A real-time virtual machine for task placement in loosely-coupled computer systems

Elsedfy, Mohamed O.; Murtada, Wael A.; Abdulqawi, Ezz F.; Gadallah, Mahmoud

doi:10.1016/j.heliyon.2019.e01998

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…When applications running inside virtual machines have real-time restrictions, threads that deploy virtual cores must be programmed in a predictable way over physical cores. Meanwhile, reference [17] states that real-time virtual machines are suitable for tightly coupled computer systems wherein tasks are executed from the associated language. Here is an approach to support the transfer of tasks between freely attached computers in a real-time environment to add more features without updating the software.…”

Section: Virtualization Systemsmentioning

confidence: 99%

Testing of a Virtualized Distributed Processing System for the Execution of Bio-Inspired Optimization Algorithms

2020

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Due to the stochastic characteristics of bio-inspired optimization algorithms, several executions are often required; then a suitable infrastructure must be available to run these algorithms. This paper reviews a virtualized distributed processing scheme to establish an adequate infrastructure for the execution of bio-inspired algorithms. In order to test the virtualized distributed system, the well known versions of genetic algorithms, differential evolution and particle swarm optimization, are used. The results show that the revised distributed virtualized schema allows speeding up the execution of the algorithms without altering their result in the objective function.

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Section: Virtualization Systemsmentioning

confidence: 99%

Testing of a Virtualized Distributed Processing System for the Execution of Bio-Inspired Optimization Algorithms

2020

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Virtual Computer Systems in AI-Powered Music Analysis: A Comparative Study for Genre Classification and Musicological Investigations

Liu,

Dai

2023

J INFORM SYSTEMS ENG

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The convergence of artificial intelligence (AI) and music analysis in recent years has altered how humans perceive and analyze music. The purpose of this study was to investigate the effectiveness of virtual computer systems for AI-powered music analysis, as well as how they affect musicological insights and genre classification. The goal of the project was to uncover hidden patterns inside musical compositions while improving our understanding of genre features and underlying musical structures by fusing cutting-edge AI algorithms with the possibilities of virtualization technology. A quantitative study design with controlled experiments using standardized music datasets was used. Musical compositions of various styles were chosen, and relevant musical features such as melody, rhythm, and harmony were retrieved. Metrics for performance evaluation included genre categorization accuracy, precision, recall, and F1-score, as well as efficacy indicators for musicological investigations. The findings of the study shed light on the innovative possibilities of AI-driven music analysis. Across a range of musical genres, accurate genre classification was achieved, demonstrating the accuracy of AI models in identifying subtle genre traits. Deeper knowledge of musical works was aided by the discovery of complex melodic motifs, chord progressions, and rhythmic patterns through musicological research. By highlighting the synergies between AI techniques and virtual computer systems, this study contributes to the expanding landscape of AI-powered music analysis. It demonstrates AI's potential for automating hard activities, complementing musicological investigations, and providing insights that supplement human expertise. The study demonstrated the potential of AI-powered music analysis, but it also highlighted its shortcomings due to biases in training data, model overfitting, and resource restrictions in virtual systems. These limitations highlight the necessity of constant improvement and awareness when incorporating AI into musicology.

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A real-time virtual machine for task placement in loosely-coupled computer systems

Cited by 2 publications

References 20 publications

Testing of a Virtualized Distributed Processing System for the Execution of Bio-Inspired Optimization Algorithms

Testing of a Virtualized Distributed Processing System for the Execution of Bio-Inspired Optimization Algorithms

Virtual Computer Systems in AI-Powered Music Analysis: A Comparative Study for Genre Classification and Musicological Investigations

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