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A Co-operative Fog-based Load Balance (CFBLB) Strategy

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Mary M. Fouad, Ahmed I. Saleh, Mohamed F. EL-Rahamawy

Mary M Fouad, Ahmed I Saleh and Mohamed F EL-Rahamawy. A Co-operative Fog-based Load Balance (CFBLB) Strategy. International Journal of Computer Applications 177(41):15-21, March 2020. BibTeX

	author = {Mary M. Fouad and Ahmed I. Saleh and Mohamed F. EL-Rahamawy},
	title = {A Co-operative Fog-based Load Balance (CFBLB) Strategy},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2020},
	volume = {177},
	number = {41},
	month = {Mar},
	year = {2020},
	issn = {0975-8887},
	pages = {15-21},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2020919764},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The most factor affects the performance of fog computing is load balancing which means resource management, it is significant to get a satisfying implementation of fog computing. The existing algorithms of LB in a fog computing environment are not extremely active. today, to predict the user requests arrivals on the fog manager is not possible so Load balancing is a complex mission. Each machine has different characteristics, So the job scheduling process among nodes turns into a very hard process Lately, load balancing is the main goal to many researchers in fog computing which means resource management. that produce many algorithms to achieve this goal such as dynamic Algorithm, the proposed algorithm used to improve load balance model for the fog. This algorithm is being introduced to improve resource utilization and response time in the fog environment, which applies fuzzy inference with load scheduling that takes advantage of Fuzzy Logic. by using java The output produced refinement on resource utilization and processing time.


  1. Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems.
  2. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. The University of Maryland at College Park.
  3. Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  4. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  5. Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398.,The University of Washington.
  6. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
  7. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
  8. Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
  9. W. Pedrycz and F. Gomide, “An introduction to fuzzy sets: analysis and design”, complex adaptive systems. MIT Press, (1998).
  10. J. J. Buckley, E. Eslami, and E. Esfandiar.” An introduction to fuzzy logic and fuzzy sets” advances in soft computing, Physica Verlag,( 2002).
  11. K. Dasgupta, B. Mandal, P. Dutta, “Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach”, Elsevier (C3IT) 2012.
  12. International Journal of Computer Applications (0975 – 8887) Volume 69– No.17, May 2013


Load balancing, fog computing, Fuzzy inference, Virtualization.