January  2016, 1(1): 1-13. doi: 10.3934/bdia.2016.1.1

ACO-based solution for computation offloading in mobile cloud computing

1. 

College of Information System and Management, National University of Defense Technology, Changsha 410073, Hunan, China

2. 

College of Information System and Management, National University of Defense Technology, Changsha, Hunan, 410073, China, China, China, China, China

Received  July 2015 Revised  August 2015 Published  September 2015

The cloud computing has attracted growing attentions for its benefits to providing on-demand services, mobile cloud computing (MCC) enables an increasing number of applications and computational services available on mobile devices. In MCC, computation offloading is one of the most important challenges to provide remote execution of applications to the mobile devices. Here we mainly introduce the ant colony optimization (ACO) to address this challeng and propose an ACO-based solution to the computation offloading problem. The proposed method can be well implemented in practice and presents with low computing complexity.
Citation: Weidong Bao, Haoran Ji, Xiaomin Zhu, Ji Wang, Wenhua Xiao, Jianhong Wu. ACO-based solution for computation offloading in mobile cloud computing. Big Data & Information Analytics, 2016, 1 (1) : 1-13. doi: 10.3934/bdia.2016.1.1
References:
[1]

Hoang T. Dinh, Chonho Lee, Dusit Niyato and Ping Wang, A survey of mobile cloud computing: Architecture, applications, and approaches, wireless communications and mobile computing,, Wireless Communications and Mobile Computing, 13 (2013).

[2]

White Paper, Mobile Cloud Computing Solution Brief,, AEPONA, (2010).

[3]

R. Holman, Mobile Cloud Computing: \$9.5 Billion by 2014,, http://www.juniperresearch.com/analyst-xpress-blog/2010/01/26/mobile-cloud-applicationrevenues-to-hit-95-billion-by-2014-driven-by-converged-mobile-services/, (2010).

[4]

S. Abolfazli, Z. Sanaei, E. Ahmed, A. Gani and R. Buyya, Cloud-based augmentation for mobile devices: Motivation, taxonomies, and open issues,, IEEE Communications Surveys and Tutorials 2014, 16 (2014), 337.

[5]

R. K. Balan, Simplifying Cyber Foraging,, Ph.D thesis, (2006).

[6]

L. F. Bittencourt, E. R. M. Madeira and N. L. S. D. Fonseca, Scheduling in hybrid clouds,, IEEE Communications Magazine, 50 (2012), 42.

[7]

L. F. Bittencourt, HCOC: A cost optimization algorithm for workflow scheduling in hybrid clouds,, Journal of Internet Services & Applications , 2 (2011), 207.

[8]

A. Colorni, M. Dorigo and V. Maniezzo, Distributed optimization by ant colonies,, actes dela premiare conference europeenne sur la vie artificielle, (1991), 134.

[9]

M. Dorigo, Optimization, Learning and Natural Algorithms,, Ph.D thesis, (1992).

[10]

N. Handigol, S. Seetharaman, M. Flajslik, N. McKeown and R. Johari, Plug-n-Serve: Load-balancing Web Traffic Using OpenFlow, ACM SIGCOMM Demo,, 2009., ().

[11]

M. Koerner and O. Kao, Multiple service load-balancing with Open-Flow,, in 2012 IEEE 13th International Conference on High Performance Switching and Routing (HPSR), (2012), 211.

[12]

K. Kumar, J. Liu and Y. H. Lu, A survey of computation offloading for mobile systems,, Mobile Networks and Applications, 18 (2013), 129.

[13]

J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh and A. H. Byers, Big data: The next frontier for innovation, competition, and productivity,, Online Report, (2011).

[14]

M. Shiraz, E. Ahmed, A. Gani and Q. Han, Investigation on runtime partitioning of elastic mobile applications for mobile cloud computing,, Journal of Supercomputing, 67 (2014), 84.

[15]

M. Shiraz, A. Gani, R. H. Khokhar and R. Buyya, A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing,, IEEE Communications Survers & Tutorials, 15 (2011), 1294. doi: 10.1109/SURV.2012.111412.00045.

[16]

M. Shiraz, M. Sookhak, A. Gani and S. A. Ali Shah, A study on the critical analysis of computational offloading frameworks for mobile cloud computing,, Journal of Network and Computer Applications, 47 (2015), 47. doi: 10.1016/j.jnca.2014.08.011.

[17]

V. Viswanathan and I. Krishnamurthi, Finding relevant semantic association paths using semantic ant colony optimization algorithm,, Soft Computing, (2014).

[18]

R. Wang, D. Butnariu and J. Rexford, OpenFlow-based server load balancing gone wild,, in Proceedings of the 11th USENIX conference on Hot topics in management of internet, (2011).

