Abstract

In the paper, we consider a multi-server retrial queueing system with setup time which is motivated by applications in power-saving data centers with the ON-OFF policy, where an idle server is immediately turned off and an off server is set up upon arrival of a customer. Customers that find all the servers busy join the orbit and retry for service after an exponentially distributed time. For this model, we derive the stability condition which depends on the setup time and turns out to be more strict than that of the corresponding model with an infinite buffer which is independent of the setup time. We propose asymptotic methods to analyze the system under the condition that the delay in the orbit is extremely long. We show that the scaled-number of customers in the orbit converges to a diffusion process. Using this diffusion limit, we obtain approximations for the steady-state probability distribution of the number of busy servers and that of the number of customers in the orbit. We verify the accuracy of the approximations by simulations and numerical analysis. Numerical results show that the retrial system under the limiting condition consumes more energy than that with an infinite buffer in front of the servers.

Highlights

  • Cloud computing is used by many companies and individuals

  • We considered a multiserver retrial queue with setup time

  • Using the method of asymptotic diffusion analysis, we obtained diffusion process whose probability density function is used an approximation for the probability distribution of the number of customers in the orbit

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Summary

Introduction

Cloud computing is used by many companies and individuals. Cloud computing is even more important under the current situation where social distancing is encouraged to combat COVID-19, leading to remote work for a large portion of our society. Cloud computing is supported by data centers in which a huge number of servers are available. These servers consume a huge amount of energy and saving-energy is crucial in management of data centers and cloud computing. Since user traffic has peak-on and peak-off nature, it is desired that more server resource is allocated in peak-on period and less resource is allocated in peak-off period

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