuSIC decreases to 65%, the executio success ratio of OAUCS decreases to 73%. Cosiderig allocatio efficiecy, as show
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 107! <=>?@ A7@B? 5+36758+ +99/8/+08: #$' #$& #$" #$% #!## %## %# ## # "## Fig. 8. Executio success ratio vs. umber of cloud resource odes. 863; 1-6..,534 +5 <=>?@ A7@B? &## ## "## ## %##!## #!## %## %# ## # "## Fig. 9. Allocatio efficiecy vs. umber of cloud resource odes. i Fig. 9, whe cloud resource ode icreases, allocatio efficiecy deteriorates. Whe the umber of cloud resource odes is 10, the allocatio efficiecy of OAUCS is as 31% less tha N = 2. Whe the umber of cloud resource ode is 12, allocatio efficiecy of OAUCS decreases to 69%, resource allocatio efficiecy of MuSIC decreases to 51%. 6. Coclusios This paper presets a optimizatio approach for utilizig cloud services for mobile cliet i mobile cloud. Mobile cloud service provisioig optimizatio cosiders the beefit of both mobile device users ad cloud dataceters. The paper proposes a distributed optimizatio algorithm for utilizig cloud services for mobile devices. Firstly, the experimet is to test covergece of the proposed algorithm. Secodly, the experimets aim to compare proposed optimizatio algorithm for utilizig cloud services for mobile devices with mobility-aware optimal service allocatio i mobile cloud MuSIC. The experimets study the impacts of ob arrival rate, deadlie, mobility speeds, ad the ode umber o eergy cosumptio ratio, executio success ratio, resource allocatio efficiecy ad cost. The experimet shows that our algorithm outperforms MuSIC i terms of some performace metrics such as allocatio efficiecy.
108 C. Li, L. Li Ackowledgemets. The authors thak the editors ad the aoymous reviewers for their helpful commets ad suggestios. The work was supported by the Natioal Natural Sciece Foudatio NSF uder grats Nos. 61472294, 61171075, Key Natural Sciece Foudatio of Hubei Provice No. 2014CFA050, Applied Basic Research Proect of WuHa, Natioal Key Basic Research Program of Chia 973 Program uder Grat No.2011CB302601, Program for the High-ed Talets of Hubei Provice, ad Ope Fud of the State Key Laboratory of Software Developmet EvirometSKLSDE. Ay opiios, fidigs, ad coclusios are those of the authors ad do ot ecessarily reflect the views of the above agecies. Refereces Abolfazli, S., Saaei, Z., Shiraz, M., Gai, A. 2012. MOMCC: market-orieted architecture for mobile cloud computig based o service orieted architecture. I: 1st IEEE Iteratioal Coferece o Commuicatios i Chia Workshops ICCC. IEEE Press, New York, pp. 8 13. Barbarossa, S., Sardellitti, S., Di Lorezo, P. 2013. Joit allocatio of computatio ad commuicatio resources i multiuser mobile cloud computig. I: Sigal Processig Advaces i Wireless Commuicatios SPAWC, 2013 IEEE 14th Workshop o. IEEE Press, New York, pp 26 30. Barbera, M.V., Kosta. S., Mei, A., Stefa, J. 2012. To offload or ot to offload? The badwidth ad eergy costs of mobile cloud computig. I: INFOCOM, 2013 Proceedigs IEEE. IEEE Press, New York, pp. 1285 1293. Bessis, N., Sotiriadis, S., Xhafa, F., Asimakopoulou, E. 2013. Cloud schedulig optimizatio: a reactive model to eable dyamic deploymet of virtual machies istatiatios. Iformatica, 243, 357 380. Choi, J., Choi, C., Yim, K., Kim, J., Kim, P. 2013. Itelliget recofigurable method of cloud computig resources for multimedia data delivery. Iformatica, 243, 381 394. Dih, H.T., Lee, C., Niyato, D., Wag, P. 2013. A survey of mobile cloud computig: architecture, applicatios, ad approaches. Wireless Commuicatios ad Mobile Computig, 1318, 1587 1611. Ferber, M., Rauber, T. 2012. Mobile cloud computig i 3G cellular etworks usig pipelied tasks. I: ESOCC 12 Proceedigs of the First Europea Coferece o Service-Orieted ad Cloud Computig, pp. 192 199. Ge, Y., Zhag, Y., Qiu, Q., Lu, Y.-H. 2012. A game theoretic resource allocatio for overall eergy miimizatio i mobile cloud computig system. I: Proceedigs of the 2012 ACM/IEEE Iteratioal Symposium o Low Power Electroics ad Desig. ACM, New York, pp. 279 284. Gkatzikis, L., Koutsopoulos, I. 2013. Migrate or ot? Exploitig dyamic task migratio i mobile cloud computig systems. IEEE Wireless Commuicatios Magazie, 203, 24 32. Hussai, F.K., Hussai, O.K. 2011. Towards multi-criteria cloud service selectio. I: 2011 Fifth Iteratioal Coferece o Iovative Mobile ad Iteret Services i Ubiquitous Computig IMIS. IEEE Press, New York, pp. 44 48. Lee, W., Lee, S.K., Yoo, S., Kim, H. 2013. A collaborative framework of eablig device participatio i mobile cloud computig. I: Mobile ad Ubiquitous Systems: Computig, Networkig, ad Services. Spriger, Berli, pp. 37 49. Li, C., Li, L. 2012. Optimal resource provisioig for cloud computig eviromet. Joural of Supercomputig, 622, 989 1022. Li, C., Li, L. 2014. Phased schedulig for resource-costraied mobile devices i mobile cloud computig. Wireless Persoal Commuicatios, 774, 2817 2837. Li, D., Cao, J., Lu, X., Che, K.C.C. 2009. Efficiet rage query processig i peer-to-peer systems. IEEE Trasactios o Kowledge ad Data Egieerig, 211, 78 91. Lu, X., Wag, H., Wag, J., Xu, J., Li, D. 2013. Iteret-based virtual computig eviromet: beyod the dataceter as a computer. Future Geeratio Computer Systems, 29, 309 322. Makris, P., Skoutas, D.N., Skiais, C. 2012. O etworkig ad computig eviromets itegratio: a ovel mobile cloud resources provisioig approach. I: 2012 Iteratioal Coferece o Telecommuicatios ad Multimedia TEMU. IEEE Press, New York, pp. 71 76.
