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              <text>TSM: A Cloud Computing Task Scheduling Model</text>
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              <text>cloud resource; makespan; Min-Min; Task Scheduling; virtualization</text>
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              <text>Cloud offers online-based runtime computing services through virtualized resources, ensuring scalability and efficient resource utilization on demand. Resource allocation in the dynamic cloud environment poses challenges for providers due to fluctuating user demand and resource availability. Cloud service providers must dynamically and economically allocate substantial resources among dispersed users worldwide. Users, in turn, expect reliable and cost-effective computing services, requiring the establishment of Service Level Agreements (SLAs). Resource distribution uncertainty arises in view of the dynamicity of the cloud, where VMs, memory capacity requirement, processing power, and networking are allocated to user applications using virtualization technology. Resource allocation strategies must address issues such as insufficient provisioning, scarcity, competition, resources fragmentation. CPU scheduler plays a crucial role in task completion, by selecting job from queue considering specific requirements. The Task Scheduling Model (TSM) algorithm improves scheduling by considering expected execution time, standard deviation, and resource completion time, aiming to address resource imbalances and task waiting times. The research discusses previous work, presents experimental findings, describes the experimental setup and results, and concludes with future research directions.   2023 IEEE.</text>
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              <text>Kamalam G.K.; Shah N.H.; Krishnasamy L.; Manjunatha Swamy C.; Pathak L.K.; Vanitha P.</text>
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              <text>Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 1575-1580.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/IC3I59117.2023.10398151" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/IC3I59117.2023.10398151&lt;/a&gt;
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              <text>ISBN: 979-835030448-0</text>
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              <text>Kamalam G.K., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India; Shah N.H., College of Computer Science &amp;amp; Information Technology, Department of Computer Science, Jazan University, Jazan, Saudi Arabia; Krishnasamy L., School of Engineering and Technology, Department of CSE, Christ University, Karnataka, Bengaluru, India; Manjunatha Swamy C., School of Engineering and Technology, Department of CSE, Christ University, Karnataka, Bengaluru, India; Pathak L.K., School of Engineering and Technology, Department of CSE, Christ University, Karnataka, Bengaluru, India; Vanitha P., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India</text>
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