An Efficient Model for Prediction Based Optimization in Mobile Cloud Task Offloading in TORO

Authors

  • G. Amirthayogam Associate Professor, Department of Information Technology, Department of Information Technology, Hindustan Institute of Technology and Science (Deemed to be University), Chennai, Tamil Nadu, India-603103
  • B. Thanikaivel Assistant Professor, Department of Information Technology, Hindustan Institute of Technology and Science (Deemed to be University), Chennai, Tamil Nadu, India-603103.
  • G.Renuga Devi Assistant Professor, Department of Computer Science and Engineering,, P.S.R.Engineering College,Sivakasi, Tamil Nadu, India.
  • J. Venkatarangan Assistant professor, Department of Computer Science and Design, St Martin’s Engineering College, Secunderbad, Telangana, India-500100
  • M. Sujaritha Professor, Sri Krishna College of Engineering and Technology, Kuniyamuthur, Tamil Nadu 641008, India.
  • N. Kumaran Assistant professor, Department of Mathematics, Vel Tech Rangarajan Dr.Sagunthala R &D Institute of science and technology, Avadi, Chennai - 600062

Keywords:

On-demand prediction, Resource discovery, Reliable Resource, context-awareness, Partitioning, Scheduling, Task offloading, Resource allocation, Resource optimization

Abstract

Mobile Cloud Computing (MCC) is a booming field with the high usage of smart devices to overcome the on-demand resource availability to improve performance. The humans are migrating faster for their work but due to the portable mobile devices cause resource migrating problem which degrades the performance of MCC. The smart devices are capable of operating various kinds of day-to-day applications such E-Commerce, Banking, Education Healthcare etc. In this paper, Task Offloading and Resource Optimization (TORO) architecture is proposed to handle the migration problem by optimizing the resource with supporting operations such as resource demand prediction, cloudlet resource discovery with reliability, task partitioning, task scheduling and task offloading. The implementation and evaluation are carried by simulating the proposed TORO architecture and comparing with the existing FDCO algorithm and mCloud model. Further, the evaluation results depict the proposed TORO architecture executes the tasks faster with multiple operations integrated together to provide better performance when compared with the existing FDCO algorithm and mCloud model.

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References

B. Varghese, R. Buyya, Next generation cloud computing: New trends and research directions, Future Generation Computer Systems, 79 (2018) 849-861. https://doi.org/10.1016/j.future.2017.09.020.

Jaiswal, A.S., Thakare, V.M. and Sherekar, S.S. (2015) ‘Study and analysis of architecture components of cloudlets in MCC’, International Journal of Electronics, Communication and Soft Computing Science and Engineering (IJECSCSE), pp. 376 – 382.

Chunlin, L., Jing, Z. and Youlong, L. (2018) Cloud-based mobile service provisioning for system performance optimization, International Journal of Ad Hoc and Ubiquitous Computing, Vol. 29, No. 3, pp. 193 – 207. doi: 10.1504/IJAHUC.2018.095476

X. Lyu, H. Tian, C. Sengul, P. Zhang, Multiuser joint task offloading and resource optimization in proximate clouds, IEEE Transactions on Vehicular Technology, 66 (4) (2017) 3435 – 3447. https://doi.org/10.1109/tvt.2016.2593486.

K. Manbir, K. Kiranbir, K. Lohit, An Efficient Resource Provisioning Technique in Inter-Cloud Using Peer-to-Peer Approach, American Journal of Engineering and Applied Sciences, 10 (2) (2017) 529–539. https://doi.org/10.3844/ajeassp.2017.529.539.

H. Wen, L. Yang, Z. Wang, ParGen: A Parallel Method for Partitioning Data Stream Applications in Mobile Edge Computing, IEEE Access, 6 (2018) 5037-5048. https://doi.org/10.1109/access.2017.2776358.

