Energy-Aware Parallel Computing for Cloud Architecture using ANN

Kapoor, Sakshi; Panda, S. N.

doi:10.1109/gcat47503.2019.8978282

2019 Global Conference for Advancement in Technology (GCAT) 2019

DOI: 10.1109/gcat47503.2019.8978282

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Energy-Aware Parallel Computing for Cloud Architecture using ANN

Sakshi Kapoor

S. N. Panda

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2021

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“…The platform's generating abilities were called into question by a variety of cloud providers. The ndings revealed that the suggested EAIFDE architecture might reduce costs and delays for a variety of cloud service providers[21].…”

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confidence: 97%

AI - Based Framework for Private Cloud Computing

Dhaya

Kanthavel

2021

Preprint

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Artificial Intelligence (AI) systems are computational simulations that are "trained" using information and expert input to duplicate a professional's choice given the same data. Only using one private cloud storage service to store information can cause a variety of issues for the system administrator. Knowledge providers, scalability, efficiency, privacy, and the potential of vendor support are examples of such concerns. Distributing information across several cloud storage services, comparable to how data is dispersed between various physical disk drives to increase error detection and increase productivity, is a possible approach. Moreover, because multiple private cloud providers have varying pricing strategies and service quality, maximizing the efficiency and profitability of many cloud providers at the same time is difficult. Based on access permission behaviors, this study presents a methodology for dynamically modifying network management rules across several cloud providers. The goal of this research is to look into how to reduce both the estimated cost and delay periods for numerous cloud providers. The architecture was put into practice in a cloud storage systems emulator, which simulated the complexity and effectiveness of numerous cloud providers in a real-world context. In particular, the architecture was evaluated in a variety of cloud storage environments. The outcomes of the platform's testing revealed that many cloud methods were successful.

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confidence: 97%

AI - Based Framework for Private Cloud Computing

Dhaya

Kanthavel

2021

Preprint

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Energy-Aware Parallel Computing for Cloud Architecture using ANN

Cited by 1 publication

References 14 publications

AI - Based Framework for Private Cloud Computing

AI - Based Framework for Private Cloud Computing

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