Yazar "Dakkak, Omar" seçeneğine göre listele

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    A novel DDoS detection method using multi-layer stacking in SDN environment
    (Elsevier, 2024-12-01) Alasali, Tasnim; Dakkak, Omar
    Software Defined Network (SDN) offers virtualized services compatible with infrastructure hosted computing, presenting a flexible, adaptive, and economical network architecture. Switches used in SDN prioritize packet matching in flow tables above packet processing, leaving them open to Denial of Service (DoS) attacks. These attacks, exemplified by Distributed Denial of Service Attacks (DDoS), target a victim while using many infected workstations at once. Due to its scalability and programmability, SDN is being used more and more for network management. However, it has specific security concerns, such as the controller's susceptibility to cyberattacks, which might result in a single point of failure and network-wide risks. This study proposes a novel DDoS prediction model by developing stacking classifier model consisting of multiple base classifiers for an SDN environment. The proposed model is built on stacking several classifiers at the base level and the Meta level, which mixes varied or heterogeneous learners to provide reliable model results. The findings demonstrate that the proposed stacking model outperforms other existing models with respect to accuracy, sensitivity, specificity, precision, and F1 score. Finally, the stacking classifier model is evaluated in terms of binary classification. The evaluation shows the highest AUC of 0.9537 whereas Random Forest, Decision Tree, and Logistic Regression achieve AUC values around 0.93–0.95.
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    Advanced cost-aware Max-Min workflow tasks allocation and scheduling in cloud computing systems
    (Springer, 2024) Raeisi-Varzaneh, Mostafa; Dakkak, Omar; Fazea, Yousef; Kaosar, Mohammed Golam
    Cloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computing. The primary purpose of the task scheduling mechanism is to reduce the cost and makespan and determine which virtual machine (VM) needs to be selected to execute the task. It is widely acknowledged as a nondeterministic polynomial-time complete problem, necessitating the development of an efficient solution. This paper presents an innovative approach to task scheduling and allocation within cloud computing systems. Our focus lies on improving both the efficiency and cost-effectiveness of task execution, with a specific emphasis on optimizing makespan and resource utilization. This is achieved through the introduction of an Advanced Max-Min Algorithm, which builds upon traditional methodologies to significantly enhance performance metrics such as makespan, waiting time, and resource utilization. The selection of the Max-Min algorithm is rooted in its ability to strike a balance between task execution time and resource utilization, making it a suitable candidate for addressing the challenges of cloud task scheduling. Furthermore, a key contribution of this work is the integration of a cost-aware algorithm into the scheduling framework. This algorithm enables the effective management of task execution costs, ensuring alignment with user requirements while operating within the constraints of cloud service providers. The proposed method adjusts task allocation based on cost considerations dynamically. Additionally, the presented approach enhances the overall economic efficiency of cloud computing deployments. The findings demonstrate that the proposed Advanced Max-Min Algorithm outperforms the traditional Max-Min, Min-Min, and SJF algorithms with respect to makespan, waiting time, and resource utilization.
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    Electric Circuit-Based Modeling and Analysis of the Translational, Rotational Mechanical and Electromechanical Systems Dynamics
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Akbaba, Mehmet; Dakkak, Omar; Kim, Byung-Seo; Cora, Adnan; Nor, Shahrudin Awang
    To cope with the rapid development in technology, engineers are dealing with complex and heterogenous systems composed of blocks that belong to different engineering fields such as electrical, mechanical, chemical, electromechanical, fluid, thermal, etc. Mechanical and electrical systems more often go hand to hand in many industrial systems. For system analyzing and designing purposes, engineers must model and simulate the systems to investigate problems and aim for the best performance before proceeding to the manufacturing stage. In the presence of complex mechanical system blocks, electrical and electronics engineers are often facing difficulties in modeling the mechanical blocks. Although the similarity between individual mechanical and electrical elements is recognized for a long time, it has not drawn deserved attention for its use at the system level. In this paper, we investigate in great detail how enabling electrical and electronics engineers to easily model and analyze complex mechanical and electromechanical systems through a systematic approach. For this objective, thirteen rules are set, established, and elaborated on how to find the electrical circuit equivalent of a given mechanical or electromechanical system in order to be modeled and analyzed. The proposed approach is tested on both complex translational mechanical and electromechanical systems which includes a rotational mechanical system. Findings demonstrate that models generated by the equivalent of electric circuits are matching the models of existing mechanical and electromechanical systems by 100%. The proposed systematic approach is promising and can be widely implemented in several industrial fields.
