Cloud Data

Cloud Computing: Surveys And Analysis

Cloud Computing: Surveys And Analysis – A New Project to Reduce Data Access and Increase Network Lifetime Using Fuzzy Criminal Search Ebola Optimization for WMSN

Open Access Policy Institutional Open Access Program Special Issues Guides Publication Process Research and Publication Ethics Article Processing Fees Awards References

Cloud Computing: Surveys And Analysis

Cloud Computing: Surveys And Analysis

All reproductions are made available for immediate use under an open worldwide license. No special permission is required to reuse all or part of the article published by, including figures and tables. In articles published under the open access Creative Common CC BY license, parts of the article may be reproduced without the express permission of the original article. For more information, please visit https:///openaccess.

Data Limitation Analysis And Mitigation Strategies In Cloud Computing

The special case represents the most research with the potential to have a major impact in the field. The report should be a substantial paper that covers a variety of methods or methods, sheds light on future research directions, and describes research practices.

Feature articles are submitted at the invitation or recommendation of scientific editors and must receive the approval of reviewers.

Editor’s Choice articles are based on recommendations from scientific editors of international journals. Editors select small articles recently published in journals that they believe will be of particular interest to readers or important in an area of ​​research. The aim is to provide a snapshot of some of the most exciting work published in the journal’s various research areas.

Written by Ahmed Hadi Ali AL-Jumaili Ahmed Hadi Ali AL-Jumaili Scilit Google Scholar View Publications 1, 2, Ravie Chandren Muniyandi Ravie Chandren Muniyandi Scilit Google Scholar View Publications 1, Mohammad Kamrul Hasan Mohammadrut Kammadrul org Google Scholar View Publications 1, *, Johnny Koh Siaw Paw Johnny Koh Siaw Paw Scilit Google Scholar View Publications 3 and Mandeep Jit Singh Mandeep Jit Singh Scilit Google Scholar View Publications 4

Beyond Cost Savings Assessing The Business Value Of Cloud Computing Adoption

Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Date mailed: January 16, 2023 / Date revised: February 3, 2023 / Date accepted: February 10, 2023 / Date published: March 8, 2023

Comparable code for energy management has significant challenges in performance such as execution time, computational complexity, and runtime and latency in energy monitoring, power generation, especially for consumer electronics, weather data, and centralized parallel processing and exploratory and predictive data mining in diagnostics. Due to these limitations, information management has become an important and critical research topic. To overcome these limitations, cloud-based information management techniques have been effective in energy management. This article examines the concept of a cloud computing architecture for energy monitoring that can be implemented at multiple levels in real time to improve monitoring and performance designed for different types of applications. Then, cloud solutions are discussed against the background of big data and integrated models such as Hadoop, Spark, Storm are briefly explained to analyze the progress, limitations and innovations. Key metrics of cloud usage, such as key data sampling, sampling, and analysis of big data competition, are modeled using relevant theories. Finally, a new design concept with cloud computing and finally some suggestions focusing on cloud computing infrastructure and methods of managing big data in real-time management that solve the problem of information search are presented electronically.

