Fog computing is a developing area that offers numerous potential advantages to different organizations and common client. It is the extended type of distributed processing. It depends on on-demand-service model in which data, infrastructure, software and different service model are given according to the customer requirement at some instance of time. Load balancing is utilized to distribute workload among various cloud systems or node to get full resource utilization. It is the prominent means to achieve efficient resource sharing and utilization. Load balancing has turned into a challenge issue now in distributed computing frameworks. To meets the clients enormous number of requests, there is a need of distributed solution to handle client request concurrently. Servers can`t be relegated to specific customers individually. Distributed computing involves a large network and different component that are available throughout the wide area. Hence, we design distributed concurrent model which handle multiple client request and reduce the load on the cloud and improve the performance. This research work is performed on student course registration in a University which shows how distributed concurrent models works in Fog when multiple request that are send by client. Our proposed Approach is use to complete tasks within deadline, efficient resource utilizations, fulfilling data consistency constraints.

Fog Computing; Dsitributed model; Course Registration; Resource Utilization; Concurrency; Load Balancin;MobileComputing

[1] P. Jakovits, S. N. Srirama, I. Kromonov, “Stratus: A Distributed Computing Framework for Scientific Simulations on the Cloud”, 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, Liverpool, pp. 1053-1059, 2012.
[2] P. Morakos, A. Meliones, “Design and implementation of a Cloud SaaS framework for Multi-Tenant applications”, The 5th International Conference on Information Intelligence Systems and Applications, Chania, pp. 273-278, 2014.
[3] L. Ferretti, C. Michele, M. Mirco, “Distributed, concurrent, and independent access to encrypted cloud databases.” IEEE transactions on parallel and distributed systems, Vol.25, Issue.2, pp.437-446, 2014.
[4] HS Mahalle, PR Kaveri, V Chavan, “LoadBalancing On Cloud Data Centres” in International Journal of Advanced Research in Computer Science and Software Engineering, Vol.3, Issue.1, pp.1-9, 2013.
[5] Rachna Jain, Sushila Madan, Bindu Garg, “Analyzing Various Existing Security Techniques to Secure Data Access in Cloud Environment”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.130-135, 2015.
[6] R.S Sajjan, R.Y. Biradar, “Load Balancing and its Algorithms in Cloud Computing: A Survey”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.95-100, 2017.
[7] R.P. Steffi, S. Sundaramoorthy , “Top-K Spatial Preference Query with Range Based Skyline Query in Mobile Environment”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.46-50, 2014.
[8] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica., “A view of cloud computing”, Comm. of the ACM, vol. 53, no. 4, pp. 50-58, 2010.
[9] L.F. Bittencourt, E.R.M. Madeira, N.L.S. Da, “Scheduling in hybrid clouds”,IEEE Communications Magazine, vol.50, no.9, pp. 42-47, 2012.
[10] Pranay Kujur, Kiran Gautam, “Smart Interaction of Object on Internet of Things”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.15-19, 2015.
[11] M. Satyanarayanan, P. Bahl, R. Caceres, N. Davies, “The case for vm-based cloudlets in mobile computing”, IEEE Pervasive Computing, Vol.8, No.4, pp. 14-23, 2009.
[12] Y. Zhang, Y. Zhou, “Transparent Computing: a New Paradigm for Pervasive Computing”, in Ubiquitous Intelligence and Computing, Berlin, pp. 1-11, 2006.
[13] M. S. Laghari, “Automated Course Advising System”, International Journal of Machine Learning and Computing, Vol.4, No.1, pp.23-31, 2014.
[14] Z. Xu, X. Hou, J. Sun, “Ant algorithm-based task scheduling in grid computing”, thm-based task scheduling in grid computing”, presented at Electrical and Computer Engineering IEEE CCECE, CanadA, pp. 1107- 1110, 2003.
[15] L. Chen, “Resource Scheduling Based on Ant Colony Optimization Algorithm in Grid Computing Environments”, Information Technology Journal, Vol. 12, No. 24, pp. 8010-8014, 2013.
[16] V.R.T. Kanakala, K.P. Kumar, S. Kavitha, “A Hybrid Heuristic Algorithm to Enhance Load balancing in Cloud Environment”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.129-132, 2016.
[17] S. Kashyap, V. Jaydeep, “A Survey Of Various Load Balancing Algorithms In Cloud Computing”, International Journal Of Scientific & Technology Research vol. 3, no. 11, pp.115-119, 2014.
[18] R. Bhaskar, “Dynamic Allocation Method For Efficient Load Balancing In Virtual Machines For Cloud Computing Environment”, Advanced Computing: An International Journal, vol. 3, no. 5, pp. 53-61, 2012.