AI-assisted virtualization software
AI-assisted virtualization software is a type of technology that combines the principles of virtualization with advanced artificial intelligence (AI) algorithms. This software is designed to improve efficiency and management of virtual environments and resources. This technology has been used in cloud computing and for various industries.
History[edit]
Virtualization originated in Mainframe computers in the 1960s in order to divide system resources between different applications. The term has since broadened.[citation needed]
The use of AI in virtualization significantly increased in the early 2020s.[1]
Uses[edit]
AI-assisted virtualization software uses AI-related technology such as machine learning, deep learning, and neural networks to attempt to make more accurate predictions and decisions regarding the management of virtual environments. Features include intelligent automation, predictive analytics, and dynamic resource allocation.[2][3]
- Intelligent Automation: Automating tasks such as resource provisioning and routine maintenance. The AI learns from ongoing operations and can predict and perform necessary tasks autonomously.
- Predictive Analytics: Utilizing AI to analyze data patterns and trends, predicting future issues or resource requirements. It aids in proactive management and mitigation of potential problems.
- Dynamic Resource Allocation: Through the analysis of real-time and historical data, the AI system dynamically assigns resources based on demand and need, optimizing overall system performance and reducing wastage.
AI-assisted virtualization software has been used in cloud computing to optimize the use of resources and reduce costs. In healthcare, these technologies have been used to create virtual patient profiles. They are also used in data centers to improve performance and energy efficiency.[4] It has also been used in network function virtualization (NFV) to improve virtual network infrastructure.[5]
Implementing this type of software requires a high degree of technological sophistication and can incur significant costs. There are also concerns about the risks associated with AI, such as algorithmic bias and security vulnerabilities. Additionally, there are issues related to governance, the ethics of artificial intelligence, and regulations of AI technologies.[6]
See also[edit]
References[edit]
- ^ Haenlein, Michael; Kaplan, Andreas (August 2019). "A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence". California Management Review. 61 (4): 5–14. doi:10.1177/0008125619864925. ISSN 0008-1256. S2CID 199866730.
- ^ Sharma, Sachin; Nag, Avishek; Cordeiro, Luis; Ayoub, Omran; Tornatore, Massimo; Nekovee, Maziar (2020-11-23). "Towards explainable artificial intelligence for network function virtualization". Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies. New York, NY, USA: ACM. pp. 558–559. doi:10.1145/3386367.3431673. ISBN 9781450379489. S2CID 227154563.
- ^ Gilbert, Mazin, ed. (2019). Artificial intelligence for autonomous networks. Chapman & Hall/CRC artificial intelligence and robotics series. Boca Raton London New York: CRC Press, Taylor & Francis Group. ISBN 978-0-8153-5531-1.
- ^ Anwar, Mohd. Sadique Shaikh (2018). Bigdata and Business Virtualization. ISBN 978-6139872022.
- ^ Jagannath, Jithin; Ramezanpour, Keyvan; Jagannath, Anu (2022-05-16). "Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks". Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning. New York, NY, USA: ACM. pp. 81–86. doi:10.1145/3522783.3529519. ISBN 9781450392778. S2CID 247957748.
- ^ Rawat, Danda B.; Awasthi, Lalit K; Balas, Valentina Emilia; Kumar, Mohit; Samriya, Jitendra Kumar (2023). Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation. Scrivener Publishing LLC. ISBN 9781119904885.