AI-assisted virtualization software: Difference between revisions

Revision as of 01:00, 5 July 2023

Revision as of 01:28, 6 August 2023

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== Functionality ==

AI-assisted virtualization software operates by leveraging AI techniques such as [[machine learning]], [[deep learning]], and [[neural network]]s to make more accurate predictions and decisions regarding the management of virtual environments. Key features include intelligent automation, predictive analytics, and dynamic resource allocation.<ref>{{Cite journalbook |last1=Sharma |first1=Sachin |last2=Nag |first2=Avishek |last3=Cordeiro |first3=Luis |last4=Ayoub |first4=Omran |last5=Tornatore |first5=Massimo |last6=Nekovee |first6=Maziar |datetitle=2020-11-23Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies |titlechapter=Towards explainable artificial intelligence for network function virtualization |date=2020-11-23 |chapter-url=http://dx.doi.org/10.1145/3386367.3431673 |journal=Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies |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>

* 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.

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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>

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 journalbook |last1=Jagannath |first1=Jithin |last2=Ramezanpour |first2=Keyvan |last3=Jagannath |first3=Anu |datetitle=Proceedings of the 2022-05-16 ACM Workshop on Wireless Security and Machine Learning |titlechapter=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 |journal=Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning |pages=81–86 |___location=New York, NY, USA |publisher=ACM |doi=10.1145/3522783.3529519|isbn=9781450392778 |s2cid=247957748 }}</ref>

== Challenges ==