Large Scale Cloudlets Deployment for Efficient Mobile Cloud Computing

70

JOURNAL OF NETWORKS, VOL. 10, NO. 1, JANUARY 2015

Large Scale Cloudlets Deployment for Efficient Mobile Cloud Computing Lo'ai Tawalbeh 1, 2, Yaser Jararweh 1, Fadi ababneh 1, and Fahd Dosari 2 1. Faculty of Computer and Information Technology, Jordan University of Science and Technology, Irbid, Jordan 2. Computer Engineering Department, Umm-Alqura University, Makka, Saudi Arabia Email: [email protected], [email protected], [email protected], and [email protected]

Abstract—Interest in using Mobile Cloud Computing (MCC) for compute intensive mobile devices jobs such as multimedia applications has been growing. In fact, many new research efforts were invested to maximize the mobile intensive jobs offloading to the cloud within the cloud system coverage area. However, a large scale MCC deployment challenges and limitations are not considered. In this paper, a large scale Cloudlet-based MCC system deployment is introduced, aiming at reducing the power consumption and the network delay of multimedia applications while using MCC. The proposed deployment offers a large coverage area that can be used by mobile devices while they are moving from one location to another to reduce broadband communication needs. Practical experimental results and simulated results using show that using the proposed model reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device requests a job to be performed remotely while satisfying the high quality of service requirements of mobile users. Index Terms—Mobile Cloud Computing; Cloudlet; Network Performance; Wireless Communication; Deployment

I.

INTRODUCTION

Mobile devices such as smart phones and tablets have become an essential part of our lives, because of their powerful capabilities. Users depend on their mobile devices to make calls, create and edit documents, perform image processing, access the online social networks websites (Facebook, twitter, etc.), organize meetings and make video and audio calls. On the other hand, the current proliferation of Cloud Computing (CC) paradigm makes a big evolution in Information Technology (IT). The concept of CC relies on a network-based resource sharing to increase resource availability and to reduce the economical and management costs. The cloud is simply a collection of high performance servers with a huge amount of storage resources connected together and accessible through the Internet. The cloud resources are provided to the users as a service in pay as you use service model. In spite of the benefits provided by the mobile smart phones, and the way they make the life easier; they have many weaknesses, such as: limited battery life-time, limited processing capabilities and limited storage capacity. It is very important to take into account these

© 2015 ACADEMY PUBLISHER doi:10.4304/jnw.10.1.70-76

limitations, since they are hindering mobile users from doing their daily tasks in an efficient way. One solution to overcome these limitations is to integrate Cloud Computing technology with mobile devices to produce what is called Mobile Cloud Computing (MCC). MCC has been introduced as a feasible solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This reduces the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud, suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. On the other hand, multimedia applications are the most common applications in today’s mobile devices; such applications require high computing resources. In MCC, the processing and storage of intensive jobs are transferred to the resources rich cloud system to take place there. In case of jobs that need high processing and computing capabilities, these jobs are migrated to the cloud where intensive processing can be done, and thus the final result is returned back to the mobile device. With this technique, CC resolves both problems: limited processing capabilities and limited power of the mobile phones. On the other hand, if there are files, videos and images with a very large size, they will be transferred to the storage inside the cloud; and whenever the mobile user needs any of them, one can request it from the cloud. With this technique CC resolves the problem of limited storage capacity of the mobile phones. MCC has recently become one of the most important and hottest research topics; because it integrates the new smart phones with the cloud computing technologies. In this paper, we introduced a practical study of newly emerged architecture of MCC with a closer cloud facility to the users using Cloudlet based cloud system. The unprecedented explosion of today mobile applications like high quality demanding multimedia applications are the force behind introducing efficient communication techniques between mobile devices and the computing resources. Multimedia applications are the most common applications in today mobile devices; such applications require a high computing resource in both


the processing and the communication. MCC is the best candidate to overcome the challenges of multimedia applications. This paper is organized as the following: section 2 includes description of the cloudlet concept with details. Section 3 contains a literature review for MCC concept. Section 4 introduces our proposed model architecture in which Section 4.1 deals with mobile movement scenarios and Section 4.2 explains the management of the mobilecloudlet-cloud connection. The implementation of the proposed model is introduced in section 5. Also, this section shows the experimental results and illustrates the benefits of using the new model. Finally, we conclude the paper in section 6. II.