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    From Grids to Clouds: Recap on Challenges and Solutions
    (Amer Inst Physics, 2018) Dakkak, Omar; Nor, Shaharuddin Awang; Sajat, Mohd Samsu; Fazea, Yousef; Arif, Suki
    Grid Computing is a set of resources; the separate computational power of these resources has combination to execute a huge task. Usually, in a Computational Grid environment, the main resource is the Central Processing Unit (CPU), mostly used in research fields that demand high computational power to perform massive and complicated calculations. Cloud Computing is a promising computing pattern which offers facilities and common resources on demand over the Web. The implementation of cloud computing applications has high priority, especially in the modern world, for example in providing adequate funding for social services and purchasing programs. In this paper, we discuss the implementation of cloud computing over a Smart Grid: reliable, guaranteed and efficient with low cost, it is expected to offer Long Term Evolution (LTE). This allows larger pieces of the spectrum, or bands, to be used, with greater coverage and less latency. The third technology is the Vehicular Network, an important research area because of its unique features and potential applications. In this survey, we present an overview of the smart grid, LTE and vehicular network integrated with cloud computing. We also highlight the open issues and research directions in implementing these technologies with cloud computing in terms of energy and information management for smart grids; applying cloud computing platforms for 4G networks to achieve specific criteria; and finally architectural formation, privacy and security for vehicular cloud computing.
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    Improving QoS for Non-trivial Applications in Grid Computing
    (Springer International Publishing Ag, 2020) Dakkak, Omar; Nor, Shahrudin Awang; Arif, Suki; Fazea, Yousef
    Classical scheduling mechanisms don't satisfy the requirements for the end user, especially if the number of the jobs has increased massively in grid computing environment. To meet the expectations for non-trivial applications, the efficiency of the system has to be improved and the resources have to be ultimately utilized. Thus, backfilling technique becomes highly required due to its efficiency in exploiting the resources by filling the gaps that was created in the scheduler by short jobs. There are two well-known mechanisms, which are Extensible Argonne Scheduling System (EASY) and Conservative Backfilling (CONS). EASY is very aggressive and well uses the resources, however it causes a delay for the jobs ahead in the queue, while CONS solve this issue at the expense of system efficiency. In addition, and to further improve the scheduling quality, schedule-based approach has to be implemented. This approach provides information for the incoming job parameters and the resources capabilities; thus, the mechanism schedules the jobs in advance. This approach has shown a significant improvement compared with queue-based approach. In this paper, a new mechanism is proposed, namely Swift Gap. This mechanism implements schedule-based approach and applies multi-level scheduling method. In the first stage, the mechanism finds the right place for the newly arrival job, while in the second stage it manipulates the jobs' positions for further optimization. Moreover, this paper introduces the completion time scheme. This scheme minimizes both start time and processing time. The evaluation has shown the significant impact of Swift Gap alongside the completion time rule compared to CONS and EASY.
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    LoRaline: A Critical Message Passing Line of Communication for Anomaly Mapping in IoV Systems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Bidollahkhani, Michael; Dakkak, Omar; Alajeeli, Adnan Saher Mohammad; Kim, Byung-Seo
    The importance of road safety is felt nowadays more than ever, where various technologies, including self-driving cars, have become abundant. Nowadays, it has more demand to build autonomous and electrical vehicles with information retrieval systems within the received sensory data not only from the local sensors but also the online and live streaming data over networks. To increase road safety dissemination of critical information, including the possibility of an obstacle or danger being in the middle of the road, automotive navigation and control systems are required. A novel method is proposed to make this critical communication possible over a specially designed vehicular ad-hoc network, where natural or urban barriers can prevent signal propagation. The network is implemented using the LoRaWAN interface and SX127x LoRa Radio module. The SX1272MB2xAS is fitted with the SX1272 transceiver, which added to a high-performance FSK/OOK RF transceiver modem. Additionally, LoRa long-range modem provides highly power-efficient communication. For this aim, two new mechanisms have been proposed. The first mechanism enables the nodes to receive data from a suggested communication link. While the second mechanism is designed to extract vital information such as establishing the connection, closing the connection, successful data transmission, errors, etc. The findings demonstrate that the proposed mechanisms have successfully enabled LoRaWAN to operate in IoV environment. The evaluation reveals that metrics such as battery consumption and covering range outperform similar technologies. Finally, this paper proposes a message-passing strategy based on Belief Propagation (BP) which provides more accurate marginal probabilities to overcome the low data rate as a foundation for our future work.
