Assessing the Accuracy of Performance Modelling in Software Defined Networks

<p>Analytical modelling and experimental measurement can are used to evaluate the performance of a network. Models provide insight and measurement provides realism. For software defined networks (SDN) it is unknown how well the existing queueing models represent the performance of a real SDN network. This leads to uncertainty between what can be predicted and the actual behaviour of a software defined network. This work investigates the accuracy of software defined network queueing models. This is done through comparing the performance results of analytical models to experimental performance results. The outcome of this is an understanding of how reliable the existing queueing models are and areas where the queueing models can be improved.</p>

Assessing the Accuracy of Performance Modelling in Software Defined Networks

<p>Analytical modelling and experimental measurement can are used to evaluate the performance of a network. Models provide insight and measurement provides realism. For software defined networks (SDN) it is unknown how well the existing queueing models represent the performance of a real SDN network. This leads to uncertainty between what can be predicted and the actual behaviour of a software defined network. This work investigates the accuracy of software defined network queueing models. This is done through comparing the performance results of analytical models to experimental performance results. The outcome of this is an understanding of how reliable the existing queueing models are and areas where the queueing models can be improved.</p>

Strong Embeddings for Transitory Queueing Models

In this paper, we establish strong embedding theorems, in the sense of the Komlós-Major-Tusnády framework, for the performance metrics of a general class of transitory queueing models of nonstationary queueing systems. The nonstationary and non-Markovian nature of these models makes the computation of performance metrics hard. The strong embeddings yield error bounds on sample path approximations by diffusion processes in the form of functional strong approximation theorems.

hQChain_Leveraging towards Blockchain and Queueing Model for Secure Smart Connected Health

Blockchain facilitates a broad spectrum of applications such as transaction of cryptocurrency, catering to financial services, designing and constructing smart cities and so on. It has astounding benefits including accountability, consistency and decentralization. Smart healthcare can be exemplified as utilizing propitious electronic technology safeguarded with blockchain for superior diagnosis of the disorders, improvised and cost-effective treatment of the patients, and enhanced quality of lives. Since, blockchain in smart healthcare architecture hosts substantial amount of patient data queueing models play a pivotal role to efficiently process the data. In this paper, it highlights the concepts of blockchain, then delve into the smart healthcare architecture and then deal with the several queueing models that already exist. It proposes the model i.e. hQChain which is inculcating M1,b/Mb/1 queueing model into blockchain based smart healthcare architecture. It offers a queuing mathematical and analytical model to analyze and study the performance measurement of hQChain model.

hQChain

Blockchain facilitates a broad spectrum of applications such as transaction of cryptocurrency, catering to financial services, designing and constructing smart cities and so on. It has astounding benefits including accountability, consistency and decentralization. Smart healthcare can be exemplified as utilizing propitious electronic technology safeguarded with blockchain for superior diagnosis of the disorders, improvised and cost-effective treatment of the patients, and enhanced quality of lives. Since, blockchain in smart healthcare architecture hosts substantial amount of patient data queueing models play a pivotal role to efficiently process the data. In this paper, it highlights the concepts of blockchain, then delve into the smart healthcare architecture and then deal with the several queueing models that already exist. It proposes the model i.e. hQChain which is inculcating M1,b/Mb/1 queueing model into blockchain based smart healthcare architecture. It offers a queuing mathematical and analytical model to analyze and study the performance measurement of hQChain model.

Heavy-Traffic Comparison of a Discrete-Time Generalized Processor Sharing Queue and a Pure Randomly Alternating Service Queue

This paper compares two discrete-time single-server queueing models with two queues. In both models, the server is available to a queue with probability 1/2 at each service opportunity. Since obtaining easy-to-evaluate expressions for the joint moments is not feasible, we rely on a heavy-traffic limit approach. The correlation coefficient of the queue-contents is computed via the solution of a two-dimensional functional equation obtained by reducing it to a boundary value problem on a hyperbola. In most server-sharing models, it is assumed that the system is work-conserving in the sense that if one of the queues is empty, a customer of the other queue is served with probability 1. In our second model, we omit this work-conserving rule such that the server can be idle in case of a non-empty queue. Contrary to what we would expect, the resulting heavy-traffic approximations reveal that both models remain different for critically loaded queues.

Sensitivity analysis of queueing models based on polynomial chaos approach

Queueing systems are modeled by equations which depend on a large number of input parameters. In practice, significant uncertainty is associated with estimates of these parameters, and this uncertainty must be considered in the analysis of the model. The objective of this paper is to propose a sensitivity analysis approach for a queueing model, presenting parameters that follow a Gaussian distribution. The approach consists in decomposing the output of the model (stationary distribution of the model) into a polynomial chaos. The sensitivity indices, allowing to quantify the contribution of each parameter to the variance of the output, are obtained directly from the coefficients of decomposition. The proposed approach is then applied to M/G/1/N queueing model. The most influential parameters are highlighted. Finally several numerical and data examples are sketched out to illustrate the accuracy of the proposed method and compare them with Monte Carlo simulation. The results of this work will be useful to practitioners in various fields of theoretical and applied sciences.

Analysis of MAP/PH/1 Queueing System with Degrading Service Rate and Phase Type Vacation

Degradation of services arises in practice due to a variety of reasons including wear-and-tear of machinery and fatigue. In this paper, we look at MAP/PH/1-type queueing models in which degradation is introduced. There are several ways to incorporate degradation into a service system. Here, we model the degradation in the form of the service rate declining (i.e., the service rate decreases with the number of services offered) until the degradation is addressed. The service rate is reset to the original rate either after a fixed number of services is offered or when the server becomes idle. We look at two models. In the first, we assume that the degradation is instantaneously fixed, and in the second model, there is a random time that is needed to address the degradation issue. These models are analyzed in steady state using the classical matrix-analytic methods. Illustrative numerical examples are provided. Comparisons of both the models are drawn.