d8bbc385-8906-4beb-b882-b8e85857aee120211126064009184naun: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, Khorasan Razavi,Neyshabur,Science and Research branch,Islamic Azad University, Neyshabur,IranTo migrate to cloud computing or other new generation of computational and communicational network we should making some laws about limitations on such networks. We should clarify clearly the level of accessions to resources and/or the ability to sending or receiving files through such network. We can avoid unpredicted and undesired works by using of law makings. Furthermore, besides of law making we can use evolutionary and/or intelligence techniques for estimation and prediction about parameters that deal with controlling of our network – for example in this paper we introduce one type of such intelligence algorithms which named as unscented Kalman filter. By giving some raw inputs about treatments and resources of such networks we’re able to estimate and predict about lots of different conditions of these networks. For example, we should have an estimation and prediction algorithm to be able to track and trace the hackers or crackers, if they’re permeating to our network. Or we should estimate the amount of users that will use particular software or application on a specific hour through such network. Also, if we study users treatment on a specific network, like cloud computing, we’re able to estimate and predict their keen and eagerness about new released software or application; because if we know how people treat and face and react with new emerging software we’re able to construct and program our applications more precisely. In this paper, at first we tell about cloud computing – we define it completely and discuss about all aspects of this new generation of internet – also in the first section we discuss about efficiency of cloud computing in saving of energy and tell differences between the amount of energy that cloud resources use and the amount of energy that other computerized frames were use. We proof our suggestions and ideas by the means of analytical and mathematical analysis; after that when we understand about all of its concepts, a brief review about Microsoft decisions on cloud computing were present, we review new systems and software’s of Microsoft that equipped with cloud computing and study the advantages and disadvantages of these new services which are equipped with cloud computing. In the next section by the knowing of whole concepts and key features of this network and also by knowing the critical uses of this network on different industries we introduce fixedinterval smoothers; which can be used for estimation and prediction of different parameters in such network. For example, we can use this for estimation and prediction about permeating of hackers or for predicting the amount of users that will use special part of our network in a particular period of time or by tracking and tracing the packets or even users. By performing such activities we’re able to eliminate malicious and spyware treatments from the beginning points of our network. Also by the means of mathematical analysis in this paper, we demonstrate all of our suggestions and according to these proofs we conclude about workability and liability of such network.112620211126202110311017https://www.naun.org/main/NAUN/energyenvironment/2021/a342011-017(2021).pdf10.46300/91012.2021.15.17https://www.naun.org/main/NAUN/energyenvironment/2021/a342011-017(2021).pdfmohamadreza 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://www.cs.unc.edu 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.