[19]

X. Zhu, C. Chen, L. T. Yang and Y. Xiang, ANGEL: Agent-based scheduling for real-time tasks in virtualized clouds,, IEEE Transactions on Computers, pp (2015). doi: 10.1109/TC.2015.2409864.

[20]

X. Zhu, R. Ge, J. Sun and C. He, 3E: Energy-efficient elastic scheduling for independent tasks in heterogeneous computing system,, Journal of Systems and Software, 86 (2013), 302. doi: 10.1016/j.jss.2012.08.017.

show all references

References:
[1]

Hoang T. Dinh, Chonho Lee, Dusit Niyato and Ping Wang, A survey of mobile cloud computing: Architecture, applications, and approaches, wireless communications and mobile computing,, Wireless Communications and Mobile Computing, 13 (2013).

[2]

White Paper, Mobile Cloud Computing Solution Brief,, AEPONA, (2010).

[3]

R. Holman, Mobile Cloud Computing: \$9.5 Billion by 2014,, http://www.juniperresearch.com/analyst-xpress-blog/2010/01/26/mobile-cloud-applicationrevenues-to-hit-95-billion-by-2014-driven-by-converged-mobile-services/, (2010).

[4]

S. Abolfazli, Z. Sanaei, E. Ahmed, A. Gani and R. Buyya, Cloud-based augmentation for mobile devices: Motivation, taxonomies, and open issues,, IEEE Communications Surveys and Tutorials 2014, 16 (2014), 337.

[5]

R. K. Balan, Simplifying Cyber Foraging,, Ph.D thesis, (2006).

[6]

L. F. Bittencourt, E. R. M. Madeira and N. L. S. D. Fonseca, Scheduling in hybrid clouds,, IEEE Communications Magazine, 50 (2012), 42.

[7]

L. F. Bittencourt, HCOC: A cost optimization algorithm for workflow scheduling in hybrid clouds,, Journal of Internet Services & Applications , 2 (2011), 207.

[8]

A. Colorni, M. Dorigo and V. Maniezzo, Distributed optimization by ant colonies,, actes dela premiare conference europeenne sur la vie artificielle, (1991), 134.

[9]

M. Dorigo, Optimization, Learning and Natural Algorithms,, Ph.D thesis, (1992).

[10]

N. Handigol, S. Seetharaman, M. Flajslik, N. McKeown and R. Johari, Plug-n-Serve: Load-balancing Web Traffic Using OpenFlow, ACM SIGCOMM Demo,, 2009., ().

[11]

M. Koerner and O. Kao, Multiple service load-balancing with Open-Flow,, in 2012 IEEE 13th International Conference on High Performance Switching and Routing (HPSR), (2012), 211.

[12]

K. Kumar, J. Liu and Y. H. Lu, A survey of computation offloading for mobile systems,, Mobile Networks and Applications, 18 (2013), 129.

[13]

J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh and A. H. Byers, Big data: The next frontier for innovation, competition, and productivity,, Online Report, (2011).

[14]

M. Shiraz, E. Ahmed, A. Gani and Q. Han, Investigation on runtime partitioning of elastic mobile applications for mobile cloud computing,, Journal of Supercomputing, 67 (2014), 84.

[15]

M. Shiraz, A. Gani, R. H. Khokhar and R. Buyya, A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing,, IEEE Communications Survers & Tutorials, 15 (2011), 1294. doi: 10.1109/SURV.2012.111412.00045.

[16]

M. Shiraz, M. Sookhak, A. Gani and S. A. Ali Shah, A study on the critical analysis of computational offloading frameworks for mobile cloud computing,, Journal of Network and Computer Applications, 47 (2015), 47. doi: 10.1016/j.jnca.2014.08.011.

[17]

V. Viswanathan and I. Krishnamurthi, Finding relevant semantic association paths using semantic ant colony optimization algorithm,, Soft Computing, (2014).

[18]

R. Wang, D. Butnariu and J. Rexford, OpenFlow-based server load balancing gone wild,, in Proceedings of the 11th USENIX conference on Hot topics in management of internet, (2011).

[19]

X. Zhu, C. Chen, L. T. Yang and Y. Xiang, ANGEL: Agent-based scheduling for real-time tasks in virtualized clouds,, IEEE Transactions on Computers, pp (2015). doi: 10.1109/TC.2015.2409864.

[20]

X. Zhu, R. Ge, J. Sun and C. He, 3E: Energy-efficient elastic scheduling for independent tasks in heterogeneous computing system,, Journal of Systems and Software, 86 (2013), 302. doi: 10.1016/j.jss.2012.08.017.

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