Data Routing Algorithm in Mobile Cloud Computing Network

DOI : 10.17577/IJERTV4IS040667

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Data Routing Algorithm in Mobile Cloud Computing Network

Ch. Srilakshmi Prasanna, M. Chenna Keshava,

Department of CSE, Department of CSE,

JNTUA College of Engineering, JNTUA College of Engineering, Pulivendula, A.P, India. Pulivendula, A.P, India

Abstract: Cloud computing technology provides assistance to companies and users to share computing resources instead of having personal devices or local servers to handle the applications. Mobile cloud computing networking [MCCN] is a novel approach. MCCN is an integration of cloud based resources and mobility. This paper gives an overview of mobile cloud network model, their challenges and technology of LTE. But sometimes it is difficult in cloud to built spontaneous network and configures its parameters. This paper gives brief view of the routing algorithm used to enhance the capacity of access network in mobile cloud services based on merging of computation and networking in heterogeneous mobile cloud networks.

Keywords: Mobile cloud computing network, Heterogeneous network, LTE [Long-Term Evolution].


Cloud computing technology provides assistance to users and companies to share computer resources on pay-as-you- use basis. Cloud technology came into existence in late 2000s.

Fig 1: Cloud computing structure

In cloud the service providers provides a variety of services such as

  1. SaaS [Software as a Service]

  2. PaaS [Platform as a Service]

  3. IaaS [Infrastructure as a Service]

In cloud the resources such as storage, computing power are not present at the users location.

Pros of cloud computing are cost, storage, access, speed, scalability.

Fig 2: Pros of cloud computing

Cons of could compute are security, privacy, internet access dependent, standardization, service level agreements, suppliers interoperability.

The popularity of mobile devices and also increased demand of mobile applications made to converged as mobile cloud computing based on user palatable. Some statistics of mobile computing are given below.

Fig 3: Statistics of mobile cloud computing

The important challenges in cloud computing are network capacity, reliability in some applications related to disasters, healthcare, interactive applications and real time media content analysis, where it is important to keep these applications working in real time streaming, using efficient resources and high QOS can be accessed anytime and from anywhere.

Fig 4: cloud computing Challenges

In MCCN network, security is considered as bottleneck. So an adaptive data routing model is used to overcome the situations when mobile devices come across high volume of traffic and disconnection situation. Why we use an adaptive data routing algorithm?

An adaptive data routing algorithms changes their routing decisions based on topology and traffic. With this solution scalability, elasticity of cloud is increased and also power reduction for mobile devices is achieved. This new routing process is known as cognitive data routing in heterogeneous mobile cloud networks (CDRHMCN).

Cognitive radio technology and algorithms are used to solve the problem of spectrum by allowing the unlicensed users (SU) to access available spectrum without affecting the activity of licensed user (PU).

Fig 6: Client /server model

Cloudlet model: A cloudlet model can be viewed as a data center whose goal is to utilize nearby resources and with one hop communication providing a low latency, high bandwidth accessing from mobile user.


The mobile cloud networking architecture is as shown in figure 5. The MCCN architecture consists of 3 models:

  1. Client/server model.

  2. Cloudlet model.

  3. Ad-hoc model.

    Fig 5: Heterogeneous mobile cloud networking architecture

    Client/Server model: In this model tasks and complex applications are offloaded from mobile device (Client) to computational infrastructure server which remains static and provides services to the mobile users.

    Fig 7: Cloudlet model

    Ad-hoc model: In Ad-hoc model mobile devices are formed a virtual cloud by sharing their resources to provide special task for other mobile.

    Fig 8: Ad-hoc model


      Challenges arise when using mobile devices in cloud are depicted in figure 9.

      Energy and Interfacing


      Security level

      Connectivity and bandwidth


      Fig 9: Challenges in mobile cloud network



As cognitive data routing algorithm provides solutions to bottleneck problems. By assuming the availability of various type of resources and alternative wireless connectivity. Heterogeneous in mobile cloud networks comes from the infrastructures, variety of hardware and technologies like Mobile devices, cloud and Wireless networks.

We propose an LTE cellular network that contains of cellular base station (e Node B) and mobile devices connected to it. LTE was designed to increase the capacity and speed of the cellular networks. In LTE download rate is100Mbps and Uplink rate is of 50Mbps. LTE supports both TDD [Time division duplexing] and FDD [Frequency division duplexing].

Fig 10: LTE

In LTE to do necessary analysis and decision of nodes has a command and control functions that is used to initiate the procedure with the following steps.

  1. Resource scanner

  2. Selecting Algorithm

  3. Decision and execution

  4. Partitioning

    Power consuming report is shown in figure.

    In this scenario by using LTE network nodes can utilize resources by switching the mobile node from cloudlet, client/server or ad-hoc models to utilize the resources and providing data routing in successful manner. By this network capacity, coverage area, network throughput increases by using CDRHMCN algorithm. Consuming of power and delay is minimized. The average throughput in the ad-hoc model with high range of QOS is shown in figure 13.





    0.10m 0.2 0.3 0.4 0.5 0.6


    Using opnet17.5 version, which supports LTE for modeling heterogeneous mobile cloud computing networking along with Visual Studio 2012 version.

    Fig13: the average throughput in the ad-hoc model.


In heterogeneous mobile cloud computing network requires adaptive data routing algorithm to overcome the critical networking challenges. By considering throughput, delay we can assess the performance of the network. Future work, there is need to overcome the security in mobile level as well as in cloud level, it is necessary to improve the storage capacity, battery life time, power consumption on MCN.


  1. B.Girod,V. Chandrasekhar andY.A. Reznik,Mobile visual search Architecture, technologies, and the emerging MPEG standard, IEEE Multimedia, vol. 18, no. 4.

  2. Anthony T.Velte, Toby J.Velte, Robert Elsenpeter, Cloud Computing A Practical Approach, TATA McGRAW HILL Edition 2010.

  3. H. Harada, H. Murakami, K. Ishizu, S. Filin, G.Miyamoto, M. Hasegawa, Y. Murata, A Software Defined Cognitive Radio System, Proceedings of the 7th International Conference on Global Telecommunications.

  4. Y. T. Larosa, J. Chen,D. Deng and H. Chao Mobile Cloud Computing Service Based on Heterogeneous Wirless and Mobile P2P Networks, Proceedings of the 7th International Conferece on Wireless Communications and Mobile Computing ( IWCMC).

  5. T. Verblen, P. Simoens, and B. Dhoedt, Cloudlet:Bringing the Cloud to the Mobile User, Proceedings of the 4th ACM Workshop on Mobile Cloud Computing and Services (MCS), pp.2936,2012.

  6. X. Wang, M. Chen, T. T. Kwon, L. Yang and V. C. N. Leung, AMES CLOUD:A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds, Proceeding of the Multimedia IEEE Transactions on (Volume :16, Issue:4) , pp.811- 820,2013.199.


CH. SriLakshmi Prasanna received her B.Tech (CSE) Degree from G. PullaReddy Engineering college, Kurnool, A.P, India in 2008 and received M. Tech (CSE) degree from JNTUA College of Engineering, Pulivendula, A.P in 2014. She has total of 1year of experience in teaching and currently working as Academic Assistant at JNTUA

College of Engineering, Pulivendula, Y.S.R. Dist., A.P, India.

M. CHENNA KESHAVA received his B.Tech (CSE) Degree from G. PullaReddy Engineering College, Kurnool, A.P, India in 2008 and pursing M. Tech (CSE) degree in JNTUA College of Engineering, Pulivendula,

A.P. He has total of 5years of experience in teaching.

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