An Analysis the Energy Consumption of Dynamic Task Offloading and Task Execution in MCC

Article Sidebar

PDF HTML
Published: Sep 2, 2024
DOI: https://doi.org/10.29070/bq8ane93
Keywords:
Mobile Cloud Computing, Cloud Computing, Offloading, Energy aware, wireless

Main Article Content

Authors

Vivek Kumar Shukla

Research Scholar, Shri Krishna University, Chhatarpur, Madhya Pradesh, India

Dr. Qaim Mehdi Rizbi

Associate Professor, Department of Computer Science & Application, Shri Krishna University, Chhatarpur, Madhya Pradesh, India

Abstract

Cloud computing is a group of distributed computing resources that may run any application. MCC is the result of merging the cloud environment with mobile devices. computational offloading is carried out on the cloud in order to save the processing capacity of handheld devices. There will be a rise in computing as the number of sensors used increases, necessitating greater data analysis. Another problem with mobile environments is the power drain on batteries. One solution is to move the work to a remote location where it can be more efficiently executed. In this off-site setting, you'll find powerful cloud servers with plenty of processing capacity. One way that dense mobile apps were able to move their work to the cloud is through computational offload. why it's important to provide a proper explanation of the time constraint in wireless & distant environments, and how reaction time affects offloading for mobile devices & remote servers.

Downloads

Download data is not yet available.

Article Details

Issue

Vol. 20 No. 2 (2023): Journal of Advances in Science and Technology (JAST)

Section

Articles

References

  1. Ali, M., J. Zain, M.F. Zolkipli and G. Badshah (2015). Mobile cloud computing & mobile's battery efficiency approaches: A Review. Journal of Theoretical and Applied Information Technology, Vol. 79, No. 1, pp. 153.
  2. Boukerche, A., Guan, S., & Grande, R. E. D. (2019). Sustainable offloading in mobile cloud computing: algorithmic design and implementation. ACM Computing Surveys (CSUR), 52(1), 1-37.
  3. Cui, Y., Ma, X., Wang, H., Stojmenovic, I., & Liu, J. (2013). A survey of energy efficient wireless transmission and modeling in mobile cloud computing. Mobile Networks and Applications, 18(1), 148-155.
  4. Mayo, R.N., and P. Ranganathan (2003). Energy consumption in mobile devices: why future systems need requirements–aware energy scale-down. In Proceeding of the International workshop on Power-Aware Computer Systems, pp. 26-39.
  5. Nagaraju, D. and V. Saritha. Energy-aware dynamic task offloading and collective task execution in Mobile Cloud Computing. Communicated to Journal of Supercomputing. (Scopus Indexed).
  6. Ou, S., K. Yang and J. Zhang (2007). An effective offloading middleware for pervasive services on mobile devices. Pervasive and Mobile Computing, Vol. 3, No. 4, pp. 362-385.
  7. Qureshi, S.S., T. Ahmad and K. Rafique (2011). Mobile cloud computing as future for mobile applications-implementation methods and challenging issues. In Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on, pp. 467-471.
  8. Rahman, M., Gao, J., & Tsai, W. T. (2013, March). Energy saving in mobile cloud computing. In 2013 IEEE International Conference on Cloud Engineering (IC2E) (pp. 285-291). IEEE.
  9. Saad, S. M., & Nandedkar, S. (2014). Energy efficient mobile cloud computing. International Journal of Computer Science and Information Technologies, 56(5), 1757-1771.
  10. Tang, C., Xiao, S., Wei, X., Hao, M., & Chen, W. (2018, January). Energy Efficient and Deadline Satisfied Task Scheduling in Mobile Cloud Computing. In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 198-205).IEEE.
  11. Wang Z., Pang X., Chen Y., Shao H., Wang Q., Wu L., Chen H. and Qi H. (2019), “Privacy-preserving Crowd-sourced Statistical Data Publishing with an Untrusted Server”, IEEE Transactions on Mobile Computing, 18(6), pp. 1356-1367, 2019.
  12. Wang, K., Yang, K., &Magurawalage, C. S. (2016). Joint energy minimization and resource allocation in C-RAN with the mobile cloud. IEEE Transactions on Cloud Computing, 6(3), 760-770
  13. Xie J, Dan L, Yin L, Sun Z, Xiao Y, 2015, ‘An energy-optimal scheduling for collaborative execution in mobile cloud computing’, In: 2015 International Conference and Workshop on Computing and Communication (IEMCON), IEEE, pp. 1–6.
  14. Xiong Y., Huang S., Wu M., She J and Jiang K. (2019), “A Johnson's-Rule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computing”, IEEE Transactions on Cloud Computing, 7(3), pp. 597-610, 2019.
  15. Xu B, Peng Z, Xiao F, Gates AM, Yu J-P, 2015, ‘Dynamic deployment of virtual machines in cloud computing using multi-objective optimization’, Soft Computing, Vol. 19, No.8,pp.2265–2273.
  16. Xu X., Dou W., Zhang X and Chen, J. (2016), “EnReal: An Energy-Aware Resource Allocation Method for Scientific Workflow Executions in Cloud Environment”, IEEE Transactions on Cloud Computing, 4(2), pp. 166-179, 2016.