Dynamic Task Scheduling Method in Cloud Computing Environment Using Optimized Neural Network

Hamdy, Noha; Aboutabl, Amal Elsayed; ElHaggar, Nahla; Mostafa, Mostafa-Sami M.

doi:10.18178/ijfcc.2017.6.4.507

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…A significant CC system must accumulate information about all the levels of tasks and should manage the monitored environment. [ 2 ] There are three main categories of services [ 3 ] : Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Software as a Service (SaaS) …”

Section: Introductionmentioning

confidence: 99%

Cloud Computing Security: Machine and Deep Learning Models Analysis

Mishra

Chakkaravarthy

2023

Macromolecular Symposia

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Cloud computing (CC) is on-demand deliverable services over computer network for data processing and storage. CC has emerged with multiple datacenters available all over the world and providing services with a pay-as-you-go criteria. Even though the cloud system has numerous advantages still it faces one of the major challenges like vulnerability to security attacks. There are multiple types of attacks categorized to break the security like Man-in-the-middle, Malware injection, and Denial-of-service (DOS), etc. In case of deep learning (DL), the network called as convolutional neural network (CNN) is a class of deep neural networks (DNN). It is generally used to explore visual imagery. CNN are computationally comprehensive for a commercial computer. In this paper, the focus is to highlight various security attacks in the cloud with machine and deep learning methods for the detection of attacks. Also review different Machine Learning (ML) methods to overcome the cloud security challenges.

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Section: Introductionmentioning

confidence: 99%

Cloud Computing Security: Machine and Deep Learning Models Analysis

Mishra

Chakkaravarthy

2023

Macromolecular Symposia

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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

Zavieh,

Javadpour,

Ja’fari

et al. 2023

Int. J. Fuzzy Syst.

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With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan.

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Synergizing AI and Multiprocessor Systems for Task Scheduling in Industry 4.0

Vimal

2024

2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

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Dynamic Task Scheduling Method in Cloud Computing Environment Using Optimized Neural Network

Cited by 4 publications

References 8 publications

Cloud Computing Security: Machine and Deep Learning Models Analysis

Cloud Computing Security: Machine and Deep Learning Models Analysis

Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

Synergizing AI and Multiprocessor Systems for Task Scheduling in Industry 4.0

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