Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment

Ismail, Leila; Liren, Zhang

doi:10.3390/a5020289

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Thus, an integrated edge-cloud computing system is often used to handle compute-intensive and/or time-critical applications [ 139 , 140 ]. However, the underlying 6G networks should consider the energy efficiency [ 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ], optimal resource provisioning and scheduling [ 149 , 150 , 151 , 152 ], and contextual-aware application partitioning [ 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 ] requirements of this integrated system.…”

Section: Taxonomy Of Technology-enabled Smart City Applications In 6g...mentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

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The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

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Section: Taxonomy Of Technology-enabled Smart City Applications In 6g...mentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

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“…In response to the request, the source sends the queried infection data to the fetcher module. The data is fetched at a periodic interval, which can be seconds, minutes, hours, or days depending on the frequency the data is updated in the data source and is stored in a cloud database (34)(35)(36)(37)(38).…”

Section: Outperforming Algorithmmentioning

confidence: 99%

Automated artificial intelligence-enabled proactive preparedness real-time system for accurate prediction of COVID-19 infections— Performance evaluation

et al. 2022

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COVID-19 is a contagious disease that has infected over half a billion people worldwide. Due to the rapid spread of the virus, countries are facing challenges to cope with the infection growth. In particular, healthcare organizations face difficulties efficiently provisioning medical staff, equipment, hospital beds, and quarantine centers. Machine and deep learning models have been used to predict infections, but the selection of the model is challenging for a data analyst. This paper proposes an automated Artificial Intelligence-enabled proactive preparedness real-time system that selects a learning model based on the temporal distribution of the evolution of infection. The proposed system integrates a novel methodology in determining the suitable learning model, producing an accurate forecasting algorithm with no human intervention. Numerical experiments and comparative analysis were carried out between our proposed and state-of-the-art approaches. The results show that the proposed system predicts infections with 72.1% less Mean Absolute Percentage Error (MAPE) and 65.2% lower Root Mean Square Error (RMSE) on average than state-of-the-art approaches.

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Simulation of Performance Evaluation Model of Cloud Computing Platform Based on Deep Learning

Ma,

Gu,

Duan

et al. 2023

2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)

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Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment

Cited by 4 publications

References 22 publications

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Automated artificial intelligence-enabled proactive preparedness real-time system for accurate prediction of COVID-19 infections— Performance evaluation

Simulation of Performance Evaluation Model of Cloud Computing Platform Based on Deep Learning

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