CLOUD COMPUTING, MOBILE CLOUD COMPUTING AND CLOUDLET

Cloud computing is a new computing paradigm that is continuously evolving and spreading. It is empowered by hardware virtualization technology, parallel computing, distributed computing and web services. Cloud computing presents a huge revolution in the Information and Communication Technology [12, 23]. Cloud computing can be defined as “a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. network, servers, storage, applications and services) that can rapidly be provisioned and released with minimal management effort or service provider interaction” [13]. There are several examples for emerging Cloud computing infrastructures/ platforms such as Microsoft Azure [13], Amazon EC2, Google App Engine, and Aneka [14]. CC builds on a wide range of different computing technologies and disciplines. It involves highperformance computing, distributed systems, virtualization, storage, networking, security, management and automation, service-oriented architecture (SOA), business process management (BPM), service-level agreement (SLA), quality of service (QoS), etc. This complexity presents a major challenge when studying CC [16]. Furthermore, CC helps companies improve the IT services, develop applications to achieve unlimited scalability and automaticity on demand services of the IT infrastructure, and increase their revenues [14] [24]. CC services include: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Clients of CC might be users in other Clouds, organizations, enterprises or single users. An emerging technology of integrating Mobiles with CC creates a new programming paradigm called Mobile Cloud Computing (MCC). MCC promises to overcome mobile limitations as the processing and storage for intensive jobs are transferred to the cloud to take place there, thus the final results are returned back to the mobile device. MCC can be considered as "a kind of cloud computing with assistance computing for mobility such as location-awareness, computing capability, and data backup" [15]. As in figure 1, mobile devices in MCC mostly use the 3G/LTE to connect to the cloud and rarely

© 2015 ACADEMY PUBLISHER

71

use the Wi-Fi because it is not always available. Table 1 compares these two technologies.

Figure 1. Traditional MCC Architecrure

From Table 1, we can notice that using Wi-Fi connection is preferable over the 3G/LTE connections. Cloudlet [3] is a trusted computer or a cluster of computers with high capabilities, connected to the Internet. It is installed along with Wi-Fi APs to allow mobile devices to access it, and in some cases both of the cloudlet and AP are integrated in one entity. In addition, the mobile devices use Wi-Fi to connect to the nearby cloudlet. Through using the low latency, high Bandwidth and one-hop wireless access links, the access to the cloudlets mobile device gets a real-time interactive response. TABLE I.

LTE AND WIFI COMPARISON

Power consumption [1][2] Connection Speed[3] Latency [3]

3G/LTE Higher 2Mbps Higher

WiFi Lower 400Mbps Lower

In the cloudlet-based mobile computing, mobile devices send jobs to the cloudlet to perform the required processing and return the final result back, this reduces the transmission delay, also reduces the power consumption of the mobile device. So it makes a great evolution in MCC. While Mobile devices are used in a daily bases, there contributions to research efforts still limited for many reasons. Moreover, accelerating research in different fields are also driven by enhancing the computing resources used [28] [30]. Security issues for both mobile devices and cloud could be among the major obstacles in the growth of this field. Advance security techniques most be used to handle security concerns in the MCC [29] [31]. III.

LITERATURE REVIEW

The work in [4] describes how the MCC is developed from both the CCC and the mobile computing. The work also describes the MCC scope, developments and current research area challenges. This paper proposes MobiCloud system developed at Arizona State University to simplify the study and the analysis of the MCC. Authors of [8] give a survey of MCC’s definition, advantages, architecture and applications (Mobile Commerce, Mobile Learning, Mobile Healthcare, and Mobile Gaming). They also describe MCC issues (Low Bandwidth, Availability, Heterogeneity, Computing Offloading, Context-aware