Cloud Computing: Surveys And Analysis

In the early stages of the development of monitoring technology, most monitoring systems were designed for specific devices [1, 2], and each system was cracked and isolated [3]. This is an island of information without information sharing and interaction, which is not useful for management and overall analysis of information management [4]. In addition, it is difficult to share the hardware of these monitoring systems, such as communication, computing and storage, resulting in waste of IT space [5]. Therefore, a unified management system created in the main control room has emerged that can operate amidst various monitoring data collected by different devices [6, 7]. In order to integrate the monitoring of different specifications into the centralized monitoring system, the State Grid Corporation of China has issued a series of regulations and communication protocols, and state monitoring centers for power transmission and electronic equipment have been established in various grid companies [8]. However, the current limitation of the monitoring device is that it can only send simple process data to the monitoring station, and the frequency of data collection is not high [9, 10]. However, with the popularity and use of high-speed fiber optic networks and wireless transmission in the electronics industry, future electronic devices will have information-receiving nature and become the perfect place to monitor the events of time from a larger area. Data centers for data integration and data sharing [11]. Therefore, the amount of data stored in the monitoring center will be similar in the future, and the data processing capacity of the current analysis will not be sufficient to meet the storage and processing of large amounts of data [12, 13]. Obviously, long serial operations cannot meet the requirements of big data [14, 15]. The analogical metaphor based on high-performance computers has always been responsible for many problems, including science and engineering [16]. There are also many deficiencies in the state’s data on energy resources [7]. In recent years, cloud computing has sprouted from parallel computing and developed very rapidly, and has many advantages that bring new ideas to the development of monitoring platforms that attract people’s education in the energy industry [2, 17], as shown in Fig. 1. According to regulatory research, most cloud computing platforms are now designed for the monitoring center and are based on a single Hadoop framework, which has certain limitations. Hadoop is good at batch processing of big data, but cannot accommodate more computational models in maintaining electronic products such as stream computing [18]. In particularly bad weather conditions (such as heavy weather), the status monitoring alarm for the power supply is activated [19].

Cloud Computing Stats And Trends To Know In 2023

The power distribution center is designed to support online monitoring technology that combines power management and control. In the future, electronic data processing will be sent to the control center. It is difficult for Data Acquisition with existing Control System and Power Management System (SCADA/EMS) to analyze big data [20]. State monitoring centers for electricity transmission and equipment changes have been established in many grid companies to monitor different products of manufacturers subject to centralized monitoring [2]. Although web service technology has reduced the complexity of information sharing, challenges remain in meeting the information needs of energy companies: electricity [14, 15].

Therefore, the monitoring system now receives information processed locally on the monitoring device. For example, monitoring equipment should make high-voltage electrical equipment part of the electrical discharge according to the number of discharges, maximum discharge volume, and connection stages before distribution [21, 22, 23]. Loading “known data” instead of “raw data” can reduce network transmission costs and monitor storage costs [24]. However, the monitoring center, which combines data from many specialized monitoring tools, continues to face failure in assessing the deep situation and achieving the goal [25, 26].

Massive research data will fill the remote electronic monitoring center, resulting in intensive storage, operation and analysis. Data analysis of electronic products has shown the key characteristics of big data: large volume, diverse types, rapid change and low cost intensity. Data analysis and analysis operations will be transferred from the distributed monitoring equipment to the central monitoring center, which will simplify the hardware and software configuration of monitoring equipment and facilitate the expansion of monitoring centers [27]. General Electric Company (GE), which oversees the operation of remote steam turbines, has moved from previous data processing and simple data transmission to the use of data memory and other technologies to retrieve and store raw data files [9]. The widespread use of fiber communications in the electronics industry provides a solution for transmitting large amounts of data [11].

The traditional use of data as a tool has faced problems due to current data growth. It is not possible to meet the needs of the workforce to quickly access information and knowledge from big data. Business information power is the research and use of major information technologies. It is an important requirement for the development of technology and skills. Data mining can be done by using key techniques to process a large amount of data and discover its hidden important information; This can realize the rapid transformation of data into information and then into value [28, 29, 30, 31, 32]. However, now the data volume is increasing rapidly and the traditional single-node serial mining method can no longer be used.

Kpmg’s 2014 Cloud Computing Survey: Enterprises Quickly Moving Beyond Cost Reduction To Customer Driven Results

The method and tool of cost analysis for cloud computing, cloud computing industry analysis, cloud computing for data analysis, enterprise and cloud computing, cloud computing market analysis, finite element analysis cloud computing, cloud computing risk analysis, analysis of cloud computing, cloud computing swot analysis, cloud computing cost analysis, cloud computing cost benefit analysis, cloud computing swot analysis ppt

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button