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RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

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Abstract

Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize the resources and achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role. A number of task scheduling technique have been proposed in the literature. However, majority of these scheduling algorithms fail to achieve efficient resource utilization that causes them to miss tasks deadlines. This is because these algorithms are not resource and deadline-aware. In this research, a Resource and deadline Aware Dynamic Load-balancer (RADL) for Cloud, tasks have been presented. The proposed scheduling scheme evenly distribute the incoming workload of compute-intensive and independent tasks at run-time. In addition, RADL approach has the capability to accommodate the newly arrived tasks (with shorter deadlines) efficiently and reduce task rejection. The proposed scheduler monitors/updates the task and VM status at run-time. Experimental results show that the proposed technique has attained up to 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, and 259.14% improvement for average resource utilization, meeting tasks deadlines, lower makespan, task response time, penalty cost, and task execution cost respectively as compared to the state-of-the-art tasks scheduling heuristics using three benchmark datasets.

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Authors and Affiliations

  1. Capital University of Science & Technology, Islamabad, 44000, Pakistan

    Said Nabi & Masroor Ahmed

  2. National University of Computer & Emerging Sciences, Islamabad, 44000, Pakistan

    Muhammad Aleem & Muhammad Arshad Islam

  3. School of Computing and Communications, Lancaster University, Lancaster, LA14YW, UK

    Muhammad Azhar Iqbal

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  1. Said Nabi
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  2. Muhammad Aleem
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  3. Masroor Ahmed
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  4. Muhammad Arshad Islam
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  5. Muhammad Azhar Iqbal
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Correspondence to Muhammad Aleem.

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Nabi, S., Aleem, M., Ahmed, M. et al. RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks. J Supercomput 78, 14231–14265 (2022). https://doi.org/10.1007/s11227-022-04426-2

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