Resource Intensification for Mobile Devices Using the Approximate Computing Entities
PDF

Keywords

Mobile Devices Cloud Computing
Mobile Cloud Computing
Optimal Offloading
Resource Allocation

How to Cite

Suma, V. 2020. “Resource Intensification for Mobile Devices Using the Approximate Computing Entities”. Journal of Trends in Computer Science and Smart Technology 2 (1): 26-36. https://doi.org/10.36548/jtcsst.2020.1.003.

Abstract

The mobile devices are termed to highly potential due to their capability of rendering services without being plugged to the electric grid. These device are becoming highly prominent due to their constant progress in computing as well as storing capacities and as they are very much closer to the users. Despites its advantages it still faces many problems due to the load balancing and energy consumption due to its limited battery limited and storage availability as some applications or the video downloading requires high storage facilities consuming majority of the energy in turn reducing the performance of the mobile devices. So as to improve the performance and the capability of the mobile devices the mobile cloud computing that integrates the mobile devices with the cloud paradigm has emerged as a promising paradigm. This enables the augmentation of the local resources for the mobile devices to enhance its capabilities in order to improve its functioning. This is basically done by proper offloading and resource allocation. The proposed method in the paper utilizes the optimal offloading strategy (Single and double strand offloading) and follows an Ant colony optimization based resource allocation for improving the functioning the mobile devices in terms of energy consumption and storage.

PDF

References

Tao, Yaling, Yongbing Zhang, and Yusheng Ji. "Efficient computation offloading strategies for mobile cloud computing." In 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, pp. 626-633. IEEE, 2015.

Hyytiä, Esa, Thrasyvoulos Spyropoulos, and Jörg Ott. "Optimizing offloading strategies in mobile cloud computing." Cryptanalyst (2013).

Smys, S., & Raj, J. S. (2019). A Stochastic Mobile Data Traffic Model for Vehicular Ad Hoc Networks. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 1(01), 55-63.

Deng, Shuiguang, Longtao Huang, Javid Taheri, and Albert Y. Zomaya. "Computation offloading for service workflow in mobile cloud computing." IEEE transactions on parallel and distributed systems 26, no. 12 (2014): 3317-3329.

Bhalaji, N. (2019). Delay Diminished Efficient Task Scheduling and Allocation for Heterogeneous Cloud Environment. Journal of trends in Computer Science and Smart technology (TCSST), 1(01), 51-62.

Ma, Xiao, Chuang Lin, Xudong Xiang, and Congjie Chen. "Game-theoretic analysis of computation offloading for cloudlet-based mobile cloud computing." In Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 271-278. 2015.

Karunakaran, V. "A Stochastic Development of Cloud Computing Based Task Scheduling Algorithm." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 41-48.

Barbarossa, Sergio, Paolo Di Lorenzo, and Stefania Sardellitti. "Computation offloading strategies based on energy minimization under computational rate constraints." In 2014 European Conference on Networks and Communications (EuCNC), pp. 1-5. IEEE, 2014.

Barbarossa, Sergio, Stefania Sardellitti, and Paolo Di Lorenzo. "Computation offloading for mobile cloud computing based on wide cross-layer optimization." In 2013 Future Network & Mobile Summit, pp. 1-10. IEEE, 2013.

Bashar, A. (2019). Secure And Cost Efficient Implementation Of The Mobile Computing Using Offloading Technique. Journal of Information Technology, 1(01), 48-57.