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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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    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.
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    DFIOT: Data Fusion for Internet of Things
    (Springer Science, 2020) Boulkaboul, Sahar; Djenouri, Djamel
    In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data fusion is one of the current research trends in this arena that is considered in this paper. We particularly consider typical IoT scenarios where the sources measurements highly conflict, which makes intuitive fusions prone to wrong and misleading results. This paper proposes a taxonomy of decision fusion methods that rely on the theory of belief. It proposes a data fusion method for the Internet of Things (DFIOT) based on Dempster–Shafer (D–S) theory and an adaptive weighted fusion algorithm. It considers the reliability of each device in the network and the conflicts between devices when fusing data. This is while considering the information lifetime, the distance separating sensors and entities, and reducing computation. The proposed method uses a combination of rules based on the Basic Probability Assignment (BPA) to represent uncertain information or to quantify the similarity between two bodies of evidence. To investigate the effectiveness of the proposed method in comparison with D–S, Murphy, Deng and Yuan, a comprehensive analysis is provided using both benchmark data simulation and real dataset from a smart building testbed. Results show that DFIOT outperforms all the above mentioned methods in terms of reliability, accuracy and conflict management. The accuracy of the system reached up to 99.18% on benchmark artificial datasets and 98.87% on real datasets with a conflict of 0.58%. We also examine the impact of this improvement from the application perspective (energy saving), and the results show a gain of up to 90% when using DFIOT.