Abstract
Cloud computing is the technology that was gaining the attention of most of the companies in market and utilization also increasing day to day by almost from companies to ordinary people. The working of these cloud models is effortless. A considerable number of servers are used to store the data and a vast amount of data and the service of providing data to the customers staying at remote locations too. Almost all cloud-based models are not free, and users need to pay a reasonable amount to use the services of these clouds. As the vast data is stored in these servers and the usage of this data by a vast number of customers, there is a chance of overcrowded at servers. Essential data or the hot data like the new movies, exam results or bank transactions, etc., can have the most of the crowds at various time intervals. Hence, it is required to analyse the number of customers is using the current cloud models at different intervals of time. Based on the results, the adjustments or the changes in the network model can be completed. In the current article, an attempt has been made to analyse a cloud model by considering the model working in study state and the performance was analysed for two queuing models. Several queuing models are available in research to analyse the performance of a queuing model. In the current article, the queuing models considered are M/M/1 and M/M/c models. The performance of the queuing models is analysed with various performance metrics of a network, or the cloud model is arrival rates to the model, service rates to the model, traffic density, throughput, etc. The results are displayed in the results section.
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Srinivas, P., Pillala, P., Thirupathi Rao, N., Bhattacharyya, D. (2021). Performance Investigation of Cloud Computing Applications Using Steady-State Queuing Models. In: Bhattacharyya, D., Thirupathi Rao, N. (eds) Machine Intelligence and Soft Computing. Advances in Intelligent Systems and Computing, vol 1280. Springer, Singapore. https://doi.org/10.1007/978-981-15-9516-5_19
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DOI: https://doi.org/10.1007/978-981-15-9516-5_19
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