A Collaborative Framework of Enabling Device Participation in Mobile Cloud Computing
Cloud Computing attracts much attention in the community of computer science and information technology because of resource efficiency and cost-effectiveness. It is also evolved to Mobile Cloud Computing to serve nomadic people. However, any service in Cloud Computing System inevitably experiences a network delay to access the computing resource or the data from the system, and entrusting the Cloud server with the entire task makes mobile devices idle. In order to mitigate the deterioration of network performance and improve the overall system performance, we propose a collaborative framework that lets the mobile device participate in the computation of Cloud Computing system by dynamically partitioning the workload across the device and the system. The proposed framework is based on it that the computing capability of the current mobile device is significantly enhanced in recent years and its multi-core CPU can employ threads to process the data in parallel. The empirical experimentation presents that it can be a promising approach to use the computing resource of the mobile device for executing computation-intensive tasks in Cloud Computing system.
KeywordsMobile Cloud Computing Multi-Thread Parallel Computing
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- 1.Buyya, R., Yeo, C.S., Venugopal, S.: Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities. In: 10th IEEE International Conference on High Performance Computing and Communications, HPCC 2008 (2008)Google Scholar
- 2.Sanaei, Z., Abolfazli, S., Gani, A., Khokhar, R.H.: Tripod of requirements in horizontal heterogeneous Mobile Cloud Computing. In: Proc.1st Int’l Conf. Computing, Information Systems, and Communications (2012)Google Scholar
- 3.Sanaei, Z., Abolfazli, S., Gani, A., Shiraz, M.: SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing. In: Mobicc. IEEE Workshop on Mobile Cloud Computing, Beijing, China (2012)Google Scholar
- 4.Woo, S., Kim, H.: Estimating Link Reliability in Wireless Networks: An Empirical Study and Interference Modeling. In: 2010 Proceedings IEEE INFOCOM (2010)Google Scholar
- 5.Zhu, J., Jiang, Z., Xiao, Z.: Twinkle: A fast resource provisioning mechanism for internet services. In: Proceedings of IEEE INFOCOM (2011)Google Scholar
- 6.Tullsen, D.M., Eggers, S.J., Levy, H.M.: Simultaneous multithreading: maximizing on-chip parallelism. In: Proceedings of the 25th Annual International Symposium on Computer Architecture, ISCA 1998, pp. 533–544 (1998)Google Scholar
- 7.Vincent, T.: On Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (1986)Google Scholar
- 8.Ahmed, N.: Discrete Cosine Transform. IEEE Transactions on Computers (1974)Google Scholar
- 9.Wells, M.T.: Mobile Image Processing on the Google Phone with the Android Operating System, http://www.3programmers.com/mwells/main_frame.html