Comprehensive Review and Analysis on Mobile Cloud Computing and Users Offloading using Improved Optimization Approach for Edge Computing

Authors

  • Chiti Nigam Computer Science, JECRC, Jaipur
  • Gajanand Sharma Computer Science, JECRC, Jaipur
  • Ekta Menghani Computer Science, JECRC, Jaipur

Keywords:

Mobile Computing, Security, Architecture, Distributed data, Parallel processing, Peer Processing

Abstract

This paper gives a depth overview about the technology mobile computing. It has been discussed here how these days cloud computing has reached into mobile and has helped us to reduce the battery and storage related issues in mobiles. Apart from smart phones mobile computing has affected other areas such as homes, offices, supermarts etc. Mobile cloud computing is fast and flexible. As a result, mobile cloud computing makes it easy for developers to create and share mobile app resources with end-users. Therefore, mobile applications can be built and updated faster .Mobile cloud computing shares resources. Mobile apps that run off the cloud aren’t constrained by any mobile device’s processing and storage limitations. All data-intensive processes can run from the cloud. This advantage means that any mobile device with access to a network can use mobile cloud apps, regardless of the operating system. Thus, users can enjoy cloud computing with Android or OS device. Sometimes fault tolerance is an issue relating to mobile cloud computing so to provide the high services without any noise interconnected high speed networks are provided . Mobile Cloud Computing (MCC) is an emerging technology that helps us in removing the shortcomings associated with the mobile computing. There is no need to download all the software that are required by the user as MCC makes it readily available. With the help of distributed data storage methods and parallel processing the process is enhanced giving a great experience to the user. In this both the data storage and processing happens outside the device. In this era mobile computing has become a trend in IT.[Kori,et.al,2019]. The users can obtain the maximum benefit of mobile technology when it is combined with the cloud technology. Mobile cloud computing also provides access to the people who are residing in the rural areas the various mobile services such as navigation, entertainment, commerce, storage and so on. Mobile cloud computing uses integrated data.  Mobile cloud computing lets users securely and quickly collect and integrate information from many sources, no matter where the data is. The architecture of mobile computer with its benefits is presented in this paper.

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References

Badea, Gheorghe, Raluca-Andreea Felseghi, Mihai Varlam, Constantin Filote, Mihai Culcer, Mariana Iliescu, and Maria Simona Răboacă. "Design and simulation of romanian solar energy charging station for electric vehicles." Energies 12, no. 1, 2019.

Singh, Dushyant, and Baldev Singh. "Secure Chess-Based Data Exchange and User Validation." Journal of Cases on Information Technology (JCIT) 24.4 (2022): 1-10.

Singh, Dushyant. "A Review on Deep Learning Models." Integrated Emerging Methods of Artificial Intelligence & Cloud Computing (2022): 223-229.

Rai, S. K. ., Rana, D. P. ., & Kashif, D. M. . (2022). Hotel Personnel Retention In Uttar Pradesh: A Study of HYATT Hotels. International Journal of New Practices in Management and Engineering, 11(01), 47–52. https://doi.org/10.17762/ijnpme.v11i01.173

Jadaun, A., Alaria, S.K. and Saini, Y. 2021. Comparative Study and Design Light Weight Data Security System for Secure Data Transmission in Internet of Things. International Journal on Recent and Innovation Trends in Computing and Communication. 9, 3 (Mar. 2021), 28–32. DOI:https://doi.org/10.17762/ijritcc.v9i3.5476.

Ashish, Vijay Kumar, Satish Kumar Alaria, Vivesk Sharma “Design Simulation and Assessment of Prediction of Mortality in Intensive Care Unit Using Intelligent Algorithms”, Mathematical Statistician and Engineering Applications. 71, 2 (May 2022), 355–367. DOI:https://doi.org/10.17762/msea.v71i2.9

Satish Kumar Alaria and Abha Jadaun, “Design and Performance Assessment of Light Weight Data Security System for Secure Data Transmission in IoT”, Journal of Network Security, Vol.: 9, Issue: 1, (2021) PP: 29-41.

Khandelwal, Ravi, Manish Kumar Mukhija, and Satish Kumar Alaria. "Numerical Simulation and Performance Assessment of Improved Particle Swarm Optimization Based Request Scheduling in Edge Computing for IOT Applications." New Arch-International Journal Of Contemporary Architecture 8, no. 2 (2021): 155-169.

