ACO based graph partitioning algorithm for optimistic deployment of software in MCC

Mobile Cloud Computing has become very popular because it integrates mobile computing and cloud computing. This paper has presented a critical review on existing MCC techniques especially for optimistic deployment of software. The optimistic deployment mainly focuses on partitioning an application in such a way so that energy consumption will be minimized. The objective of this paper is to study and explore several graph partitioning algorithms and propose a new ACO based technique to deploy software in mobile clouds in optimistic approach. The recommended algorithm has the ability to overcome the limitations which are present in earlier techniques.

[1]  Ermyas Abebe,et al.  Adaptive application offloading using distributed abstract class graphs in mobile environments , 2012, J. Syst. Softw..

[2]  Alan Messer,et al.  Adaptive offloading inference for delivering applications in pervasive computing environments , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[3]  Marin Litoiu,et al.  Partitioning applications for hybrid and federated clouds , 2012, CASCON.

[4]  Gustavo Alonso,et al.  Dynamic Software Deployment from Clouds to Mobile Devices , 2012, Middleware.

[5]  Kun Yang,et al.  An adaptive multi-constraint partitioning algorithm for offloading in pervasive systems , 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications (PERCOM'06).

[6]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[7]  Filip De Turck,et al.  Graph partitioning algorithms for optimizing software deployment in mobile cloud computing , 2013, Future Gener. Comput. Syst..

[8]  Yong-Hyuk Kim,et al.  Genetic approaches for graph partitioning: a survey , 2011, GECCO '11.

[9]  Chen-Khong Tham,et al.  Energy-Efficient Mapping and Scheduling of Task Interaction Graphs for Code Offloading in Mobile Cloud Computing , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[10]  Shahryar Shafique Qureshi,et al.  Mobile cloud computing as future for mobile applications - Implementation methods and challenging issues , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[11]  Filip De Turck,et al.  Adaptive deployment and configuration for mobile augmented reality in the cloudlet , 2014, J. Netw. Comput. Appl..

[12]  Cheng Wang,et al.  Parametric analysis for adaptive computation offloading , 2004, PLDI '04.

[13]  Rajkumar Buyya,et al.  Application partitioning algorithms in mobile cloud computing: Taxonomy, review and future directions , 2015, J. Netw. Comput. Appl..

[14]  Mohammed Atiquzzaman,et al.  Bandwidth-adaptive partitioning for distributed execution optimization of mobile applications , 2014, J. Netw. Comput. Appl..

[15]  Sridhar Iyer,et al.  Automated refactoring of objects for application partitioning , 2005, 12th Asia-Pacific Software Engineering Conference (APSEC'05).

[16]  Luis Pedrosa,et al.  The Case for Complexity Prediction in Automatic Partitioning of Cloud-enabled Mobile Applications , 2012 .

[17]  Ermyas Abebe,et al.  A Hybrid Granularity Graph for Improving Adaptive Application Partitioning Efficacy in Mobile Computing Environments , 2011, 2011 IEEE 10th International Symposium on Network Computing and Applications.