72

mobile cloud services, Security and Enhancing the Efficiency of Data Access) and list the existing solutions. At the end, they present the future works in this filed. The impact of using Cloudlet with respect to Cloud mobile computing in interactive applications (file editing, video streaming and collaborative chatting) is analyzed in [5]. The work in [5] also compares the two models in terms of system throughput and data transfer delay. The paper results show that the use of cloudlet-based model has outperformed the cloud-based model in most cases. A modelling and simulation model for MCC are presented in [16]. In [6] the authors introduces a new architecture called MOCHA for face recognition applications. The purpose of this architecture is to reduce the response time during the face recognition process. MOCHA integrates Mobile device, cloudlet and cloud servers. Admission control and resource allocation problems for the running mobile applications in the cloudlet are discussed in [7]. To solve these problems; authors formulate them as a semi-Markov decision process (SMDP). The newly proposed model [7] paper provides a QoS for different classes of mobile users. In [3] the technical obstacles of using cloudlet in mobile computing have been discussed. A new architecture has been proposed to deal with these obstacles. This new architecture manages the sessions opened by mobile users inside the cloudlet. The management is based on VM instantiation for each mobile user. The key performance metrics of using VM is to manage jobs execution inside the cloudlets discussed in [10]. These metrics include: overhead of VM life cycle when deploying it in the execution of cloudlet, cloudlet allocation to VM and scheduling of VMs. The authors used the CloudSim as a platform environment and concluded that it is so important to efficiently deploy and manage VMs in CC to reduce the amount of execution time because of the previously mentioned performance metrics. [11] Presents a prototype implementation of cloudlet architecture and shows the advantages of this architecture for the real-time applications. The proposed architecture in [11] is a fine grained cloudlet to manage the running application on the component model, where the cloudlet can be chosen dynamically from any resource rich device inside the LAN and not as the traditional concept where the cloudlet is fixed near to the wireless access points. In [9] authors analyzed the critical factors that affect the power consumption of mobile clients when using CC, they also provided an example on how to save mobile client power. To define the balance between using local mobile computing and remote cloud computing, they presented their own measurements of the main characteristics of modern mobile devices. The authors in [20] present a survey of the intended usages of MCC. They discuss three existing architectures of MCC, which are the traditional centralized cloud, cloudlet and peer-based ad hoc mobile cloud, and provide their visions for the future MCC architecture. Also they discuss the main contributions of using Clouds in mobile computing a) computation offloading and b) capability extending (computation, networking, and storage). The



work in [17] [18] [19] [21] [22] focuses mainly on the feasibility of using mobile cloud computing for multimedia applications and data collection in large scale networks. The authors in [25] provide a Scalable Cloudletbased Mobile Computing Model. We extened this model to build a large scal deplyment of cloudlets using the CloudExp tool [16] [26]. CloudExp tool is an extended cloud computing simulation tool for TeachCloud [16]. The authors in [27]

show how to use large number of cloudlet to collect data from WBANs. Accelerating research in different fields are also driven by enhancing the computing resources used [28]. IV.

PROPOSED ARCHITECTURE

We propose a new MCC cloudlet-based model. The new model is composed of a set of distributed and wellconnected cloudlets within one location where most mobiles use cloud services. In addition, all of these cloudlets are connected to the Enterprise remote cloud. Figure 2 shows the basic elements of our proposed model. The mobile device directly communicates with the cloudlet which is connected to the Enterprise cloud. In some cases, the mobile device will need to connect to the enterprise cloud, even if the cloudlets are available. In one case, the device need to access a file stored in the Enterprise Cloud. In another case, the mobile device will need to access services that are not available in the Cloudlet.

Figure 2. Elements of the Proposed MCC Cloudlet based Model

A. Mobility Scenarios Figure 3 depicts the possible mobility scenarios for a mobile user with respect to a deployed cloudlet system. A mobile user in location A is connected to Cloudlet1 (CL1) using Wi-Fi connection while it is available in CL1 coverage area. Moving out of the coverage range of CL1 may result in moving to the coverage area of another cloudlet like moving to location B or moving to 3G\LTE coverage area like location C. CL1 and CL2 are also connected to the EC through wired connection. We basically consider the following mobility scenario: Initial location is A and stay in A: In this situation, the mobile device accesses the EC through CL1 and remains in the coverage area of this CL until the requested job is completed. Initial location is A and then moves to location B: In this situation, the mobile device accesses the EC through CL1, but before the completion of the requested job it changes its location to the coverage area of CL2 and outside the coverage area of CL1. In this case, the mobile


