Abstract
The cloud is new generation computing technology. The technology is a huge infrastructure that is developed using networks, computational resources and memory units. These infrastructures are not working alone they are always shared with the other cloud infrastructures. Therefore these services can deal with the fluctuating loads on servers. Additionally, scaled when more and more resource requirements appears. Therefore any of the servers can face the issue of resource availability any time due to lake of computational resources. This event in cloud infrastructure is also termed as the uncertain load appearance because the significant amount of load appeared and to execute these request less amount of resources are available by which the waiting time of the jobs increases and server running cost also increases. In order to deal with such kind of situation, the proposed work introduces a two-phase scheduling technique that helps to monitor the load appearance and based on the load patterns optimization of resource allocation is performed. In this context, two popular algorithm namely regression analysis and ACO (ant colony optimization) algorithms are applied. The simulation and modeling of the proposed approach is performed on CloudSim simulator. The experiments of the given technique demonstrate the efficient and enhance resource management.
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Kothari, N.B., Mahalkari, A. (2019). Improving the Cloud Server Resource Management in Uncertain Load Conditions Using ACO and Linear Regression Algorithms. In: Kamal, R., Henshaw, M., Nair, P. (eds) International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol 870. Springer, Singapore. https://doi.org/10.1007/978-981-13-2673-8_10
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DOI: https://doi.org/10.1007/978-981-13-2673-8_10
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