A Novel Framework for Computational Offloading in Mobile Cloud Computing
Keywords:
Mobile Cloud Computing, Computational Offloading, Task Offloading, Cloud Offloading, Energy Efficiency, Resource AllocationAbstract
One promising paradigm for overcoming the resource constraints of mobile devices is mobile cloud computing or MCC. To do this, tasks requiring a lot of processing power are relocated to servers in the cloud. To optimize resource utilization, decrease energy consumption, and generally enhance mobile device performance, this research suggests using a new paradigm for computational offloading in MCC. Intelligent decision-making algorithms are a part of the offered framework; they decide on the fly if tasks should be moved to the cloud. The device's capabilities, the network's state, the activity's complexity, and the amount of energy spent are among the considerations in this decision-making process. Protocols for communication, methods for dividing tasks, and techniques for offloading are only a few of the important parts of the system examined in this article. While keeping energy efficiency and reaction times fast, the experimental findings show that the suggested design greatly enhances mobile device performance.
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References
Shiraz, M., Gani, A., Shamim, A., Khan, S., & Ahmad, R. W. (2015). Energy efficient computational offloading framework for mobile cloud computing. Journal of Grid Computing, 13, 1-18.
Naouri, A., Wu, H., Nouri, N. A., Dhelim, S., & Ning, H. (2021). A novel framework for mobile-edge computing by optimizing task offloading. IEEE Internet of Things Journal, 8(16), 13065-13076.
Manukumar, S. T., & Muthuswamy, V. (2019). A novel multi-objective efficient offloading decision framework in cloud computing for mobile computing applications. Wireless Personal Communications, 107, 1625-1642.
Kovachev, D. (2012). Framework for computation offloading in mobile cloud computing. IJIMAI, 1(7), 6-15.
Shiraz, M., Sookhak, M., Gani, A., & 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, 47-60.
Chen, X., Chen, S., Zeng, X., Zheng, X., Zhang, Y., & Rong, C. (2017). Framework for context-aware computation offloading in mobile cloud computing. Journal of Cloud Computing, 6, 1-17.
Shiraz, M., & Gani, A. (2014). A lightweight active service migration framework for computational offloading in mobile cloud computing. The Journal of Supercomputing, 68, 978-995.
Cardellini, V., De Nitto Personé, V., Di Valerio, V., Facchinei, F., Grassi, V., Lo Presti, F., & Piccialli, V. (2016). A game-theoretic approach to computation offloading in mobile cloud computing. Mathematical Programming, 157, 421-449.
Akherfi, K., Gerndt, M., & Harroud, H. (2018). Mobile cloud computing for computation offloading: Issues and challenges. Applied computing and informatics, 14(1), 1-16.
Erana Veerappa Dinesh, S., & Valarmathi, K. (2020). A novel energy estimation model for constraint-based task offloading in mobile cloud computing. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5477-5486.
Kumar, J., Malik, A., Dhurandher, S. K., & Nicopolitidis, P. (2017). Demand-based computation offloading framework for mobile devices. IEEE Systems Journal, 12(4), 3693-3702.
Zheng, J., Cai, Y., Wu, Y., & Shen, X. (2018). Dynamic computation offloading for mobile cloud computing: A stochastic game-theoretic approach. IEEE Transactions on Mobile Computing, 18(4), 771-786.
Choi, M., Park, J., & Jeong, Y. S. (2013). Mobile cloud computing framework for a pervasive and ubiquitous environment. The Journal of Supercomputing, 64, 331-356.
