Abstract
A complex issue in handling systems with continually changing processing demands is an intractable task. A more current example of these systems can be observed in wireless sensor networks and traffic-intensive IoT networks. Thus, an adaptive framework is desired which can handle the load and can also assist in enhancing the performance of the system. In this paper, our objective is to provide the non-stationary solution of Erlang loss queueing model where s servers can serve at most s jobs at a time. We have employed time-dependent perturbation theory to obtain the probability distribution of M/M/s/s queueing model. The time-dependent arrival and service rates are assumed to be in sinusoidal form. The opted theory gives approximation for probability distribution correct up to first and second order. The result shows that first- and second-order approximations provide better approximation than the existing ones.
Amit Kumar Singh was a Ph.D. student at JNU, New Delhi.
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Singh, A.K., Senapati, D., Bebortta, S., Rajput, N.K. (2021). A Non-stationary Analysis of Erlang Loss Model. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-6584-7_28
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DOI: https://doi.org/10.1007/978-981-15-6584-7_28
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