Y. Son and Y. Lee, “Offloading Method for Efficient Use of Local Computational Resources in Mobile Location-Based Services Using Clouds,” Mobile Information Systems, vol. 2017, pp. 1–9, 2017. https://doi.org/10.1155/2017/1856329

Y.-H. Kao, B. Krishnamachari, M.-R. Ra, and F. Bai, “Hermes: Latency Optimal Task Assignment for Resource-constrained Mobile Computing,” IEEE Transactions on Mobile Computing, vol. 16, no. 11, pp. 3056–3069, Nov. 2017. https://doi.org/10.1109/tmc.2017.2679712

X. Jin, Z. Wang, and W. Hua, “Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing,” Mobile Information Systems, vol. 2019, pp. 1–17, Sep. 2019. https://doi.org/10.1155/2019/8049804

B. Li, Y. Pei, H. Wu, and B. Shen, “Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds,” The Journal of Supercomputing, vol. 71, no. 8, pp. 3009–3036, Apr. 2015. https://doi.org/10.1007/s11227-015-1425-9

W. Junior, A. França, K. Dias, and J. N. de Souza, “Supporting mobility-aware computational offloading in mobile cloud environment,” Journal of Network and Computer Applications, vol. 94, pp. 93–108, Sep. 2017. https://doi.org/10.1016/j.jnca.2017.07.008

M. Goudarzi, M. Zamani, and A. T. Haghighat, “A fast hybrid multi-site computation offloading for mobile cloud computing,” Journal of Network and Computer Applications, vol. 80, pp. 219–231, Feb. 2017. https://doi.org/10.1016/j.jnca.2016.12.031

Z. Kuang, S. Guo, J. Liu, and Y. Yang, “A quick-response framework for multi-user computation offloading in mobile cloud computing,” Future Generation Computer Systems, vol. 81, pp. 166–176, Apr. 2018. https://doi.org/10.1016/j.future.2017.10.034

I. Yaqoob, E. Ahmed, A. Gani, S. Mokhtar, and M. Imran, “Heterogeneity-Aware Task Allocation in Mobile Ad Hoc Cloud,” IEEE Access, vol. 5, pp. 1779–1795, 2017. https://doi.org/10.1109/access.2017.2669080

H. Cao and J. Cai, “Distributed Multiuser Computation Offloading for Cloudlet-Based Mobile Cloud Computing: A Game-Theoretic Machine Learning Approach,” IEEE Transactions on Vehicular Technology, vol. 67, no. 1, pp. 752–764, Jan. 2018. https://doi.org/10.1109/tvt.2017.2740724

X. Tao, K. Ota, M. Dong, H. Qi, and K. Li, “Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing,” IEEE Wireless Communications Letters, vol. 6, no. 6, pp. 774–777, Dec. 2017. https://doi.org/10.1109/lwc.2017.2740927

Z. Yin, H. Chen, and F. Hu, “An advanced decision model enabling two-way initiative offloading in edge computing,” Future Generation Computer Systems, vol. 90, pp. 39–48, Jan. 2019. https://doi.org/10.1016/j.future.2018.07.031

C. Papagianni, A. Leivadeas, S. Papavassiliou, V. Maglaris, C. Cervello-Pastor, and A. Monje, “On the optimal allocation of virtual resources in cloud computing networks,” IEEE Transactions on Computers, vol. 62, no. 6, pp. 1060–1071, Jun. 2013. https://doi.org/10.1109/tc.2013.31

F. A. Nakahara and D. M. Beder, “A context-aware and self-adaptive offloading decision support model for mobile cloud computing system,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, no. 5, pp. 1561–1572, Apr. 2018. https://doi.org/10.1007/s12652-018-0790-7

M. Chen and Y. Hao, “Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 3, pp. 587–597, Mar. 2018. https://doi.org/10.1109/jsac.2018.2815360

A. Ceselli, M. Fiore, M. Premoli, and S. Secci, “Optimized assignment patterns in Mobile Edge Cloud networks,” Computers & Operations Research, vol. 106, pp. 246–259, Jun. 2019. https://doi.org/10.1016/j.cor.2018.02.022

G. Peng, H. Wang, J. Dong, and H. Zhang, “Knowledge-Based Resource Allocation for Collaborative Simulation Development in a Multi-Tenant Cloud Computing Environment,” IEEE Transactions on Services Computing, vol. 11, no. 2, pp. 306–317, Mar. 2018. https://doi.org/10.1109/tsc.2016.2518161

J. Tang, W. P. Tay, and T. Q. S. Quek, “Cross-Layer Resource Allocation With Elastic Service Scaling in Cloud Radio Access Network,” IEEE Transactions on Wireless Communications, vol. 14, no. 9, pp. 5068–5081, Sep. 2015. https://doi.org/10.1109/twc.2015.2432023

