Search

Your search keyword '"Ben Alla, Said"' showing total 25 results
Start Over Author "Ben Alla, Said"
25 results on '"Ben Alla, Said"'

1. Capacity Prediction for Lithium-Ion Batteries Using Different Neural Networks Methods

Author

Loukili, Salah Eddine, Ezzati, Abdellah, Ben Alla, Said, Zraibi, Brahim, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Gherabi, Noredine, editor, Awad, Ali Ismail, editor, Nayyar, Anand, editor, and Bahaj, Mohamed, editor

Published
2024
Full Text
View/download PDF

5. An Efficient Dynamic Priority-Queue Algorithm Based on AHP and PSO for Task Scheduling in Cloud Computing

Author

Ben Alla, Hicham, Ben Alla, Said, Ezzati, Abdellah, Touhafi, Abdellah, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Abraham, Ajith, editor, Haqiq, Abdelkrim, editor, Alimi, Adel M., editor, Mezzour, Ghita, editor, Rokbani, Nizar, editor, and Muda, Azah Kamilah, editor

Published
2017
Full Text
View/download PDF

6. A Priority Based Task Scheduling in Cloud Computing Using a Hybrid MCDM Model

Author

Ben Alla, Hicham, Ben Alla, Said, Ezzati, Abdellah, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sabir, Essaid, editor, García Armada, Ana, editor, Ghogho, Mounir, editor, and Debbah, Mérouane, editor

Published
2017
Full Text
View/download PDF

8. A Lightweight Approach (BL-DAC) to Secure Storage Sharing in Cloud-IoT Environments.

Author

Dlimi, Zakariae, Ezzati, Abdellah, and Ben Alla, Said

Subjects
CLOUD computing, INTERNET of things, LIGHTWEIGHT materials, ACCESS control, DATA analysis
Abstract

The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud (BL-DAC). Initially, the BL-DAC performs decentralized authentication using the Quantum Neural Network Cryptography (QNNC) algorithm. IoT users and edge nodes are authenticated in the blockchain deployed by distributed Trusted Authorities (TAs) using multiple credentials. The user data is classified into sensitive and non-sensitive categories using the Enhanced Seagull Optimization (ESO) algorithm. Also, the authentication to access this data is performed by a decentralized access control method using smart contract policy. Sensitive user data is encrypted using the QNNC algorithm and stored in the private cloud. In contrast, non-sensitive data is stored in the public cloud, and IPFS is used to store data in a decentralized manner with high reliability. In addition, data security is improved by using a hierarchical blockchain which improves scalability by managing the multiple blockchains hierarchically and is lightweight using Proof of Authentication Consensus (PoAH). The BL-DAC is simulated and validated using the Network Simulator-3.26 simulation tool and validated. This work shows better results than the compared ones in terms of validation metrics such as throughput (26%), encryption time (19%), decryption time (16%), response time (15%), block validation time (31%), attack detection rate (16%), access control precision (13%), and scalability (28%). [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

12. A Dynamic Task Scheduling Algorithm for Cloud Computing Environment

Author

Ezzati Abdellah, Ben Alla Hicham, and Ben Alla Said

Subjects
General Computer Science, Computer science, business.industry, Distributed computing, 0202 electrical engineering, electronic engineering, information engineering, 020207 software engineering, 020201 artificial intelligence & image processing, Cloud computing, 02 engineering and technology, business, Task (project management)
Abstract

Background: Cloud computing environment is a novel paradigm in which the services are hosted, delivered and managed over the internet. Tasks scheduling problem in the cloud has become a very interesting research area. However, the problem is more complex and challenging due to the dynamic nature of cloud and users’ needs as well as cloud providers’ requirements including the quality of service, users’ priorities and computing capabilities. Objective: The main objective is to solve the problem of tasks scheduling through an algorithm which can not only improves the client satisfaction, but also allows cloud service provider to gain maximum profit and ensure that the cloud resources are utilized efficiently. Method: (a) Optimization of the waiting time and the queue length. Methods: (a) Optimization of the waiting time and the queue length. (b) Distribution of all requests into a novel queueing system in a dynamic manner based on a decision threshold. (c) Assignment of requests to VMs based on Particle Swarm Optimization and Simulated Annealing algorithms. (d) Incorporation of the priority constraint in the scheduling process by considering three priorities levels including the tasks, queues and VMs. Results: The results comparison of our algorithm with particle swarm optimization and First Come First Serve algorithms demonstrate the effectiveness of our algorithm in terms of waiting time, makespan, resources utilization and degree of imbalance. Conclusion: This study introduces an efficient strategy to schedule users’ tasks by using dynamic dispatch queues and particle swarm optimization with simulated annealing algorithms. Moreover, it incorporates the priority issue in the scheduling process.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results