S. Hu and G. Li, "Dynamic Request Scheduling Optimization in Mobile Edge Computing for IoT Applications," in IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1426-1437, Feb. 2020, doi: 10.1109/JIOT.2019.2955311.

Sehirli, E., & Alesmaeil, A. (2022). Detecting Face-Touch Hand Moves Using Smartwatch Inertial Sensors and Convolutional Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 122–128. https://doi.org/10.18201/ijisae.2022.275

M. Smith, A. Maiti, A. D. Maxwell, and A. A. Kist, “Object detection resource usage within a remote real-time video stream,” in Online Engineering & Internet of Things, Cham, Switzerland: Springer, 2018, pp. 266–277.

P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Commun. Surveys Tut., vol. 19, no. 3, pp. 1628–1656, 3rd Quart., 2017.

Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322–2358, 4th Quart., 2017.

H. A. Alameddine, S. Sharafeddine, S. Sebbah, S. Ayoubi, and C. Assi, “Dynamic task offloading and scheduling for low-latency IoT services in multi-access edge computing,” IEEE J. Sel. Areas Commun., vol. 37, no. 3, pp. 668–682, Mar. 2019.

M. Chen and Y. Hao, “Task offloading for mobile edge computing in software defined ultra-dense network,” IEEE J. Sel. Areas Commun., vol. 36, no. 3, pp. 587–597, Mar. 2018.

X. Lyu, H. Tian, C. Sengul, and P. Zhang, “Multiuser joint task offloading and resources optimization in proximate clouds,” IEEE Trans. Veh. Technol., vol. 66, no. 4, pp. 3435–3447, Apr. 2017.

Q. Wang, S. Guo, J. Liu, and Y. Yang, “Energy-efficient computation offloading and resource allocation for delay-sensitive mobile edge computing,” Sustain. Comput. Informat. Syst., vol. 21, pp. 154–164, Mar. 2019.

T. X. Tran and D. Pompili, “Joint task offloading and resource allocation for multi-server mobile-edge computing networks,” IEEE Trans. Veh. Technol., vol. 68, no. 1, pp. 856–868, Jan. 2019.

M. Shojafar, N. Cordeschi, and E. Baccarelli, “Energy-efficient adaptive resource management for real-time vehicular cloud services,” IEEE Trans. Cloud Comput., vol. 7, no. 1, pp. 196–209, Jan.–Mar. 2019.

S. M. R. Islam, N. Avazov, O. A. Dobre, and K.-S. Kwak, “Powerdomain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges,” IEEE Commun. Surveys Tuts., vol. 19, no. 2, pp. 721–742, 2nd Quart., 2017.

M. Kamel, W. Hamouda, and A. Youssef, “Ultra-dense networks: A survey,” IEEE Commun. Surveys Tuts., vol. 18, no. 4, pp. 2522–2545, 4th Quart., 2016.

Gill, D. R. . (2022). A Study of Framework of Behavioural Driven Development: Methodologies, Advantages, and Challenges. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 09–12. https://doi.org/10.17762/ijfrcsce.v8i2.2068

D. López-Pérez, M. Ding, H. Claussen, and A. H. Jafari, “Towards 1 Gbps/UE in cellular systems: Understanding ultra-dense small cell deployments,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2078–2101, 4th Quart., 2015.

B. Yu, L. Pu, Q. Xie, and J. Xu, “Energy efficient scheduling for IoT applications with offloading, user association and BS sleeping in ultra dense networks,” in Proc. 16th Int. Symp. Model. Optim. Mobile Ad Hoc Wireless Netw. (WiOpt), Shanghai, China, 2018, pp. 1–6.

C. Ma, F. Liu, Z. Zeng, and S. Zhao, “An energy-efficient user association scheme based on robust optimization in ultra-dense networks,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC Workshops), Beijing, China, 2018, pp. 222–226.

X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient multi-user computation offloading for mobile-edge cloud computing,” IEEE/ACM Trans. Netw., vol. 24, no. 5, pp. 2795–2808, Oct. 2016.