F. Wang, J. Xu, X. Wang, and S. Cui, “Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems,” IEEE Transactions on Wireless Communications, vol. 17, no. 3, pp. 1784–1797, Mar. 2018. https://doi.org/10.1109/twc.2017.2785305

S. Li, N. Zhang, S. Lin, L. Kong, A. Katangur, M. K. Khan, M. Ni, and G. Zhu, “Joint Admission Control and Resource Allocation in Edge Computing for Internet of Things,” IEEE Network, vol. 32, no. 1, pp. 72–79, Jan. 2018. https://doi.org/10.1109/mnet.2018.1700163

J. L. D. Neto, S.-Y. Yu, D. F. Macedo, J. M. S. Nogueira, R. Langar, and S. Secci, “ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing,” IEEE Transactions on Mobile Computing, vol. 17, no. 11, pp. 2660–2674, Nov. 2018. https://doi.org/10.1109/tmc.2018.2815015

Y. Gu, Z. Chang, M. Pan, L. Song, and Z. Han, “Joint Radio and Computational Resource Allocation in IoT Fog Computing,” IEEE Transactions on Vehicular Technology, vol. 67, no. 8, pp. 7475–7484, Aug. 2018. https://doi.org/10.1109/tvt.2018.2820838

F.-H. Tseng, H.-H. Cho, K.-D. Chang, J.-C. Li, and T. K. Shih, “Application-oriented offloading in heterogeneous networks for mobile cloud computing,” Enterprise Information Systems, vol. 12, no. 4, pp. 398–413, Feb. 2017. https://doi.org/10.1080/17517575.2017.1287432

X. Guo, L. Liu, Z. Chang, and T. Ristaniemi, “Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds,” Wireless Networks, vol. 24, no. 1, pp. 79–88, Jun. 2016. https://doi.org/10.1007/s11276-016-1322-z

S. Rashidi and S. Sharifian, “A hybrid heuristic queue based algorithm for task assignment in mobile cloud,” Future Generation Computer Systems, vol. 68, pp. 331–345, Mar. 2017. https://doi.org/10.1016/j.future.2016.10.014

J. Li and C. Wu, “Aviation Logistics Mobile Internet Cloud Computing Optimization,” International Journal of u- and e- Service, Science and Technology, vol. 9, no. 7, pp. 369–380, Jul. 2016. https://doi.org/10.14257/ijunesst.2016.9.7.37

Y. Cai, F. R. Yu, and S. Bu, “Dynamic Operations of Cloud Radio Access Networks (C-RAN) for Mobile Cloud Computing Systems,” IEEE Transactions on Vehicular Technology, vol. 65, no. 3, pp. 1536–1548, Mar. 2016. https://doi.org/10.1109/tvt.2015.2411739

Y. Lin, “Based on Particle Swarm Optimization Algorithm of Cloud Computing Resource Scheduling in Mobile Internet,” International Journal of Grid and Distributed Computing, vol. 9, no. 6, pp. 25–34, Jun. 2016. https://doi.org/10.14257/ijgdc.2016.9.6.03

Joshi, P., Rathnamma, M.V., Srujan Raju, K., Pawar, U. (2021). Miss Rate Estimation (MRE) an Novel Approach Toward L2 Cache Partitioning Algorithm’s for Multicore System. In: Satapathy, S., Bhateja, V., Janakiramaiah, B., Chen, YW. (eds) Intelligent System Design. Advances in Intelligent Systems and Computing, vol 1171. Springer, Singapore. https://doi.org/10.1007/978-981-15-5400-1_58

Jeyakanth, Krishnan, Perumalsamy Venkatakrishnan, and Chinnasamy Chitra. "Optimized channel prediction and auction‐based channel allocation for personal cognitive networks." International Journal of Communication Systems 36, no. 3 (2023): e5391.

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Published

24.03.2024

How to Cite

Amirthayogam, G. ., Thanikaivel, B. ., Devi, G. ., Venkatarangan, J. ., Sujaritha, M. ., & Kumaran, N. . (2024). An Efficient Model for Prediction Based Optimization in Mobile Cloud Task Offloading in TORO . International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 781–791. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5302

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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