An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

Saravanakumar, C.; Priscilla, R.; Prabha, B.; Kavitha, A.; Prakash, Mishra Jyoti; Arun, C.

doi:10.32604/csse.2022.022122

Cited by 12 publications

(6 citation statements)

References 18 publications

(3 reference statements)

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“…An organization may more effectively build long-term goals to improve its operations and concurrently respond with intelligence to events that occur as a result of putting real-time business intelligence (BI) into action inside the organization. Have examined how business intelligence affects small and medium sized businesses along with the benefits and problems associated with it in [10]. Utilizing information effectively is one of the most important success criteria for businesses.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Approach for Maximizing Business Intelligence using Machine Learning

Afsaruddin,

Agrawal,

Nayak

2023

IJRITCC

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The subject of study known as business intelligence is responsible for the development of techniques and tools for the analysis of business information with the goal of assisting in the management and decision-making processes of corporations. In the current climate, business intelligence is essential to the process of formulating a strategy and carrying out operations that are data-driven. Throughout the many stages of the company operation, an organization will need assistance evaluating data and making decisions; a decision support system may provide this assistance by including business intelligence as an essential component. The fact that this enormous quantity of data is distributed over a number of different types of platforms, however, makes it a difficult challenge, in particular to understand the information that is actually relevant and to make efficient use of it for business intelligence. One of the most important challenges facing modern society is maximizing business intelligence through the application of machine learning. It offers a full analysis that is based on predictions and is extracted for Business Intelligence techniques along with current application fields. This anomalous gap has been pointed up, and solutions and future research areas have been offered to overcome it in order to create effective business strategies.

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Section: Literature Reviewmentioning

confidence: 99%

An Optimized Approach for Maximizing Business Intelligence using Machine Learning

Afsaruddin,

Agrawal,

Nayak

2023

IJRITCC

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“…e study was carried out using WEKA ML equipment and an actual database from the American corporation Orange. Saravana Kumar et al [12] suggested a CCP technique that employs soft voting and SACtacking models via an ensemble learning method. Machine learning methods Xgboost, LR, DT and NB are utilised to build a two-stage stacking approach, with the three outputs of the succeeding level used for soft voting.…”

Section: Existing Ccp Models For Telecommunication Sectormentioning

confidence: 99%

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets

Banu

Neelakandan

Geetha

et al. 2022

Computational Intelligence and Neuroscience

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The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommunications industry. Because customer churns have a direct impact on a company's total revenue, telecommunications firms have begun to develop 76 models to reduce churns at an earlier stage. Previous research has revealed that AI and ML models are effective CCP solutions. According to this viewpoint, this study proposes a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM). The AICCP-TBM model's purpose is to control the existence of churners and non-churners in the telecom sector. The proposed AICCP-TBM model employs a Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) method for the best feature assortment. In addition, a Fuzzy Rule-based Classifier(FRC) is used to distinguish between client churners and non-churners. A technique known as Quantum Behaved Particle Swarm Optimization (QPSO) is used to pick the membership functions for the FRC model in order to improve the classification performance of the FRC model. The performance of the AICCP-TBM model is validated using a benchmark CCP dataset and the experimental results are reviewed from several angles. In relations of presentation, the imitation consequences demonstrated that the AICCP-TBM model surpassed the most recent state-of-the-art CPP models. The suggested AICCP-TBM method's comparative accuracy was thoroughly tested on the three datasets used. Using datasets 1-3, this technique obtained better levels of accuracy, with the maximum attainable values being 97.25 %, 97.5 % and 94.33 %. The simulation results for the AICCP-TBM model demonstrated improved prediction performance.

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“…Through the course of our research, we were able to distinguish and categorize the most prevalent types of virtualization, as depicted in Table 5 and illustrated in Figure 7. These types broadly encompass virtualization of servers, networks, storage, data, desktops, and applications (Li et al, 2022;Liu., 2021;Nazarov et al, 2020;Park et al, 2021;Pons, et al, 2022;Qazi, 2020;Radchenko et al, 2019;Rosioru et al, 2022;Saravanakumar et al, 2022). The diversity in types of virtualization underscores the broad applicability and immense potential of this technology in transforming various aspects of computing and information technology.…”

Section: Current Types Of Virtualizationmentioning

confidence: 99%

Virtualization Development Trends: Scoping Review

Aseeva,

Yukhnin,

Uvarov

et al. 2022

РИК

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Introduction: The development of virtualization tools, which rapidly unfolded in the 1990s, continues to demonstrate a dynamic trajectory. Modern solutions in this area have significantly evolved and diversified. The relevance of the stated issues in the era of Covid-19 has received an even deeper interpretation.Purpose: This review analyzes articles on virtualization published during the pandemic years with the aim of identifying virtualization development trends.Materials and Methods: We conducted a keyword search in the Scopus database. The focus was on articles published in English from December 2019 to December 2022. The authors followed an expanded PRISMA-ScR protocol. The search identified 164 articles, of which 131, based on their title and abstract, were marked as relevant.Results: The selected articles were classified by year of publication, type of publication, country of publication, area of virtualization, mention of Covid-19, and type of virtualization. The articles included in our review indicate that virtualization had already been integrated into all spheres of life before Covid-19. However, the pandemic caused a surge, which accelerated its implementation in various areas, ranging from education and medicine to smart cities and computer technologies.Conclusion: The presented review of the subject area allowed us to identify sectors of public life that significantly influenced virtualization during the Covid19 years, as well as the most actively developing forms of virtualization, in particular, those that allow segregating computational processes and resources. It was recorded that modern solutions in the field of virtualization have significantly diversified compared to previous years and can be classified into separate categories.

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An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

Cited by 12 publications

References 18 publications

An Optimized Approach for Maximizing Business Intelligence using Machine Learning

An Optimized Approach for Maximizing Business Intelligence using Machine Learning

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets

Virtualization Development Trends: Scoping Review

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