device uses the CL2 to perform new tasks or to complete the previous jobs, so that, if there are any data or processes remaining in the CL1, a request is sent to CL1 to send this information to CL2 in order to complete these jobs. This case is similar to moving from location B to any other location with CL coverage like moving back to A. Initial location is B and then moves to location C: In this situation, the mobile device accesses the EC through CL2, but before the completion of the requested job, it changes its location to outside the coverage area of the host CL. Unfortunately, there is no available CL at location C. In this case, the mobile device has to use the costly 3G/LTE connection to access the EC. Using this connection, mobile device can perform new tasks or complete the previous job, so that, if there are any data or processes remaining in the CL2, it will send a request to CL2 to send this information to EC in order to complete these jobs. Initial location is C and then moves to location B: In this case, the mobile user is moving from 3G/LTE coverage area to CL coverage area. The mobile user data processing can be moved to the CL after that.

73

the mobile side. In the first approach, the mobile device is not aware of the management process; as a result, everything is done by the EC. TABLE II. Parameter Current status Connection type Current services Current files Recent Cloudlets

Value Connected or disconnected Direct or Cloudlet Names of currently used services Names of currently used files IDs of the recently used cloudlets IDs of incomplete jobs along with the host cloudlet

Incomplete Jobs TABLE III. Parameter Recent Cloudlets Incomplete Jobs

V.

MOBILE DEVICE STATUS

CLOUDLET AND JOB INFORMATION Value IDs of the recently used cloudlets IDs of incomplete jobs along with the responsible cloudlet

EXPERIMANTAL RESULTS AND EVALUATION

TABLE IV.

CLOUDLET AND ENTERPRISE CLOUD RESULTS COMPARISON

File Size ( KB) 1048 5611 10220

226000

Case One CL EC One CL EC One CL Two CLs EC One CL Two CLs EC

Power (mW) 220 671 267 1000 417 513 1100 680 721 1400

Delay (s) 1.6 21.2 8.9 81 18.4 40 105 226.5 328 2286

Throughput (KB/s) 655 49.4 630.4 69.7 555.4 255.5 97.3 999.4 690.1 99

Figure 3. Mobility Scenarios

B. Management In the movement scenarios discussed above, there were situations where the mobile device leaves the coverage area of the host CL to the coverage area of a new one before completing all of the started jobs. In order to complete the jobs that were running in the host CL, the new CL must be provided with full information about the host CL in addition to the services or files the mobile devices that used them. Next we discuss two approaches to manage this information. 1) Centralized Approach In this approach, the EC is responsible for managing and tracking mobile device movement in the system. This management is done by storing the tracking information about the mobile device. These information are included in Table 2. 2) De-Centralized Approach In this approach, the mobile device itself is responsible for managing its movement in the system. The mobile devices store a movement history along with the service that is currently running and the hosting CL to provide this information to new CL as needed (as shown in Table 3). Using any of the above two approaches is dependent on the simplicity and easiness of management required by © 2015 ACADEMY PUBLISHER

In order to evaluate our new model, we built three different mobile computing scenarios. In the first one, the mobile device is connected to the enterprise cloud (EC) through 3G. In the second scenario, the mobile device is connected to the EC through one Cloudlet (CL). And in the last scenario, the mobile device is always connected to the EC, but in this scenario it is moving from one CL to another one. The latter scenario supports both user mobility and continues connectivity to the cloud provider. These two factors are very important for many applications especially the multimedia applications. All the experiments were carried out using a real testbed. All the testbed components were built from scratch. A. Practical Experimental Setup: Our testbed consists of the following components: Cloudlet servers: Two Lenovo Thinkpad Laptops (Core i5, 4 GB memory, windows 7). Access points: A BandLuxe PR30 series router, connected to internet and a TP-Link TL-MR3220 router connected to internet. Mobile Device: Samsung Galaxy Note N7000. B. Experimental Results: To test the implementation of the proposed model, we uploaded four files of sizes (1048, 5611, 10220, 22600 KB) to both the Cloudlet and the enterprise Cloud. In the

74


implementation, we are interested in the amount of power consumed during the uploading process; the amount of time needs to complete the job and the throughput of the whole process. The cost of using all scenarios is also presented. Table 4 shows Cloudlet and Enterprise cloud results comparison. Table 4 provides the total required delay to complete the upload process and the total consumed power by the mobile device respectively. From this implementation, we can notice how much the use of Wi-Fi is preferable over the use of 3G, since it consumes less power than one half of the power consumed while using the 3G connections. It also gives about 10-times the throughput with respect to 3G connection throughput. The usage of EC requires a huge time with large file sizes, while the delay is within an acceptable ranges for using the cloudlets.

