Performance Analysis of Finite Buffer Queueing System with Multiple Heterogeneous Servers

Misra, Chinmaya; Swain, P. K.

doi:10.1007/978-3-642-11659-9_19

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…A stochastic model was presented for cloud centers with heterogeneous server pools in [10]. A model was constructed for a queueing system with three heterogeneous servers in [11]. The average number of jobs and their average waiting times in the system queue were obtained by the developed model for finite heterogeneous servers with different service rates in [12].…”

Section: Related Workmentioning

confidence: 99%

“…The average number of jobs and their average waiting times in the system queue were obtained by the developed model for finite heterogeneous servers with different service rates in [12]. The power consumption of servers was not considered in [10], [11] or [12]. Additionally, setup times for servers were not considered in [11] or [12].…”

Section: Related Workmentioning

confidence: 99%

“…The power consumption of servers was not considered in [10], [11] or [12]. Additionally, setup times for servers were not considered in [11] or [12].…”

Section: Related Workmentioning

confidence: 99%

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Performance Analysis for Heterogeneous Cloud Servers Using Queueing Theory

Wang

Ruiz

2020

IEEE Trans. Comput.

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In this paper, we consider the problem of selecting appropriate heterogeneous servers in cloud centers for stochastically arriving requests in order to obtain an optimal tradeoff between the expected response time and power consumption. Heterogeneous servers with uncertain setup times are far more common than homogenous ones. The heterogeneity of servers and stochastic requests pose great challenges in relation to the tradeoff between the two conflicting objectives. Using the Markov decision process, the expected response time of requests is analyzed in terms of a given number of available candidate servers. For a given system availability, a binary search method is presented to determine the number of servers selected from the candidates. An iterative improvement method is proposed to determine the best servers to select for the considered objectives. After evaluating the performance of the system parameters on the performance of algorithms using the analysis of variance, the proposed algorithm and three of its variants are compared over a large number of random and real instances. The results indicate that proposed algorithm is much more effective than the other four algorithms within acceptable CPU times.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Performance Analysis for Heterogeneous Cloud Servers Using Queueing Theory

Wang

Ruiz

2020

IEEE Trans. Comput.

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“…In healthcare, queuing models have been widely used to study numerous problems (Preater, 2002;Koizumi et al, 2005;Cochran and Bharti, 2006;Green, 2006;Singh, 2006;Fomundam and Herrmann, 2007) including in disaster management, accidents and emergencies planning, inventory control, hospital waiting lists, mass dispensing and vaccination clinic planning, pharmacies with high-volume outpatient workloads, resource planning such as bed planning. For applications to transport, see Vandaele et al (2000) and Woensel and Vandaele (2007); and for telecommunications applications, see Maulik and Resnick (2003), Mehmood et al (2007), Alani and Mehmood (2009), Misra and Swain (2010) and Mehmood et al (2011). Also see Altowaijri and Mehmood (2010), which uses queuing models to evaluate the suitability of computational grids for pervasive deployment of medical applications in three different types of healthcare organisations.…”

Section: Introductionmentioning

confidence: 99%

Computational Markovian analysis of large systems

Mehmood

2011

Journal of Manufacturing Technology Management

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Purpose -Markov chains and queuing theory are widely used analysis, optimization and decision-making tools in many areas of science and engineering. Real life systems could be modelled and analysed for their steady-state and time-dependent behaviour. Performance measures such as blocking probability of a system can be calculated by computing the probability distributions. A major hurdle in the applicability of these tools to complex large problems is the curse of dimensionality problem because models for even trivial real life systems comprise millions of states and hence require large computational resources. This paper describes the various computational dimensions in Markov chains modelling and briefly reports on the author's experiences and developed techniques to combat the curse of dimensionality problem. Design/methodology/approach -The paper formulates the Markovian modelling problem mathematically and shows, using case studies, that it poses both storage and computational time challenges when applied to the analysis of large complex systems. Findings -The paper demonstrates using intelligent storage techniques, and concurrent and parallel computing methods that it is possible to solve very large systems on a single or multiple computers. Originality/value -The paper has developed an interesting case study to motivate the reader and have computed and visualised data for steady-state analysis of the system performance for a set of seven scenarios. The developed methods reviewed in this paper allow efficient solution of very large Markov chains. Contemporary methods for the solution of Markov chains cannot solve Markov models of the sizes considered in this paper using similar computing machines.

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Performance Analysis of Finite Buffer Queueing System with Multiple Heterogeneous Servers

Cited by 6 publications

References 6 publications

Performance Analysis for Heterogeneous Cloud Servers Using Queueing Theory

Performance Analysis for Heterogeneous Cloud Servers Using Queueing Theory

Computational Markovian analysis of large systems

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