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    A Novel Graphical Approach for the Fast Estimation of Filter Capacitor Value and the Output Performance of Various Uncontrolled Rectifier
    (World Scientific Publ Co Pte Ltd, 2023) Akbaba, Mehmet; Dakkak, Omar; Atasoy, Ferhat; Cora, Adnan
    This paper proposes a novel approach for low-power applications for the fast estimation of filter capacitor value and the output performance parameters such as average, and Root Mean Square (RMS) values for the voltage and current considering single-phase full-wave, three-phase half-wave and three-phase full-wave rectifier circuits. For this aim, the novel equations in this work are derived for the average output voltage and rms ripple voltage separately for each of the above-mentioned three rectifier types. Then % ripple factors for each rectifier type are calculated using newly derived equations and plotted versus the newly introduced Normalized Time Constant (NTC). Besides, considering the peak supply voltage as the base voltage, the Per Unit (p.u.) output average voltage and rms ripple voltage for each of the rectifier circuits are computed and plotted versus NTC. These graphs will be normalized graphs since the output values of these graphs have turned into independent of both supply voltage amplitude and supply frequency. Normalized graphs are set up only once for each type of the rectifier circuit. Then, and for a pre-selected ripple factor value, the corresponding NTC value is obtained by straightforward reading from the graphs set up between the % ripple factor and NTC. Once the NTC value has been acquired, using the formula of NTC leads to finding the capacitor value required for the pre-selected ripple factor with one simple step calculation. Furthermore, the p.u. values of the average output voltage and rms ripple voltage values that correspond to the same NTC value are obtained by reading them from the set-up graphs directly. Finally, the efficiency of the proposed method is demonstrated through design examples for each type of rectifier circuit. The three design examples highlight how the output performance values can be obtained easily, accurately and swiftly. Furthermore, the viability of the graphical approach is verified by the experimental results which demonstrate the suitability of the derived equations in the proposed method.
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    Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Raeisi-Varzaneh, Mostafa; Dakkak, Omar; Habbal, Adib; Kim, Byung-Seo
    An inflection point in the computing industry is occurring with the implementation of the Internet of Things and 5G communications, which has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. The purpose of edge computing is to provide computing, network control, and storage to the network edge to accommodate computationally intense and latency-critical applications at resource-limited endpoints. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server's computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. This paper briefly summarizes the edge computing architecture for information and task processing. Meanwhile, the collaborative scheduling scenarios are examined. Resource scheduling techniques are then discussed and compared based on four collaboration modes. As part of our survey, we present a thorough overview of the various task offloading schemes proposed by researchers for edge computing. Additionally, according to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Finally, edge computing resource scheduling issues, challenges, and future directions have discussed.
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    Towards accommodating deadline driven jobs on high performance computing platforms in grid computing environment
    (Elsevier, 2021) Dakkak, Omar; Fazea, Yousef; Nor, Shahrudin Awang; Arif, Suki
    Grid computing is a connected computing infrastructure that furnishes reliable, stable, ubiquitous, and economic access to high-end computational power. The dynamic nature of the grid brings several challenges to scheduling algorithms that operate in queuing-based scheduling approach. This approach typically performs scheduling based on a certain fixed priority which leads to increase the delay for the running applications. Thus, the overall performance will be deteriorated sharply. The main aim of this study is to minimize the delay in the scheduler for the dynamic jobs. Therefore, this paper tackles dynamic scheduling issues by proposing Swift Gap (SG) mechanism. SG comprises of two stages by applying two mechanisms: A Backfilling Mechanism and Metaheuristic Local Search Optimization Mechanism. In the first stage, the job is placed in the earliest gap available in the local resources' schedules, while the second stage optimizes the performance by checking all available gaps among resources' schedules to find a better gap to place the job in. To further improve the performance, the Completion Time Scheme (CTS) is developed. CTS reduces the delay by placing the job in the gap that guarantees the best start time for the job, and the fastest resource available. The integration between SG and CTS (SG-CTS) is achieved by applying best start time rule in the first stage only, whereas the second stage includes both rules.SGCTS is evaluated through simulation by using real workloads that reflect a real grid system environment. The findings demonstrate that SG-CTS improves the slowdown by 27 %, bounded slowdown by 25 %, tardiness by 21 %, waiting time by 16 % and response time by 7% compared to Conservative backfilling mechanism followed by Gap Search (CONS-GS). Finally, SG-CTS is evaluated against Deadline-Based Backfilling (DBF) algorithm. The evaluation revealed that SG-CTS performs better than DBF for slowdown and waiting time in HPC2N workload.