CloudAISim: A toolkit for modelling and simulation of modern applications in AI-driven cloud computing environments

被引:1
|
作者
Bhowmik A. [1 ]
Sannigrahi M. [1 ]
Chowdhury D. [2 ,3 ]
Dey A. [4 ]
Gill S.S. [5 ]
机构
[1] SeaTech School of Engineering, University of Toulon, Toulon
[2] Center for Application Research in India (CARIn), Carl Zeiss (Bangalore) India Pvt Ltd., Bangalore
[3] Dept of Electronics and Communication Engineering, IIIT Naya Raipur, Naya Raipur
[4] TCS Research and Innovation, Kolkata
[5] School of Electronic Engineering and Computer Science, Queen Mary University of London, London
关键词
Artificial intelligence; Cloud computing; CloudAISim; Explainable AI; Healthcare; Machine learning; Simulation;
D O I
10.1016/j.tbench.2024.100150
中图分类号
学科分类号
摘要
There is a very significant knowledge gap between Artificial Intelligence (AI) and a multitude of industries that exist in today's modern world. This is primarily attributable to the limited availability of resources and technical expertise. However, a major obstacle is that AI needs to be flexible enough to work in many different applications, utilising a wide variety of datasets through cloud computing. As a result, we developed a benchmark toolkit called CloudAISim to make use of the power of AI and cloud computing in order to satisfy the requirements of modern applications. The goal of this study is to come up with a strategy for building a bridge so that AI can be utilised in order to assist those who are not very knowledgeable about technological advancements. In addition, we modelled a healthcare application as a case study in order to verify the scientific reliability of the CloudAISim toolkit and simulated it in a cloud computing environment using Google Cloud Functions to increase its real-time efficiency. A non-expert-friendly interface built with an interactive web app has also been developed. Any user without any technical knowledge can operate the entire model, which has a 98% accuracy rate. The proposed use case is designed to put AI to work in the healthcare industry, but CloudAISim would be useful and adaptable for other applications in the future. © 2024 The Authors
引用
收藏
相关论文
共 50 条
  • [21] Reinvent 4: Modern AI-driven generative molecule design
    Loeffler, Hannes H.
    He, Jiazhen
    Tibo, Alessandro
    Janet, Jon Paul
    Voronov, Alexey
    Mervin, Lewis H.
    Engkvist, Ola
    JOURNAL OF CHEMINFORMATICS, 2024, 16 (01)
  • [22] iQuantum: A toolkit for modeling and simulation of quantum computing environments
    Nguyen, Hoa T.
    Usman, Muhammad
    Buyya, Rajkumar
    SOFTWARE-PRACTICE & EXPERIENCE, 2024, 54 (06): : 1141 - 1171
  • [23] CloudAIBus: a testbed for AI based cloud computing environments
    Velu, Sasidharan
    Gill, Sukhpal Singh
    Murugesan, Subramaniam Subramanian
    Wu, Huaming
    Li, Xingwang
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (09): : 11953 - 11981
  • [24] AI-Driven Task Scheduling Strategy with Blockchain Integration for Edge Computing
    Sinha, Avishek
    Singh, Samayveer
    Verma, Harsh K.
    JOURNAL OF GRID COMPUTING, 2024, 22 (01)
  • [25] CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments
    Andreoli, Remo
    Zhao, Jie
    Cucinotta, Tommaso
    Buyya, Rajkumar
    SOFTWARE-PRACTICE & EXPERIENCE, 2025,
  • [26] Examining the applicability of the Protection of Personal Information Act in AI-driven environments
    Mbonye, Vicent
    Moodley, Marlini
    Nyika, Farai
    SOUTH AFRICAN JOURNAL OF INFORMATION MANAGEMENT, 2024, 26 (01):
  • [27] A secure and flexible edge computing scheme for AI-driven industrial IoT
    Yan Zhao
    Ning Hu
    Yue Zhao
    Zhihan Zhu
    Cluster Computing, 2023, 26 : 283 - 301
  • [28] A secure and flexible edge computing scheme for AI-driven industrial IoT
    Zhao, Yan
    Hu, Ning
    Zhao, Yue
    Zhu, Zhihan
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2023, 26 (01): : 283 - 301
  • [29] AI-Driven Task Scheduling Strategy with Blockchain Integration for Edge Computing
    Avishek Sinha
    Samayveer Singh
    Harsh K. Verma
    Journal of Grid Computing, 2024, 22
  • [30] AI-Driven Management of Dynamic Multi-Tenant Cloud Networks
    Mir, Nader F.
    SOUTHEASTCON 2023, 2023, : 716 - 717