A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices 109 Park, J.S., Yu, H.C., Lee, E.Y. 2012. Resource allocatio techiques based o availability ad movemet reliability for mobile cloud computig. I: Distributed Computig ad Iteret Techology. Spriger, Heidelberg, pp. 263 264. Rahimi, M.R., Vekatasubramaia, N., Vasilakos, A.V. 2013. MuSIC: mobility-aware optimal service allocatio i mobile cloud computig. I: 2013 IEEE Sixth Iteratioal Coferece o Cloud Computig CLOUD. IEEE Press, New York, pp. 75 82. Shiraz, M., Abolfazli, S., Saaei, Z., Gai, A. 2013. A study o virtual machie deploymet for applicatio outsourcig i mobile cloud computig. The Joural of Supercomputig, 633, 946 964. Shiraz, M., Gai, A., Khokhar, R.H., Buyya, R. 2013. A review o distributed applicatio processig frameworks i smart mobile devices for mobile cloud computig. IEEE Commuicatios Surveys & Tutorials, 153, 1294 1313. Shiraz, M., Ahmed, E., Gai, A., Ha, Q. 2014 Ivestigatio o rutime partitioig of elastic mobile applicatios for mobile cloud computig. The Joural of Supercomputig, 671, 84 103. Wag, Y., Li, X., Pedram, M. 2013. A ested two stage game-based optimizatio framework i mobile cloud computig system. I: 2013 IEEE 7th Iteratioal Symposium o Service Orieted System Egieerig SOSE, pp. 494 502. Zhag, W., We, Y., Wu, D.O. 2013. Eergy-efficiet schedulig policy for collaborative executio i mobile cloud computig. I: INFOCOM, 2013 Proceedigs IEEE. IEEE Press, New York, pp. 190 194. Zhag, Y., Niyato, D., Wag, P. 2013. A auctio mechaism for resource allocatio i mobile cloud computig systems. I: Wireless Algorithms, Systems, ad Applicatios. Spriger, Berli, pp. 76 87. C. Li received the ME i computer sciece from Wuha Trasportatio Uiversity i 2000, ad PhD degree i Computer Software ad Theory from Huazhog Uiversity of Sciece ad Techology i 2003. She ow is a professor of Computer Sciece i Wuha Uiversity of Techology. Her research iterests iclude cloud computig, grid computig, ad distributed computig. She has published over 15 papers i iteratioal ourals. L. Li received the BE degree i Commuicatio Egieerig from Harbi Istitute of Military Egieerig, Chia i 1970 ad the ME degree i Commuicatio ad Electrical Systems from Huazhog Uiversity of Sciece ad Techology, Chia i 1982. Sice 1982, he has bee with the Wuha Uiversity of Techology, Chia, where he is curretly a Professor ad PhD tutor of Computer Sciece, ad Editor i Chief of the Joural of WUT. He is Director of Iteratioal Society of High-Techology ad Paper Reviewer of IEEE INFOCOM, ICCC ad ISRSDC. His research iterests iclude high speed computer etworks, protocol egieerig ad image processig. Professor Li has published over 150 techical papers ad is the author of six books. He also was awarded the Natioal Special Prize by the Chiese Govermet i 1993.
110 C. Li, L. Li Debesų paslaugų mobiliesiems įregiiams optimizavimas Chuli LI, Layua LI Mobilioi debesų kompiutera atsirado siekiat padėti mobiliesiems įregiiams atlikti daug skaičiavimų arba duomeų reikalauačius uždaviius audoat debesų išteklius. Šiame darbe pristatome debesų paslaugų mobiliaam klietui mobiliaame debesye optimizavimo būdą, kuris įvertią audą mobiliųų įregiių audotoams ir debesų duomeų cetrams. Mobiliųų debesų paslaugų optimizavimas atliekamas įvertiat termio, biudžeto ir eergos išlaidų apriboimus. Šiame straipsye siūlome paskirstytą debesų paslaugų mobiliesiems įregiiams optimizavimo algoritmą. Eksperimetu ištyrėme pasiūlyto algoritmo kovergavimą ir atlikome palygiimą su sususiais darbais. Eksperimeto rezultatai rodo, kad pasiūlytas algoritmas praoksta kitus kai kuriais rodikliais, pavyzdžiui, paskirstymo efektyvumą.