Browsing by Author "Hioual Ouassila"

Now showing 1 - 6 of 6
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    A Method based on Multi-agent Systems and Passive Replication Technique for Predicting Failures in Cloud Computing
    (Bentham Science Publishers, 2023-11-01) Hioual Ouided; Hioual Ouassila; Hemam Sofiane Mounine; Maifi Lyes
    Cloud computing refers to the computing capacities of remote computers, where the user has considerable computing power without having to own power units. The probability of failures, which can occur during execution, increases in the number of nodes. Since failures cannot be completely avoided, one solution is to use failure tolerance mechanisms. Predicting failures has become a major task for engineers and software developers, as failure increases resource usage costs. Objectives and Methods: This paper presents a hybrid method of predicting failures in a cloudcomputing environment based on the passive replication technique and multi-agent systems. It detects failures, improves the average response time and minimizes lost time. This method makes it possible to efficiently and transparently guarantee the continuity of cloud computing services in the presence of failures. Results: The results show that the proposed method performs well in the presence of failures, improves the response time and minimizes the additional costs caused by the failures. Conclusion: This paper proposes a hybrid method of predicting failures in cloud-computing based on the passive replication technique and multi-agent systems to detect failures and minimize lost time. The replication technique works by duplicating some system components, which are deployed simultaneously across different resources. This technique aims to make the system robust, increase availability and guarantee the execution of jobs. In addition, it is suitable for long-running tasks.
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    An approach based on genetic algorithms and neural networks for QoS-aware IoT services composition
    (Elsevier, 2022-09-13) Boucetti Rabeh; Hioual Ouassila; Hemam Sofiane Mounine
    In the Internet of Things (IoT) environment, the services provided by the connected objects are published as services through the web. This allows to machines to interact between them and, makes the IoT services composition possible. However, the vast proliferation of smart object generates services with the same functionalities but different in terms of quality of services (QoS) proprieties. This makes the satisfaction of the user requirements often complex and a NP-hard problem. Indeed, respecting the QoS constraints (user preferences in terms of QoS) is a challenge, due to the high number of candidate services for the composition. This challenge consists of selecting the most appropriate services so that the composite service must meet both the functional and the non-functional requirements of the user. To deal with this challenge, we propose an approach based on Genetic Algorithms (GA) and Neural Networks (NN) for QoS-aware IoT Services Composition in the context of large-scale environments. The combination between GA and NN allows finding the quasi-optimal IoT service composition. This latter is based on global QoS optimization. To reach this objective, the QoS intervals are decomposed into M QoS-levels to engage them into the theoretical composition. Then, the proposed first GA is used to obtain the ideal theoretical composition with an overall QoS optimization. Afterward, the proposed NN is used to eliminate the inappropriate concrete IoT services, and retain only the services having the same categories of the atomic theoretical services composing the ideal theoretical composition. This allows us to optimize the search space as well as the execution time. Finally, we apply the second GA on the concrete services of the retained categories, in order to obtain the IoT service concrete composition with an overall QoS optimization. The simulation results show that the proposed approach has the best composition time, the best Hypervolume indicator and the best compositional optimality compared to SC-FLA, Improved GA and MGA approaches. On another side, it has almost the same performances compared to TS-QCA and, it finds the near-optimal composition in a very short time compared to PSA, which is an optimal approach. Thus, the obtained results show the effectiveness of our approach.
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    Dynamic load balancing upon the replication and deletion of cloud services
    (IOS Press, 2023-01-05) Hemam Sofiane Mounine; Hioual Ouided; Hioual Ouassila
    In the last decade, the considerable increase of the cloud services use has led to the need to have search and selection techniques that match both the requirements of end users and those of the system. Indeed, to select a cloud service that meet the needs of both system and user is a challenge, due to the several conflicting criteria problem for the user on one hand, and for the system, i.e., the load balancing between Virtual Machines (VMs), on the second hand. Therefore, the main challenge, in this context, is how to ensure the user requirements by maintaining the system performance constraint. To deal with this challenge, we present in this paper an approach based on the cloud service replication on one or more VMs when the number of the user requests will be important at a given moment. This allows better load balancing between VMs by distrusting the users’ requests over them. In addition, it allows to select the best cloud service according to the users need. However, the cloud services replication introduces the problem of the storage space saturation. Thus, our second contribution is to select and delete the cloud service replicas without degradation of the load balancing. The two proposed contributions are based on the MCDM techniques in order to select the VMs that can receive the replica of the cloud service and to select those, which their storage space is overloaded in order to delete the replica cloud service. The experimental results, based on Cloudsim simulator, show that our proposal can effectively achieve good performance (load balancing) and improve the response time.