C. The Simulation Environment We extend the results that we achieved in Table 4 to simulate a large scale cloudlets deployments using the CloudExp simulation tool [26]. Figure 6 shows a sample for large scale deployment of 7 cloudlets with mobile devices. Each cloudlet is forming a cell like structure based on its coverage. Mobile devices are moving from one cell to another based on the scenarios provided in figure 3. The mobile devices are assumed to be using cloudlets whenever they are within the coverage area of the cloudlets. In case the mobile device is out the coverage area of the cloudlets, it will be forced to connect directly to the enterprise cloud using the 3 G connection. The Deployment shown in figure 6 can create a coverage area of about 600*500 meter with a WiFi range for about 100 meters of each cloudlet. Number of mobile devices served within this deployment can reach up to 1000 devices. Larger deployment of 16 cloudlets can create a coverage area of a medium size university campus (i.e. 1000*1000 meter) with up 2500 users.

Figure 4. Delay required with different files size and different configurations, (EC, 1 CL and 2 CL)

Figure 6. A deployment of 7 cloudlet with mobile devices

Figure 5. Power consumption required with different files size and different configurations (EC, 1 CL and 2 CL)

Figure 4 and Figure 5 provide the total required delay to complete the upload process and the total consumed power by the mobile device respectively. From this implementation, we can notice how much the use of Wi-Fi is preferable over the use of 3G, since it consumes less power than one half of the power consumed while using the 3G connection. It also gives about 10-times of throughput with respect to 3G connection throughput. The usage of EC will require a huge time with large file sizes, while the delay is within an acceptable ranges for using the cloud lets


In our experiments, we simulated different cloudlets deployments ranging from 4 cloudlets to 16 cloudlets. All cloudlets are uniform. We consider that each cloudlet is able to handle request from 150 mobile devices simultaneously. So, in case of 16 cloudlets, we have a total of 2400 mobile devices. The mobile devices are moving using the random way point model. The message size is ranging from 10 to 100 KB and its uniform and randomly selected for each mobile device. One experiment uses only the enterprise cloud with 2400 mobile devices. This experiment will be used for the comparison with the largest deployment of the 16 cloudlets for 2400 mobile devices. D. Simulation Results Table 5 shows the simulated results of different deployments scales ranging from 0 cloudlet (i.e. Enterprise Cloud only) to 16 cloudlets. The decreasing trend of the delay and the power consumption per user with increasing the size of the cloudlet deployment is justified by the fact that the mobile devices are using the low cost WiFi wireless interface. The Enterprise cloud case will be the most costly case as the mobile devices


are using the 3G interface. Increasing the deployments size by two cloudlet will improve the power consumption and the delay by 8% to 15%. While the improvements in the power is ranging from 10% to 20 %. For the comparison purpose. The enterprise cloud experiment is using 2400 mobile devices as the 16 cloudlet deployment. The 16 cloudlets deployment will need only 6% of the power consumed by the mobile devices in the case of the enterprise cloud case and about 5.5% for the processing delay. All cloudlet deployment are outperforming the enterprise cloud case. TABLE V.

[3]

[4]

[5]

LARGE SCALE CLOUDLET DEPLOYMENT SIMULATION

[6]

RESULTS

Number of Cloudlets 4 6 8 10 12 14 16 (Enterprise Cloud) 0 Cloudlets

75

Power Consumption (mW) Per Mobile Device 294 265 251 223 198 171 157

Processing Delay (s) Per Mobile Device 45 41 38 33 31 29 24

2554

437

VI.

