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International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.10, No.2, Feb. 2018

MLRTS: Multi-Level Real-Time Scheduling Algorithm for Load Balancing in Fog Computing Environment

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Author(s)

Mohamed A. Elsharkawey, Hosam E. Refaat

Index Terms

Cloud system;Fog computing;resource allocation;Real-Time Systems

Abstract

Cloud computing is an innovative technology which is based on the internet to preserve large applications. It is warehoused as a shared data over one platform. In addition, it offers better services to clients who belong to different organizations. In spite of the maximum utilization of computational resources provided by the cloud computing with lower cost, it suffers from specific restrictions. These restrictions are encountered through the load balancing of data in the cloud data centers. These restrictions are represented in the less bandwidth utilization, resource limitations, fault tolerance and security etc. In order to overcome these limitations, new computing model called Fog Computing is presented.  It aims to offer the required service of the sensitive data to end users without delaying. The function of the fog computing is similar to the cloud computing with two preferred advantages. The first one is that it is placed more near to the end users to introduce its service in less time. Secondly, it is more valuable for streaming the real time applications, sensor networks, IOT which need high speed and reliable internet connection.
In this paper, a novel load balancing algorithm has been proposed over a novel architectural model in the Fog Computing environment. The proposed model aims to serve the real-time tasks within their deadline. In addition, it serves the different soft tasks without starving. The soft tasks are classified according to the execution time and the priority levels. In addition, they are served according to their waiting time and priority-level. Furthermore, the proposed algorithm is employed to maximize the throughput, the resources and the network utilization and preserving the data consistency with less complexity to accomplish the end users demand.

Cite This Paper

Mohamed A. Elsharkawey, Hosam E. Refaat, " MLRTS: Multi-Level Real-Time Scheduling Algorithm for Load Balancing in Fog Computing Environment", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.2, pp. 1-15, 2018.DOI: 10.5815/ijmecs.2018.02.01

Reference

[1]Chandrasekhar S. Pawar, Rajnikant B. Wagh,‖ Priority Based Dynamic resource allocation in Cloud Computing with modified Waiting Queue‖, Proceeding of the IEEE 2013 International Conference on Intelligent System and Signal Processing(ISSP) Pages 311-316. 

[2]Yusen Li, Xueyan Tang, Wentong Cai,‖ Dynamic Bin packing for on demand cloud resource allocation ‖, Proceedings of the IEEE Transactions on Parallel and Distributed Systems ,2015,Paged 1-14. 

[3]Kamyab Khajehei, ―Role of virtualization in cloud computing‖, International Journal of Advance Research in Computer Science and Management Studies Volume 2,  Issue 4, April 2014.

[4]Savani Nirav M, Prof. Amar Buchade, ―Priority Based Allocation in Cloud Computing‖, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 IJERTV3IS051140 Vol. 3 Issue 5, May – 2014. 

[5]Brototi Mondala, Kousik Dasguptaa, Paramartha Duttab”Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach”, Elsevier, Procedia Technology 4(2012) pp. 783 – 789.

[6]Ivan Stojmenovic, sheng Wen, “The Fog Computing Paradigm: Scenarios and security issues” Proceedings of the IEEE International Fedrerated Conference on Computer Science and Information Systems, 2014, pp.1-8.

[7]Flavio Bonomi, Rodolfo Milito, Jiang Zhu, Sateesh Addepalli “Fog Computing and its Role in the internet of things”,http://conferences.sigcomm.org/sigcomm/2012/pa per/mcc/p13.pdf.

[8]Manisha Verma, Neelam Bhardwaj Arun Kumar Yadav,” An architecture for load balancing techniques for Fog computing environment”, International Journal of Computer Science and Communication, Vol. 8 • Number 2 Jan - Jun 2015 pp. 43-49.

[9]S. F. El-Zoghdy and S. Ghoniemy, “A Survey of Load Balancing In High-Performance Distributed Computing Systems”, International Journal of Advanced Computing Research, Volume 1, 2014.

[10]Mohsen and Hossein Delda, “Balancing Load in a   Computational Grid Applying Adaptive, Intelligent Colonies of Ants”, Informatica 32 (2008) 327–335.

[11]Antony Thomas, Krishnalal G, Jagathy Raj V P,”Credit Based Scheduling Algorithm in Cloud Computing Environment”, International Conference on Information and Communication Technologies, Procedia Computer Science 46(2014) 913-920.

[12]Dhinesh Babu L.D, P. Venkata Krishna,”Honey bee behavior inspired load balancing”, Elsevier, Applied Soft Computing 13(2013) 2292-2303. 

[13]Brototi Mondala, Kousik Dasguptaa, Paramartha Duttab”Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach”, Elsevier, Procedia Technology 4(2012) pp. 783 – 789.

[14]Atul Vikas Luthra and Dharmendra Kumar Yadav,”Multi- Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization”, International Conference on Intelligent, Communication & Convergence, Procedia Computer Science 48(2015) 107-113.

[15]Manisha Verma, Neelam Bhardwaj, and Arun Kumar Yadav, "Real Time Efficient Scheduling Algorithm for Load Balancing in Fog Computing Environment", International Journal of Information Technology and Computer Science, Vol.4, No.2, pp.1-10, 2016. DOI: 10.5815/ijitcs.2016.04.01

[16]Po-Huei Liang and Jiann-Min Yang,”Evaluation of two level global load balancing framework in Cloud Environment”, International Journal of Computer Science and Information Technology (IJCSIT), Vol. 7 No 2, April 2015.

[17]Mohamed A. Elsharkawey, Hosam E. Refaat,"CVSHR: Enchantment Cloud-based Video Streaming using the Heterogeneous Resource Allocation", International Journal of Computer Network and Information Security (IJCNIS), Vol.9, No.9, pp.1-11, 2017.DOI: 10.5815/ijcnis.2017.09.01

[18]M.Verma, N. Bhardwaj and A. Kumar,  "Real Time Efficient Scheduling Algorithm for Load Balancing in Fog Computing Environment",I.J. Information Technology and Computer Science, April, 2016, 4, 1-10

[19]Himani and Kamaljit Kaur,” Deadline Scheduling in Cloud Computing: A Review”, International Journal of Computer Applications(0975-8887),Vol. 96-No.24,june 2014.

[20]W. Chen and E. Deelman, ―Workflowsim: A toolkit for simulating scientific workflows in distributed environments, in 2012 IEEE 8th International Conference on E-Science, ser. eScience, 2012, pp. 1–8. [Online]. Available:https://github.com/WorkflowSim 

[21]R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, ―CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience, vol. 41, no. 1, 2011.

[22]Xiaofang Li, Yingchi Mao, Xianjian Xiao, "An improved Max-Min task-scheduling algorithm for elastic cloud", Computer, Consumer and Control (IS3C), 2014 International Symposium on

[23]B.Anju and C.Inderveer (2016), "Multilevel Priority-Based Task Scheduling Algorithm for Workflows in Cloud Computing Environment". In Proceedings of International Conference on ICT for Sustainable Development: Volume

[24]Swati Agarwal, Shashank Yadav, Arun Kumar Yadav,"An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.1, pp.48-61, 2016. DOI: 10.5815/ijieeb.2016.01.06