A Swarm Intelligence Based Model for Mobile Cloud Computing

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Author(s)

Ahmed S. Salama 1,*

1. Computer and Information Systems Department, Sadat Academy for Management Sciences, Cairo, Egypt

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2015.02.04

Received: 21 Apr. 2014 / Revised: 9 Aug. 2014 / Accepted: 23 Sep. 2014 / Published: 8 Jan. 2015

Index Terms

Mobile Cloud Computing, Swarm Intelligence, Parallel Particle Swarm Optimization (PPSO), E-Commerce

Abstract

Mobile Computing (MC) provides multi services and a lot of advantages for millions of users across the world over the internet. Millions of business customers have leveraged cloud computing services through mobile devices to get what is called Mobile Cloud Computing (MCC). MCC aims at using cloud computing techniques for storage and processing of data on mobile devices, thereby reducing their limitations. This paper proposes architecture for a Swarm Intelligence Based Mobile Cloud Computing Model (SIBMCCM). A model that uses a proposed Parallel Particle Swarm Optimization (PPSO) algorithm to enhance the access time for the mobile cloud computing services which support different E Commerce models and to better secure the communication through the mobile cloud and the mobile commerce transactions.

Cite This Paper

Ahmed S. Salama, "A Swarm Intelligence Based Model for Mobile Cloud Computing", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.2, pp.28-34, 2015. DOI:10.5815/ijitcs.2015.02.04

Reference

[1]H. Jacson Christensen, "Using RESTful web-services and cloud computing to create next generation mobile applications", Proceedings of the 24th ACM SIGPLAN conference companion on Object oriented programming systems languages and applications (OOPSLA), 2009, 627-634.

[2]L. Liu, R. Moulic, and D. Shea, “Cloud Service Portal for Mobile Device Management”, Proceedings of IEEE 7th International Conference on e-Business Engineering (ICEBE), 2011, 474.

[3]AEPONA, “Mobile Cloud Computing Solution Brief”, White Paper, (2010).

[4]M. Ali, “Green Cloud on the Horizon”, Proceedings of the 1st International Conference on Cloud Computing (CloudCom), 2009, 451- 459.

[5]S. Chetan, K. Gautam, "Cloud Computing for Mobile World", Technical Report, 2010.

[6]C. Sun, "Research of E-Commerce Based on Cloud Computing", Advances in Computer Science and Information Engineering, Vol. 169, 2012, 15-20.

[7]S. Juncai, and Shao, "Based on Cloud Computing E-commerce Models and Its Security", International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 1, No. 2, 2011, 175-180.

[8]A. Kundu, and Ji. Chunlin, "Swarm Behavior of Intelligent Cloud", arXiv preprint arXiv:1203.1395, 2012.‏

[9]E. Ridge, D. Kudenko, D. Kazakov, E. Curry, “Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments,” in Self-Organization and Autonomic Informatics (I), Vol. 135, 2005, pp. 35-49.

[10]K. Carolin, K. Johannes, B. Freimut, “Swarm Intelligence for Analyzing Opinions in Online Communities,” Proceedings of the 43 rd Hawaii International Conference on System Sciences, 2010, pp. 1-9. 

[11]S. Pakin, “The Design and Implementation of a Domain-Specific Language for Network Performance Testing,” IEEE Transactions on Parallel and Distributed Systems, Vol. 18, No.10, 2007, pp. 1436-1449.

[12]E. Hart, Thomas, E. Paul McKenney, and D. Angela Brown, “Making lockless synchronization fast: Performance implications of memory reclamation,” In 20th IEEE International Parallel and Distributed Processing Symposium , IPDPS 2006, 2006.

[13]M. Herlihy, “The transactional manifesto: software engineering and non-blocking synchronization,” In PLDI ’05: Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation, New York, NY, USA, 2005, pp. 280-280.

[14]D. Grossman, “The transactional memory / garbage collection analogy,” In OOPSLA ’07: Proceedings of the 22 nd annual ACM SIGPLAN conference on Object oriented programming systems and applications, New York, NY, USA, 2007, pp. 695-706.

[15]B. Gamsa, Orran Krieger, J. Appavoo, and M. Stumm, “Tornado: Maximizing locality and concurrency in a shared memory multiprocessor operating system”, In Proceedings of the 3rd Symposium on Operating System Design and Implementation , New Orleans, LA, Vol. 99, 1999, pp. 87-100. 

[16]B. Urgaonkar, P. Shenoy, “Sharc: Managing CPU and Network Bandwidth in Shared Clusters,” IEEE Transactions on Parallel and Distributed Systems, Vol. 15, No. 1, 2004, pp. 2-17. 

[17]M. Herlihy, “A methodology for implementing highly concurrent data structures,” In Proceedings of the 2nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming , 1990, pp. 745-770. 

[18]M. D. Hill, M. R. Marty, "Amdahl's Law in the Multicore Era," IEEE Computer Society, July 2008, pp. 33-38.