[7]

[8]

CONCLUSION

In this paper, we extend the integration between Cloudlet novel concept and the mobile computing for a large scale deployment of cloudlets. The extended model takes into account the mobility and the movement nature of the mobile devices and how to deal with this challenge with lowest costs possible in terms of power and processing delay. Experimental results showed that the extended model achieved the goals in reducing the power consumption of the mobile device, besides reducing the communication and the processing latency when the mobile device requests a job to remotely take place. The main contribution in this work is the scalability of the proposed model that will help in providing high quality communication for mobile device running high demanding and large scale applications e.g. Multimedia applications with minimum cost. ACKNOWLEDGMENT This work is funded by grant number (13-ELE2527-10) from the Long-Term National Science Technology and Innovation Plan (LT-NSTIP), the King Abdul-Aziz City for Science and Technology (KACST), Kingdom of Saudi Arabia. We thank the Science and Technology Unit at Umm Al-Qura University for their continued logistics support.

[9]

[10]

[11]

[12]

[13]

[14] [15]

[16]

REFERENCES [1] Raiciu, D. Niculescu, M. Bagnulo, and M. Handley. “Opportunistic mobility with multipath TCP”. In ACM Workshop on MobiArch, pages 7-12, 2011. [2] Niranjan Balasubramanian, Aruna Balasubramanian, and ArunVenkataramani. “Energy consumption in mobile phones: a measurement study and implications for network


[17]

[18]

applications”. In Proc. IMC, pages 280–293, New York, NY, USA, 2009. ACM. Mahadev Satyanarayanan; Bahl, P. ; Caceres, R. ; Davies, N., "The Case for VM-Based Cloudlets in Mobile Computing, " Pervasive Computing, IEEE, vol. 8, no. 4, pp. 14, 23, Oct. -Dec. 2009. Dijiang Huang, "Mobile Cloud Computing, " Multimedia communications technical committee, IEEE communications society, Vol. 6, No. 10, October 2011 Fesehaye, D. ; YunlongGao; Nahrstedt, K. ; Guijun Wang, "Impact of Cloudlets on Interactive Mobile Cloud Applications, " Enterprise Distributed Object Computing Conference (EDOC), 2012 IEEE 16th International, vol., no., pp. 123, 132, 10-14 Sept. 2012 Soyata, T. ; Muraleedharan, R. ; Funai, C. ; Minseok Kwon; Heinzelman, W., "Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture, " Computers and Communications (ISCC), 2012 IEEE Symposium on, vol., no., pp. 59-66, 1-4 July 2012 Dinh Thai Hoang; Niyato, D. ; Ping Wang, "Optimal admission control policy for mobile cloud computing hotspot with cloudlet, " Wireless Communications and Networking Conference (WCNC), 2012 IEEE, pp. 3145, 3149, 1-4 April 2012 Dinh, H. T., Lee, C., Niyato, D. and Wang, P. (2011), “A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. Antti P. Miettinen, Jukka K. Nurminen; “Energy efﬁciency of mobile clients in cloud computing “. HotCloud'10 proceedings of the 2nd USENIX conference on hot topics in cloud computing. Pages 4-4 Shiraz, Muhammad; Gani, Abdullah; “Mobile Cloud Computing: Critical Analysis of Application Deployment in Virtual Machines”; International Proceedings of Computer Science & Information Tech; 2012, Vol. 27, p11 Verbelen, Tim, Pieter Simoens, Filip De Turck, and Bart Dhoedt. 2012. “Cloudlets : Bringing the Cloud to the Mobile User” In 3rd ACM Workshop on Mobile Cloud Computing and Services, Proceedings, 29–35. Ghent, Belgium: Ghent University, Department of Information technology. Rindos, Andy, Mladen Vouk and Yaser Jararweh. "The Virtual Computing Lab (VCL): An Open Source Cloud Computing Solution Designed Specifically for Education and Research. " IJSSMET 5. 2 (2014): 51-63. Web. 29 Nov. 2014. P. Mell and T. Grance. "The NIST definition of cloud computing". National Institute of Standards and Technology, 2011 D. Chappell. Introducing the Azure services platform. White paper, Oct. 2008. Kim, S. ; Yonglk Yoon; Minkyo In; Kangchan Lee; Seungyun Lee, "The evolution of standardization for mobile cloud, " 2011 International Conference on ICT Convergence (ICTC). Yaser Jararweh; Zakarea Alshara; Moath Jarrah; Mazen Kharbutli; Mohammad N. Alsaleh, “TeachCloud: a cloud computing educational toolkit”. International Journal of Cloud Computing, 2013 Vol. 2, No. 2/3, pp. 237 – 257. Muhannad Quwaider, Yaser Jararweh “An Efficient Big Data Collection in Body Area Networks”, 2014 5th International Conference on Information and Communication Systems (ICICS), April, 2014. Shaoxuan Wang; Dey, S., "Adaptive Mobile Cloud Computing to Enable Rich Mobile Multimedia

