AI-assisted virtualization software: Difference between revisions

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== History ==

[[Virtualization]] originated in [[Mainframemainframe computer]]s in the 1960s in order to divide system resources between different applications. The term has since broadened.{{cn|date=May 2024}}

The use of AI in virtualization significantly increased in the early 2020s.<ref>{{Cite journal |last1=Haenlein |first1=Michael |last2=Kaplan |first2=Andreas |date=August 2019 |title=A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence |url=http://journals.sagepub.com/doi/10.1177/0008125619864925 |journal=California Management Review |language=en |volume=61 |issue=4 |pages=5–14 |doi=10.1177/0008125619864925 |s2cid=199866730 |issn=0008-1256|url-access=subscription }}</ref>

== FunctionalityUses ==

AI-assisted virtualization software uses AI-related technology such as [[machine learning]], [[deep learning]], and [[neural network]]s 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.<ref>{{Cite book |last1=Sharma |first1=Sachin |last2=Nag |first2=Avishek |last3=Cordeiro |first3=Luis |last4=Ayoub |first4=Omran |last5=Tornatore |first5=Massimo |last6=Nekovee |first6=Maziar |title=Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies |chapter=Towards explainable artificial intelligence for network function virtualization |date=2020-11-23 |chapter-url=http://dx.doi.org/10.1145/3386367.3431673 |pages=558–559 |___location=New York, NY, USA |publisher=ACM |doi=10.1145/3386367.3431673|isbn=9781450379489 |s2cid=227154563 }}</ref><ref>{{Cite book |title=Artificial intelligence for autonomous networks |date=2019 |publisher=CRC Press, Taylor & Francis Group |isbn=978-0-8153-5531-1 |editor-last=Gilbert |editor-first=Mazin |series=Chapman & Hall/CRC artificial intelligence and robotics series |___location=Boca Raton London New York}}</ref>

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* 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.

Furthermore, AI-assisted virtualization software has hadbeen notable contributionsused in cloud computing to optimize the fielduse of networkresources functionand virtualizationreduce (NFV)costs. ItIn hashealthcare, enabledthese atechnologies morehave dynamicbeen andused to flexiblecreate virtual networkpatient infrastructure,profiles. capableThey ofare auto-scalingalso basedused onin networkdata load,centers identifyingto potentialimprove threats,performance and autonomouslyenergy recoveringefficiency.<ref>{{Cite frombook faults|last=Anwar |first=Mohd. Sadique Shaikh |title=Bigdata and Business Virtualization |year=2018 |isbn=978-6139872022}}</ref> It has also been used in network function virtualization (NFV) to improve virtual network infrastructure.<ref>{{Cite book |last1=Jagannath |first1=Jithin |last2=Ramezanpour |first2=Keyvan |last3=Jagannath |first3=Anu |title=Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning |chapter=Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks |date=2022-05-16 |chapter-url=http://dx.doi.org/10.1145/3522783.3529519 |pages=81–86 |___location=New York, NY, USA |publisher=ACM |doi=10.1145/3522783.3529519|isbn=9781450392778 |s2cid=247957748 }}</ref>

== Impact and applications ==

AI-assisted virtualization software has had a profound impact on various sectors. It has revolutionized cloud computing by optimizing the use of resources and significantly reducing costs. In healthcare, the technology is used to create virtual patient profiles that can be easily accessed and updated, improving diagnosis and treatment. It is also used in data centers to improve performance and energy efficiency.<ref>{{Cite book |last=Anwar |first=Mohd. Sadique Shaikh |title=Bigdata and Business Virtualization |year=2018 |isbn=978-6139872022}}</ref>

Despite its many advantages, AI-assisted virtualization software is not without its challenges. 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.<ref>{{Cite book |last1=Rawat |first1=Danda B. |title=Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation |last2=Awasthi |first2=Lalit K |last3=Balas |first3=Valentina Emilia |last4=Kumar |first4=Mohit |last5=Samriya |first5=Jitendra Kumar |publisher=Scrivener Publishing LLC |year=2023 |isbn=9781119904885}}</ref>

Furthermore, AI-assisted virtualization has had notable contributions in the field of network function virtualization (NFV). It has enabled a more dynamic and flexible virtual network infrastructure, capable of auto-scaling based on network load, identifying potential threats, and autonomously recovering from faults.<ref>{{Cite book |last1=Jagannath |first1=Jithin |last2=Ramezanpour |first2=Keyvan |last3=Jagannath |first3=Anu |title=Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning |chapter=Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks |date=2022-05-16 |chapter-url=http://dx.doi.org/10.1145/3522783.3529519 |pages=81–86 |___location=New York, NY, USA |publisher=ACM |doi=10.1145/3522783.3529519|isbn=9781450392778 |s2cid=247957748 }}</ref>

== Challenges ==

Despite its many advantages, AI-assisted virtualization software is not without its challenges. 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, ethics, and regulations of AI technologies.<ref>{{Cite book |last1=Rawat |first1=Danda B. |title=Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation |last2=Awasthi |first2=Lalit K |last3=Balas |first3=Valentina Emilia |last4=Kumar |first4=Mohit |last5=Samriya |first5=Jitendra Kumar |publisher=Scrivener Publishing LLC |year=2023 |isbn=9781119904885}}</ref>

== Future prospects ==

As the fields of AI and virtualization continue to evolve, AI-assisted virtualization software is expected to become more advanced and integrated into an even wider range of applications. Future trends may include advanced self-healing systems, integration with quantum computing, and the development of more sophisticated AI models that can autonomously manage increasingly complex virtual environments.<ref>{{Cite book |date=2020 |editor-last=Hemanth |editor-first=Jude |editor2-last=Bhatia |editor2-first=Madhulika |editor3-last=Geman |editor3-first=Oana |url=http://dx.doi.org/10.1007/978-3-030-25797-2 |volume=32 |doi=10.1007/978-3-030-25797-2 |isbn=978-3-030-25796-5 |s2cid=241311770 |issn=2367-4512 |title=Data Visualization and Knowledge Engineering |series=Lecture Notes on Data Engineering and Communications Technologies }}</ref>

== See also ==

* [[Virtualization]]

* [[Artificial intelligence]]

== References ==

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[[Category:AI software]]

[[Category:Software]]

[[Category:Virtualization software]]

[[Category:Virtualization]]