AI-assisted virtualization software

Last updated

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.

Contents

History

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

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]

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

Related Research Articles

<span class="mw-page-title-main">Computer science</span> Study of computation

Computer science is the study of computation, information, and automation. Computer science spans theoretical disciplines to applied disciplines.

<span class="mw-page-title-main">IBM Db2</span> Relational model database server

Db2 is a family of data management products, including database servers, developed by IBM. It initially supported the relational model, but was extended to support object–relational features and non-relational structures like JSON and XML. The brand name was originally styled as DB2 until 2017, when it changed to its present form.

<span class="mw-page-title-main">Analytics</span> Discovery, interpretation, and communication of meaningful patterns in data

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

<span class="mw-page-title-main">Edge computing</span> Distributed computing paradigm

Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data. More broadly, it refers to any design that pushes computation physically closer to a user, so as to reduce the latency compared to when an application runs on a centralized data centre.

Employee scheduling software automates the process of creating and maintaining a schedule. Automating the scheduling of employees increases productivity and allows organizations with hourly workforces to re-allocate resources to non-scheduling activities. Such software will usually track vacation time, sick time, compensation time, and alert when there are conflicts. As scheduling data is accumulated over time, it may be extracted for payroll or to analyze past activity. Although employee scheduling software may or may not make optimization decisions, it does manage and coordinate the tasks. Today's employee scheduling software often includes mobile applications. Mobile scheduling further increased scheduling productivity and eliminated inefficient scheduling steps. It may also include functionality including applicant tracking and on-boarding, time and attendance, and automatic limits on overtime. Such functionality can help organizations with issues like employee retention, compliance with labor laws, and other workforce management challenges.

Artificial intelligence (AI) has been used in applications throughout industry and academia. Similar to electricity or computers, AI serves as a general-purpose technology that has numerous applications. Its applications span language translation, image recognition, decision-making, credit scoring, e-commerce and various other domains. AI which accommodates such technologies as machines being equipped perceive, understand, act and learning a scientific discipline.

Business transaction management (BTM), also known as business transaction monitoring, application transaction profiling or user defined transaction profiling, is the practice of managing information technology (IT) from a business transaction perspective. It provides a tool for tracking the flow of transactions across IT infrastructure, in addition to detection, alerting, and correction of unexpected changes in business or technical conditions. BTM provides visibility into the flow of transactions across infrastructure tiers, including a dynamic mapping of the application topology.

<span class="mw-page-title-main">Samsung SDS</span> South Korean IT company and subsidiary of Samsung Group

Samsung SDS Co., Ltd., Established in 1985 as a subsidiary of Samsung Group, is a provider of Information Technology (IT) services, including consulting, technical, and outsourcing services. SDS is also active in research and development of emerging IT technologies such as Artificial Intelligence (AI), Blockchain, Internet of Things (IoT) and outsourcing in engineering. In 2019, Samsung SDS reported a net profit of 750.4 billion won, an increase of 17.5% year-on-year. The company is estimated to have the 11th most valuable brand among global IT service companies, at US$3.7 billion as of January 2020. Samsung SDS has headquarters in South Korea and eight other overseas subsidiaries, one in America, Asia-Pacific, China, Europe, Latin America, Middle East, India, and Vietnam.

Dynamic Infrastructure is an information technology concept related to the design of data centers, whereby the underlying hardware and software can respond dynamically and more efficiently to changing levels of demand. In other words, data center assets such as storage and processing power can be provisioned to meet surges in user's needs. The concept has also been referred to as Infrastructure 2.0 and Next Generation Data Center.

<span class="mw-page-title-main">Computational sustainability</span>

Computational sustainability is an emerging field that attempts to balance societal, economic, and environmental resources for the future well-being of humanity using methods from mathematics, computer science, and information science fields. Sustainability in this context refers to the world's ability to sustain biological, social, and environmental systems in the long term. Using the power of computers to process large quantities of information, decision making algorithms allocate resources based on real-time information. Applications advanced by this field are widespread across various areas. For example, artificial intelligence and machine learning techniques are created to promote long-term biodiversity conservation and species protection. Smart grids implement renewable resources and storage capabilities to control the production and expenditure of energy. Intelligent transportation system technologies can analyze road conditions and relay information to drivers so they can make smarter, more environmentally-beneficial decisions based on real-time traffic information.

<span class="mw-page-title-main">Converged infrastructure</span> Way of structuring an IT system

Converged infrastructure is a way of structuring an information technology (IT) system which groups multiple components into a single optimized computing package. Components of a converged infrastructure may include servers, data storage devices, networking equipment and software for IT infrastructure management, automation and orchestration.

The fields of marketing and artificial intelligence converge in systems which assist in areas such as market forecasting, and automation of processes and decision making, along with increased efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems, computer programs that process input and provide valuable output for marketers.

Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision, human–computer interaction, dialog and narrative generation, among other technologies.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

<span class="mw-page-title-main">Apache SINGA</span> Open-source machine learning library

Apache SINGA is an Apache top-level project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.

The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML.

Intelligent transformation is the process of deriving better business and societal outcomes by leveraging smart devices, big data, artificial intelligence, and cloud technologies. Intelligent transformation can facilitate firms in gaining recognition from external investors, thereby enhancing their market image and attracting larger consumers who are more eager to collaborate. Conversely, intelligent transformation can foster the development of more interactive and multidimensional value-creation models while optimizing the conventional organizational model.

Artificial Intelligence for IT Operations (AIOps) is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics. AIOps is the acronym of "Artificial Intelligence Operations". Such operation tasks include automation, performance monitoring and event correlations among others.

<span class="mw-page-title-main">ModelOps</span>

ModelOps, as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models". "ModelOps lies at the heart of any enterprise AI strategy". It orchestrates the model lifecycles of all models in production across the entire enterprise, from putting a model into production, then evaluating and updating the resulting application according to a set of governance rules, including both technical and business key performance indicators (KPI's). It grants business domain experts the capability to evaluate AI models in production, independent of data scientists.

References

  1. 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.
  2. 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.
  3. 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.
  4. Anwar, Mohd. Sadique Shaikh (2018). Bigdata and Business Virtualization. ISBN   978-6139872022.
  5. 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.
  6. 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.