Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Aldmour, R.; Yousef, S.; Yaghi, M.; Tapaswi, S.; Pattanaik, K. K.; Cole, M. (2016)
Languages: English
Types: Article

Classified by OpenAIRE into

Offloading in cloud computing is a way to execute big files in short times due to the available processing resources on core computers. However in some cases it is vital to execute the file locally on the node if the file size is less than a threshold size. There is a trade off in this issue due to the limited power of the node, therefore, in this paper a novel algorithm is proposed where the file size in each case is measured and then a decision is taken to either execute the file on the node or to send the file to be processed in the core cloud. The main reason is to save time of the execution of the file. However, the second and important reason, is to save the limited node energy in some large file, where the power consumption of the node will be very high. The measurement of the file size and the execution time and the power consumption for the local node and the core cloud is measured to represent an input to the execution decision
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Zhou, B., Dastjerdi, A.V., Calheiros, R.N., Srirama, S.N. and Buyya, R., 2015, June. A context sensitive offloading scheme for mobile cloud computing service. In Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on (pp. 869-876). IEEE.
    • 2. K. Kumar, J. Liu, Y.-H. Lu, and B. Bhargava, \A Survey of Computation Offloading for Mobile Systems," Mobile Networks and Applications, vol. 18, no. 1, pp. 129{140, 2013. 31, 33
    • 3. Wolski, R., Gurun, S., Krintz, C. and Nurmi, D., 2008, April. Using bandwidth data to make computation offloading decisions. In Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on (pp. 1-8). IEEE.
    • 4. Gao, B., He, L., Liu, L., Li, K. and Jarvis, S.A., 2012, September. From mobiles to clouds: Developing energy-aware offloading strategies for workflows. In Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing (pp. 139-146). IEEE Computer Society.
    • 5. Kumar, K. and Lu, Y.H., 2010. Cloud computing for mobile users: Can offloading computation save energy?. Computer, (4), pp.51-56.
    • 6. Altamimi, M., Abdrabou, A., Naik, K. and Nayak, A., 2015. Energy cost models of smartphones for task offloading to the cloud. Emerging Topics in Computing, IEEE Transactions on, 3(3), pp.384-398.
    • 7. Qian, H. and Andresen, D., 2015, June. Reducing mobile device energy consumption with computation offloading. In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015 16th IEEE/ACIS International Conference on (pp. 1-8). IEEE.
    • 8. Altamimi, M., Palit, R., Naik, K. and Nayak, A., 2012, June. Energy-as-a-Service (EaaS): On the efficacy of multimedia cloud computing to save smartphone energy. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on (pp. 764-771). IEEE.
    • 9. Altamimi, M. and Naik, K., 2014, June. A Practical Task Offloading Decision Engine for Mobile Devices to Use Energy-as-a-Service (EaaS). In Services (SERVICES), 2014 IEEE World Congress on (pp. 452-453). IEEE.
    • 10. Justino, T. and Buyya, R., 2014, October. Outsourcing resource-intensive tasks from mobile apps to clouds: Android and aneka integration. In Cloud Computing in Emerging Markets (CCEM), 2014 IEEE International Conference on (pp. 1-8). IEEE.
    • 11. Jararweh, Y., Ababneh, F., Khreishah, A. and Dosari, F., 2014. Scalable cloudlet-based mobile computing model. Procedia Computer Science, 34, pp.434-441.
    • 12. R. Kemp, N. Palmer, T. Kielmann, and H. Bal, “Cuckoo: A Computation Offloading Framework for Smartphones,” in Mobile Computing, Applications, and Services, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, M. Gris and G. Yang, Eds. Springer Berlin Heidelberg, 2012, vol. 76, pp. 59-79. 34, 35
    • 13. Magurawalage, C.M.S., Yang, K., Hu, L. and Zhang, J., 2014. Energy-efficient and network-aware offloading algorithm for mobile cloud computing.Computer Networks, 74, pp.22-33.
    • 14. Shiraz, M., Sookhak, M., Gani, A. and Shah, S.A.A., 2015. A study on the critical analysis of computational offloading frameworks for mobile cloud computing. Journal of Network and Computer Applications, 47, pp.47-60.
    • 15. Shiraz, M., Gani, A., Shamim, A., Khan, S. and Ahmad, R.W., 2015. Energy efficient computational offloading framework for mobile cloud computing.Journal of Grid Computing, 13(1), pp.1-18.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article