Yazar "Fazea, Yousef" seçeneğine göre listele

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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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    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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    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.