621caf24-2204-447f-a914-69f7a5e7c60420211126064015317naun:naunmdt@crossref.orgMDT DepositInternational Journal of Energy and Environment2308-100710.46300/91012http://www.naun.org/cms.action?id=3043324202132420211510.46300/91012.2021.15https://www.naun.org/cms.action?id=23309MohamadrezaMohamadzadehDepartment of Electronic Engineering, Science and Research branch,Islamic Azad University, Neyshabur,IranThese days’ lots of technologies migrate from traditional systems into cloud and similar technologies; also we should note that cloud can be used for military and civilian purposes [3]. On the other hand, in such a large scale networks we should consider the reliability and powerfulness of such networks in facing with events such as high amount of users that may login to their profiles simultaneously, or for example if we have the ability to predict about what times that we would have the most crowd in network, or even users prefer to use which part of the Cloud Computing more than other parts – which software or hardware configuration. With knowing such information, we can avoid accidental crashing or hanging of the network that may be cause by logging of too much users. In this paper we propose Kalman Filter that can be used for estimating the amounts of users and software’s that run on cloud computing or other similar platforms at a certain time. After introducing this filter, at the end of paper, we talk about some potentials of this filter in cloud computing platform. In this paper we demonstrate about how we can use Kalman filter in estimating and predicting of our target, by the means of several examples on Kalman filter. Also at the end of paper we propose information filter for estimation and prediction about cloud computing resources.112620211126202111512119https://www.naun.org/main/NAUN/energyenvironment/2021/a382011-019(2021).pdf10.46300/91012.2021.15.19https://www.naun.org/main/NAUN/energyenvironment/2021/a382011-019(2021).pdfDarbandi “Applying Kalman Filtering in solving SSM estimation problem by the means of EM algorithm with considering a p ractical example”; published by the Journal of Computing – Springer, 2012; USA. http://www.cs.unc.edu mohamadreza mohamadzadeh '' An overview of the effects of processing on C loud Computing dramatic present and provide new security solutions ''; published by the life science journal - marsland press, 2013; USA http://info.acm.org/pubs/toc/CRnotice.html Microsoft’s Accessible Technology Vision and Strategy; September 2011. www.wikipedia.org. 10.1109/34.387503C.G. Atkeson and J.M. Hollerbach. 1985. “Kinematic features of unrestrained vertical arm movements,” Journal of Neuroscience, 5:2318-2330. Ali Azarbayejani and Alex Pentland. June 1995. “Recursive Estimation of Motion, Structure, and Focal Length,” IEEE Trans. Pattern Analysis and Machine Intelligence, June 1995, 17(6). Ronald Azuma and Mark Ward. 1991. “Space- Resection by Collinearity: Mathematics Behind the Optical Ceiling Head-Tracker,” UNC Chapel Hill Department of Computer Science technical report TR 91-048 (November 1991). 10.1145/192161.192199Ronald Azuma and Gary Bishop. 1994. “Improving Static and Dynamic Registration in an Optical See-Through HMD,” SIGGRAPH Conference Proceedings, Annual Conference Series, pp. 197-204, ACM SIGGRAPH, Addison Wesley, July 1994. ISBN0-201-60795-6. Ronald Azuma. 1995. “ Predictive Tracking for Augmented Reality,” Ph.D. dissertation, University of North Carolina at Chapel Hill, TR95-007. 10.1109/tpami.1986.4767755Ted J. Broida and Rama Chellappa. 1986. “Estimation of object motion parameters from noisy images,” IEEE Trans. Pattern Analysis and Machine Intelligence, January 1986, 8(1), pp. 90-99. R. G. Brown and P. Y. C. Hwang. 1992. Introduction to Random Signals and Applied Kalman Filtering, 2nd Edition, John Wiley & Sons, Inc.