76

[19]

[20]

[21]

[22]

[23]

[24]


Applications, " Multimedia, IEEE Transactions on, vol. 15, no. 4, pp. 870, 883, June 2013 Karadimce, Aleksandar, and Danco Davcev. "Adaptive Multimedia Learning Delivered in Mobile Cloud Computing Environment. " CLOUD COMPUTING 2013, The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization. 2013. Fangming Liu; Peng Shu; Hai Jin; Linjie Ding; Jie Yu; Di Niu; Bo Li, "Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications, " Wireless Communications, IEEE, vol. 20, no. 3, pp. 14, 22, June 2013 Quwaider, Muhannad; Jararweh, Yaser, "Cloudlet-based for big data collection in body area networks, " 2013 8th International Conference forInternet Technology and Secured Transactions (ICITST), , vol., no., pp. 137, 141, 912 Dec. 2013 Jararweh, Y. ; Tawalbeh, L. ; Ababneh, F. ; Dosari, F., "Resource Efficient Mobile Computing Using Cloudlet Infrastructure, " 2013 IEEE Ninth International Conference on Mobile Ad-hoc and Sensor Networks (MSN), , vol., no., pp. 373, 377, 11-13 Dec. 2013 J. Y. Shi, M. Taifi, A. Khreishah, and J. Wu, (2011) “Sustainable GPU Computing at Scale”, IEEE 14th International Conference on Computational Science and Engineering, Aug. 2011. Yaser Jararweh (2013) "CloudFlow: An innovative Management Framework for High performance Cloud Computing Systems", Conference on Scientific Computing (CSC 2013), Paphos, Cyprus, Dec 2013.


[25] Yaser Jararweh, Lo’ai Tawalbeh, Fadi Ababneh, Abdallah Khreishah, Fahd Dosari, Scalable Cloudlet-based Mobile Computing Model, Procedia Computer Science, Volume 34, 2014, Pages 434-441, ISSN 1877-0509. [26] Yaser Jararweh, Moath Jarrah, Mazen kharbutli, Zakarea Alshara, Mohammad Alsaleh, Mahmoud Al-Ayyoub, CloudExp: A Comprehensive Cloud Computing Experimental Framework, Simulation Modelling Practice and Theory, ISSN 1569-190X. [27] Muhannad Quwaider, Yaser Jararweh, Cloudlet-based Efficient Data Collection in Wireless Body Area Networks, Simulation Modelling Practice and Theory, Available online 25 July 2014, ISSN 1569-190X, [28] Yaser Jararweh, Arjun Hary, Youssif B. Al-Nashif, Salim Hariri, Ali Akoglu, Darell jenerette. "Accelerated Discovery through Integration of Kepler with Data Turbine for Ecosystem Research". AICCSA, May, 2009, Rabat, Morocco. [29] Yaser Jararweh, Loai Tawalbeh, Hala Tawalbeh, Abedalrahman Mohd, “Hardware Performance Evaluation of SHA-3 Candidate Algorithms”, Journal of information Security, 3(2): 69-76 (2012) [30] Yaser Jararweh, Salim Hariri. “Power and Performance Management of GPUs based Cluster”, International Journal of Cloud Applications and Computing, 2(4), 16-31. [31] Lo’ai Tawalbeh, Yaser Jararweh, Abedalrahman Mohd. “An Integrated Radix-4 Modular Divider/ Multiplier Hardware Architecture for Cryptographic Applications”. IAJIT, Vol. 9, No. 3, May 2012.