International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Energy Efficient Data Transfer in Mobile Cloud Computing Environment Using Particle-Salp Swarm Optimization Technique


Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India
2 Department of Computer Science, Government Arts College, Udhagamandalam, Tamil Nadu, India
 

Mobile cloud computing (MCC) can request the service of cloud from the mobile appliance, such as mobile phones, laptops, palm tops and so on for data transfer in such a way to provide more beneficial applications. The consumption of energy is a major problem during the transfer of data in MCC that is needed to be optimized with the allocation of the tasks to mobile or cloud environment in an efficient manner. In order to deal this issue, a hybrid particle-salp (PS-SALP) swarm optimization technique is proposed in this research that incorporates the characteristic features of the particles and the salps leading to better convergence to enhanced solution in the resource allocation of data transfer. The main aim of this paper is to minimize the utility cost (UC) representing a better balance between energy consumption and the period of execution of the task. Initially, the local optimal solutions for each problem is found, followed by which the global optimal solution is obtained using the proposed PS-SALP optimization algorithm. The performance of the proposed technique of data transfer in MCC is analyzed in terms of the metrics, such as UC, energy consumption, and the task execution time. The results show the superiority of the proposed technique in energy efficient data transfer in the MCC environment.

Keywords

Mobile Cloud Computing, Utility Cost, Optimization, Energy Consumption, Data Transfer, PS-SALP.
User
Notifications
Font Size

  • Abolfazli, S., Sanaei, Z., Gani, A., Xia, F., Yang, L.T., 2014. "Rich mobile applications:genesis, taxonomy, and open issues". J. Netw. Comput. Appl. 40, 345–362.

  • Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al., 2010. "A view of cloud computing".Commun. ACM 53 (4), 50–58.

  • Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N., 2009. The case for vm-based cloudlets in mobile computing.IEEE Pervasive Comput. 8 (4), 14–23.

  • Gao, Zihan, Wanming Hao, Ruizhe Zhang, and Shouyi Yang. "Markov decision process-based computation offloading algorithm and resource allocation in time constraint for mobile cloud computing." IET Communications 14, no. 13 (2020): 2068-2078.

  • Alkhalaileh, Mohammad, Rodrigo N. Calheiros, QuangVinh Nguyen, and BahmanJavadi, "Data-intensive application scheduling on mobile edge cloud computing", Journal of Network and Computer Applications 167 (2020): 102735.

  • D. C. Marinescu, ``Big data, data streaming, and the mobile cloud,'' in Cloud Computing. San Mateo, CA, USA: Morgan Kaufmann, 2018.

  • Li, Juan, and XiaoluXu, "EERA: An Energy-Efficient Resource Allocation Strategy for Mobile Cloud Workflows", IEEE Access 8 (2020): 217008-217023

  • Alfakih, Taha, Mohammad Mehedi Hassan, Abdu Gumaei, Claudio Savaglio, and Giancarlo Fortino. "Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA." IEEE Access 8 (2020): 54074-54084.

  • Akki, Praveena, and V. Vijayarajan. "An efficient mobility prediction model for resource allocation in mobile cloud computing." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 1 (2021): 149-157.

  • Sinha, D., Datta, S. and Das, A.K., 2020,Secure Data Sharing for Cloud-Based Services inHierarchical Multi-group Scenario,in Advances in Computational Intelligence, pp. 229-244, Springer, Singapore.12, pp. 439487.. IEEE Pervasive Comput. 8 (4), 14–23.

  • Alnajrani, H.M., Norman, A.A. and Ahmed, B.H., 2020,Privacy and data protection in mobile cloud computing: A systematic mapping study, Plos one, 15(6), p.e0234312.

  • Arumugam, M., S. Deepa, G. Arun, P. Sathishkumar, and K. Jeevanantham. "Secure data sharing for mobile cloud computing using RSA." In IOP Conference Series: Materials Science and Engineering, vol. 1055, no. 1, p. 012108. IOP Publishing, 2021.

  • Hidayat, T. and Mahardiko, R., 2020. A Systematic Literature Review Method On AES Algorithm for Data Sharing Encryption On Cloud Computing. International Journal of Artificial Intelligence Research, 4(1), 2020.

