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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.