Abstract
In a Cloud Computing environment, dynamic and uncertain nature makes task scheduling problems more complex. It states the need for efficient task scheduling designed and implementation as a primary requirement for achieving QoS. A proper resource utilization enables maximum profit for the Cloud providers. The best scheduling algorithm does not consider the task set collected from the users, but it considers the resources provided by providers for operating the tasks. In this paper, we propose a Dynamic Group of Pair Scheduling and Optimization (DGPSO) algorithm. The proposed DGPSO is the performance-enhancing of AWSQP by using VM pair implementation and partition-based priority system into three levels. These three levels in the priority system such as low, medium, and high. According to the task size, the VM pairing is done. For this, the VM's parameters include communication time, system capacity, memory size, and processing speed. On the dataset, the task sizes are examined and separated according to the priority levels. On which the high priority comprises video files, the audio files under medium-level priority, and the remaining text documents, ppts, etc. included in under low priority levels. Based on the proposed task scheduling mechanism, an experiment is conducted on the aspects of computation cost, communication cost, execution time, CPU utilization, and bandwidth. The obtained results prove its achieved performance is far better than the existing approaches.
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Arul Sindiya, J., Pushpalakshmi, R. (2022). Job Scheduling in Cloud Computing Based on DGPSO. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_3
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DOI: https://doi.org/10.1007/978-981-16-3728-5_3
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