  • Nanda, Sarmistha, Chhabi Rani Panigrahi, and BibudhenduPati. "Emergency management systems using mobile cloud computing: A survey." International Journal of Communication Systems (2020): pp.4619.

  • Saha, Sajeeb, and Mohammad S. Hasan. "Effective task migration to reduce execution time in mobile cloud computing." In 2017 23rd International Conference on Automation and Computing (ICAC), pp. 1-5.IEEE, 2017.

  • Nir, Manjinder, Ashraf Matrawy, and Marc St-Hilaire. "Economic and energy considerations for resource augmentation in mobile cloud computing." IEEE Transactions on Cloud Computing 6, no. 1 (2015): 99-113.

  • Abdelfattah, Amr S., Tamer Abdelkader, and EI-Sayed M. EI-Horbaty. "RAMWS: Reliable approach using middleware and WebSockets in mobile cloud computing." Ain Shams Engineering Journal 11, no. 4 (2020): 1083-1092.

  • Nawrocki, Piotr, and WojciechReszelewski. "Resource usage optimization in mobile cloud computing." Computer Communications 99 (2017): 1-12.

  • Aliyu, Ahmed, Abdul Hanan Abdullah, OmprakashKaiwartya, Syed Hamid HussainMadni, Usman Mohammed Joda, Abubakar Ado, and Muhammad Tayyab. "Mobile cloud computing: taxonomy and challenges." Journal of Computer Networks and Communications, 2020.

  • Garcia-Gonzalo, Esperanza, and Juan Luis Fernandez-Martinez. "A brief historical review of particle swarm optimization (PSO)." Journal of Bioinformatics and Intelligent Control 1, no. 1 (2012): 3-16.

  • Zhou, Chi, H. B. Gao, Liang Gao, and W. G. Zhang. "Particle Swarm Optimization (PSO) Algorithm [J]." Application Research of Computers 12 (2003): 7-11.

  • Sayed, Gehad Ismail, GhadaKhoriba, and Mohamed H. Haggag. "A novel chaotic salp swarm algorithm for global optimization and feature selection." Applied Intelligence 48, no. 10 (2018): 3462-3481.

  • Tamilselvan, Latha. "Client Aware Scalable Cloudlet to Augment Edge Computing with Mobile Cloud Migration Service." iJIM 14, no. 12 (2020): 165.

  • Ishibuchi, Hisao, and Tadahiko Murata. "Multi-objective genetic local search algorithm."In Proceedings of IEEE international conference on evolutionary computation, pp. 119-124.IEEE, 1996.

  • Sivanandam, S. N., and S. N. Deepa. "Genetic algorithm optimization problems."In Introduction to genetic algorithms, pp. 165-209.Springer, Berlin, Heidelberg, 2008.


Abstract Views: 335

PDF Views: 2




  • Energy Efficient Data Transfer in Mobile Cloud Computing Environment Using Particle-Salp Swarm Optimization Technique

Abstract Views: 335  |  PDF Views: 2

Authors

C. T. K. Amarnath
Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India
S. K. Mahendran
Department of Computer Science, Government Arts College, Udhagamandalam, Tamil Nadu, India

Abstract


Mobile cloud computing (MCC) can request the service of cloud from the mobile appliance, such as mobile phones, laptops, palm tops and so on for data transfer in such a way to provide more beneficial applications. The consumption of energy is a major problem during the transfer of data in MCC that is needed to be optimized with the allocation of the tasks to mobile or cloud environment in an efficient manner. In order to deal this issue, a hybrid particle-salp (PS-SALP) swarm optimization technique is proposed in this research that incorporates the characteristic features of the particles and the salps leading to better convergence to enhanced solution in the resource allocation of data transfer. The main aim of this paper is to minimize the utility cost (UC) representing a better balance between energy consumption and the period of execution of the task. Initially, the local optimal solutions for each problem is found, followed by which the global optimal solution is obtained using the proposed PS-SALP optimization algorithm. The performance of the proposed technique of data transfer in MCC is analyzed in terms of the metrics, such as UC, energy consumption, and the task execution time. The results show the superiority of the proposed technique in energy efficient data transfer in the MCC environment.

Keywords


Mobile Cloud Computing, Utility Cost, Optimization, Energy Consumption, Data Transfer, PS-SALP.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F209189