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Hybrid Load-Balanced Scheduling in Scalable Cloud Environment

Hybrid Load-Balanced Scheduling in Scalable Cloud Environment

Anant Kumar Jayswal
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 17
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781799806875|DOI: 10.4018/IJISMD.2020070104
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MLA

Jayswal, Anant Kumar. "Hybrid Load-Balanced Scheduling in Scalable Cloud Environment." IJISMD vol.11, no.3 2020: pp.62-78. http://doi.org/10.4018/IJISMD.2020070104

APA

Jayswal, A. K. (2020). Hybrid Load-Balanced Scheduling in Scalable Cloud Environment. International Journal of Information System Modeling and Design (IJISMD), 11(3), 62-78. http://doi.org/10.4018/IJISMD.2020070104

Chicago

Jayswal, Anant Kumar. "Hybrid Load-Balanced Scheduling in Scalable Cloud Environment," International Journal of Information System Modeling and Design (IJISMD) 11, no.3: 62-78. http://doi.org/10.4018/IJISMD.2020070104

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Abstract

Cloud computing is a high computational distributed environment with high reliability and quality of service. It is playing an important role in the next generation of computing with pay per use model and high elasticity. With increased requirement for cloud resources, load over the cloud servers has increased, which makes cloud use a more efficient algorithm to maintain its performance and quality of service to users. The performance metrics that define the performance of task scheduling include execution time, finish time, scheduling time, task completion cost, and load balancing on each computing resources. So, to overcome existing solutions and provide better QoS performance, a neural-network-based GA-ANN scheduling algorithm is proposed in this paper, which outperforms the existing solutions. To simulate the proposed GA-ANN model, cloudsim3.0 toolkit is used, and the performance is evaluated by comparing simulation time, average start time, average finish time, execution time, and utilization percentage of computing resources (VMs).

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