T. V. Do, N. H. Do, H. T. Nguyen, C. Rotter, A. Hegyi, and P. Hegyi, “Comparison of scheduling algorithms for multiple mobile computing edge clouds,” Simulat. Model. Pract. Theory, vol. 93, pp. 104–118, May 2019.

L. Gu, J. Cai, D. Zeng, Y. Zhang, H. Jin, and W. Dai, “Energy efficient task allocation and energy scheduling in green energy powered edge computing,” Future Gener. Comput. Syst., vol. 95, pp. 89–99, Jun. 2019.

Y. Jie, X. Tang, K.-K. R. Choo, S. Su, M. Li, and C. Guo, “Online task scheduling for edge computing based on repeated Stackelberg game,” J. Parallel Distrib. Comput., vol. 122, pp. 159–172, Dec. 2018.

A. Kiani and N. Ansari, “Toward hierarchical mobile edge computing: An auction-based profit maximization approach,” IEEE Internet Things J., vol. 4, no. 6, pp. 2082–2091, Dec. 2017.

K. Lin, S. Pankaj, and D. Wang, “Task offloading and resource allocation for edge-of-things computing on smart healthcare systems,” Comput. Elect. Eng., vol. 72, pp. 348–360, Nov. 2018.

T. Wang, G. Zhang, A. Liu, M. Z. A. Bhuiyan, and Q. Jin, “A secure IoT service architecture with an efficient balance dynamics based on cloud and edge computing,” IEEE Internet Things J., vol. 6, no. 3, pp. 4831–4843, Jun. 2019.

T. Bahreini, H. Badri, and D. Grosu, “An envy-free auction mechanism for resource allocation in edge computing systems,” in Proc. IEEE/ACM Symp. Edge Comput. (SEC), Seattle, WA, USA, 2018, pp. 313–322.

S. Misra and N. Saha, “Detour: Dynamic task offloading in softwaredefined fog for IoT applications,” IEEE J. Sel. Areas Commun., vol. 37, no. 5, pp. 1159–1166, May 2019.

Chiba, Z., El Kasmi Alaoui, M. S., Abghour, N., & Moussaid, K. (2022). Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm. International Journal of Communication Networks and Information Security (IJCNIS), 14(1). https://doi.org/10.17762/ijcnis.v14i1.5264

H. Guo, J. Liu, and J. Zhang, “Computation offloading for multi-access mobile edge computing in ultra-dense networks,” IEEE Commun. Mag., vol. 56, no. 8, pp. 14–19, Aug. 2018.

H. Guo, J. Zhang, J. Liu, H. Zhang, and W. Sun, “Energy-efficient task offloading and transmit power allocation for ultra-dense edge computing,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2018, pp. 1–6.

S. Jeong, O. Simeone, and J. Kang, “Mobile edge computing via a UAVmounted cloudlet: Optimization of bit allocation and path planning,” IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2049–2063, Mar. 2018.

Y. Nakamura, T. Mizumoto, H. Suwa, Y. Arakawa, H. Yamaguchi, and K. Yasumoto, “In-situ resource provisioning with adaptive scale-out for regional IoT services,” in Proc. IEEE/ACM Symp. Edge Comput. (SEC), Seattle, WA, USA, 2018, pp. 203–213.

M. Kumar and C. Guria, “The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization,” Inf. Sci., vols. 382–383, pp. 15–37, Mar. 2017.

Singh, Pooja, Manish Kumar Mukhija, and Satish Kumar Alaria. "An Approach for Cloud Security Using TPA-and Role-Based Hybrid Concept." In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security, pp. 153-162. Springer, Singapore, 2023.

S. K. A. S. D. “Reducing the Packets Loss Using New MAC Protocol”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 1, no. 9, Sept. 2013, pp. 747-51, doi:10.17762/ijritcc.v1i9.2856.

Mishra, P. ., S. K. . Alaria, and P. . Dangi. “Design and Comparison of LEACH and Improved Centralized LEACH in Wireless Sensor Network”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 9, no. 5, May 2021, pp. 34-39, doi:10.17762/ijritcc.v9i5.5478

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Published

15.10.2022

How to Cite

Nigam, C. ., Sharma, G. ., & Menghani, E. . (2022). Comprehensive Review and Analysis on Mobile Cloud Computing and Users Offloading using Improved Optimization Approach for Edge Computing. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 234 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2286

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Research Article