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    MOOA-CSF: A Multi-Objective Optimization Approach for Cloud Services Finding
    (Computing and Informatics, 2023-12-07) Bezza Youcef; Hioual Ouassila; Hioual Ouided; Yiltas-Kaplan Derya; Gürkaş-Aydin Zeynep
    Cloud computing performance optimization is the process of increasing the performance of cloud services at minimum cost, based on various features. In this paper, we present a new approach called MOOA-CSF (Multi-Objective Optimization Approach for Cloud Services Finding), which uses supervised learning and multi-criteria decision techniques to optimize price and performance in cloud computing. Our system uses an artificial neural network (ANN) to classify a set of cloud services. The inputs of the ANN are service features, and the classification results are three classes of cloud services: one that is favorable to the client, one that is favorable to the system, and one that is common between the client and system classes. The ELECTRE (ÉLimination Et Choix Traduisant la REalité) method is used to order the services of the three classes. We modified the genetic algorithm (GA) to make it adaptive to our system. Thus, the result of the GA is a hybrid cloud service that theoretically exists, but practically does not. To this end, we use similarity tests to calculate the level of similarity between the hybrid service and the other benefits in both classes. MOOA-CSF performance is evaluated using different scenarios. Simulation results prove the efficiency of our approach.
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    Real-Time Human Action Representation Based on 2D Skeleton Joints
    (IEEE, 2023-12-04) Bouguerra Adil; Hemam Sofiane Mounine; Hioual Ouassila
    We present a new approach to capture human actions by using 2D skeletal joints as the foundation for the real-time representation. Our approach combines three distinct types of information, namely: (1) motion detection to identify salient regions within the action. This allows us to compute joint contribution ratios and save processing time by excluding still joints and focusing on the main joints involved in the action; (2) utilization of a predefined map for joints trajectory shapes, which encodes the temporal information and avoids noisy data using the map; and (3) incorporation of a direction map that captures the movement of the joints, in the spatial space. By integrating these elements, we have devised a comprehensive representation capable of discerning even highly similar actions. To evaluate the effectiveness of our proposed representation, we conducted experiments on the UTD-MHAD dataset [25], which encompasses a diverse range of 27 actions performed by 8 subjects (4 females and 4 males), each with 4 repetitions. The evaluation results exhibit a notable dissimilarity within the same action category (intra-class) and a significant similarity across different action categories (inter-class). Specifically, our approach achieved a commendable score of 94.81 on the UTD-MHAD dataset, thus demonstrating its efficacy and robustness.
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    Semantic Interoperability in Electronic Health Record System Based on Blockchain Technology: a Survey
    (IEEE, 2023-11-22) Chemini Nawal; Hioual Ouassila; Hemam Sofiane Mounine
    The Electronic Health Record (EHR) represents a patient's medical information and treatment history in a digital format. Patients’ data are heterogeneous, divided, and distributed among healthcare organizations. Thus, these data help enhance the quality of healthcare services and improve diagnosis efficiency. Designing an EHR system requires many considerations, such as security, privacy, scalability, integrity, information confidentiality, and interoperability. Healthcare parties involved can internally exchange patient data, but there is an issue in sharing, interpreting, and processing it semantically with other healthcare providers. In this context, blockchain enables the sharing of clinic data in a robust, transparent, and interoperable way between different healthcare providers. It provides secure and scalable sharing of the patient’s health data, expediting coordinated care. This paper provides a systematic review focused on semantic interoperability in EHR systems based on blockchain technology. The objective is to identify the current challenges, solutions, and limitations related to this topic.