Browsing by Author "Khiat, Abdelhamid"

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    Genetic-Based Algorithm for Task Scheduling in Fog–Cloud Environment
    (Springer, ) Khiat, Abdelhamid; Haddadi, Mohamed; Bahnes, Nacera
    Over the past few years, there has been a consistent increase in the number of Internet of Things (IoT) devices utilizing Cloud services. However, this growth has brought about new challenges, particularly in terms of latency. To tackle this issue, fog computing has emerged as a promising trend. By incorporating additional resources at the edge of the Cloud architecture, the fog–cloud architecture aims to reduce latency by bringing processing closer to end-users. This trend has significant implications for enhancing the overall performance and user experience of IoT systems. One major challenge in achieving this is minimizing latency without increasing total energy consumption. To address this challenge, it is crucial to employ a powerful scheduling solution. Unfortunately, this scheduling problem is generally known as NP-hard, implying that no optimal solution that can be obtained in a reasonable time has been discovered to date. In this paper, we focus on the problem of task scheduling in a fog–cloud based environment. Therefore, we propose a novel genetic-based algorithm called GAMMR that aims to achieve an optimal balance between total consumed energy and total response time. We evaluate the proposed algorithm using simulations on 8 datasets of varying sizes. The results demonstrate that our proposed GAMMR algorithm outperforms the standard genetic algorithm in all tested cases, with an average improvement of 3.4% in the normalized function.
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    Intrusion Detection Systems using Data Mining Techniques: A comparative study
    (IEEE, 2022-01-20) Haddadi, Mohamed; Khiat, Abdelhamid; Bahnes, Nacera
    Data mining tools are widely used in computer networks. The well-known and mostly used tools to secure computers and network systems are WEKA and TANAGRA. The purpose of this study is to compare these two tools in terms of detection accuracy and computation time. This comparison was conducted using a well-known NSL-KDD dataset. Experiments show that TANAGRA achieves better results than WEKA in detection accuracy. But, TANAGRA is competitive with WEKA in terms of computation time.
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    Les méta modèles de management dans les architectures de grilles de ressources GRID
    (Université Abderrahmane Mira de Bejaia, 2013-03) Khiat, Abdelhamid; Badache, Nadjib
    Les systèmes de gestion de processus métier BPM sont largement utilisés pour les besoins d’automatisation des processus dans de larges environnements qui interagissent et nécessitent une forte synchronisation et une orchestration des tâches. Ces processus sont de nature distribués et sont pris en charge par plusieurs sous systèmes indépendants, d’où la nécessité d’orchestration. Par ailleurs, les architectures de grilles de calcul constituent des environnements avec plusieurs ressources et plusieurs services distribués et qui nécessitent des systèmes de management évolués. Notre objectif est de montrer la faisabilité de l’intégration de la technologie des BPM avec une méta modélisation adaptée à la gestion de ressources des grilles de calcul. Pour cet effet nous nous basons sur l’étude des différentes technologies associées à cette thématique et sur la validation de l’approche d’intégration par une implémentation sur un environnement GRID
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    Optimizing Cloud Energy Consumption Using Static Task Scheduling Algorithms: A Comparative Study
    (IEEE, 2023-12) Khiat, Abdelhamid
    Cloud data centers, comprising a diverse set of heterogeneous resources working collaboratively to achieve high-performance computing, face the challenge of resource dynamism, where performance fluctuates over time. This dynamism poses complexities in task scheduling, warranting further research on the resilience of existing static task scheduling algorithms when deployed in dynamic cloud environments. This study adapts three well-known task scheduling algorithms to the cloud computing context and conducts a comprehensive comparison to assess their resilience to dynamic conditions. The evaluation, employing simulation techniques, analyzes total energy consumption and total response time as key metrics. The results offer detailed insights into the effectiveness of the adapted algorithms, providing valuable guidance for optimizing task scheduling in dynamic cloud data centers.