Browsing by Author "Dawood, K."

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Citation - Scopus: 0
Enhanced Task Scheduling in Iaas Cloud Environments Using Elitism-Based Genetic Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2024) Osama, M.; Sultan Mohiuddin Qadri, S.S.; Shams Malick, R.A.; Shahid, M.F.; Dawood, K.
Cloud computing (CC) is a modern commercial model that enables customers to acquire large amounts of virtual resources on demand. Among the various service models in CC, Infrastructure as a Service (IaaS) provides Virtual Machines (VMs) and data centers. Efficient task scheduling, which maps cloud tasks to VMs, is key to optimizing data center performance and reducing energy consumption. Given the heterogeneous nature and computational intensity of these tasks, meta-heuristic methods are often employed for scheduling. This research proposes an enhanced Genetic Algorithm (GA) that integrates an Elitism-Based strategy with Conditional Parameter Tuning to improve convergence speed and solution quality. The elitism approach preserves top-performing solutions across generations, while conditional parameter tuning dynamically adjusts algorithm parameters based on population diversity and fitness levels. Experimental evaluations on Amazon EC2 show that the proposed method significantly outperforms traditional approaches in task completion time, resource utilization, and convergence efficiency. The results demonstrate the effectiveness of combining elitism with adaptive strategies to create a scalable, robust solution for task scheduling in high-demand cloud environments. © 2024 IEEE.
Citation - Scopus: 0
A Linear Programming Approach To Carpooling: Enhancing Commute Efficiency at Federal University of Technology Minna
(Institute of Electrical and Electronics Engineers Inc., 2024) Abdulrahman, H.S.; Almusawi, A.; Bamisaye, R.T.; Qadri, S.S.S.M.; Dawood, K.
This study explores the development of a carpooling system specifically designed for the Federal University of Technology Minna staff, utilizing the Civil Engineering Department as a case study. Amidst the escalating concerns of environmental sustainability, traffic congestion, and the economic burdens of individual commuting, carpooling presents itself as a sustainable alternative. Employing a mixed-methods approach, this research integrates a comprehensive survey to assess staff attitudes towards carpooling with the development of a linear programming model aimed at optimizing vehicle routes and allocations. The findings from the survey indicate a significant willingness among the staff to engage in carpooling, motivated by the anticipated benefits such as cost savings and reduced commuting times. The linear programming model further validates the practicality of substantially lowering total travel distances and emissions when compared to solo commuting practices. This targeted investigation showcases the carpooling system's capability not only to enhance commute efficiency among university staff but also positions it as a replicable and sustainable model for other academic institutions. The study contributes valuable insights into the design and operationalization of effective carpooling strategies within the university landscape, proposing a scalable framework applicable to similar urban contexts. © 2024 IEEE.