The convergence between artificial intelligence (AI) and cloud computing has revolutionized information technology (IT), since AI makes it possible to perform tasks automatically, consisting of the union of several technological tools, which allows a computer vision and processing. more sophisticated to improve the ability to generate large amounts of data and thus create more accurate and accurate forecasts. AI in cloud computing (with all the properties and characteristics of this technology) is the technology that enables robots to operate intelligently and to respond to commands and perform logical activities previously limited to humans, as this combination creates an intelligent cloud.
Published in Chapter:
Lower Memory Consumption for Data Transmission in Smart Cloud Environments With CBEDE Methodology
Reinaldo Padilha França (State University of Campinas (UNICAMP), Brazil), Yuzo Iano (State University of Campinas (UNICAMP), Brazil), Ana Carolina Borges Monteiro (State University of Campinas (UNICAMP), Brazil), and Rangel Arthur (State University of Campinas (UNICAMP), Brazil)
Copyright: © 2020
|Pages: 22
DOI: 10.4018/978-1-7998-2112-0.ch011
Abstract
Smart telecoms will deliver lasting improvements to business productivity and enduring consumer benefits that raise the quality of life by enabling telecommuting, telemedicine, entertainment, access to e-government, and a wealth of other online services. And we'll need next-generation digital platforms on which telecom providers can create and deliver all kinds of services. Therefore, this chapter develops a method of data transmission based on discrete event concepts. This methodology was named CBEDE. Using the MATLAB software, the memory consumption of the proposed methodology was evaluated, presenting the great potential to intermediate users and computer systems, ensuring speed, low memory consumption, and reliability. With the differential of this research, the use of discrete events applied in the physical layer of a transmission medium, the bit itself, being this to low-level of abstraction, the results show better computational performance related to memory utilization, showing an improvement of up to 79.89%.