Stochastic Performance Evaluation Research Articles

Stochastic performance evaluation method of wind power DC bus voltage control system

The stochastic fluctuation of DC bus voltage of wind power grid-connected system is related to the safe and stable of power system, and the stochastic performance evaluation is an important means for early warning system. In order to evaluate stochastic performance of control system, this paper proposes a stochastic performance evaluation method based on minimum variance theory. A dual closed loop control model of wind power grid-connected converter DC bus voltage is established, the stochastic performance evaluation indicator is derived by time-series analysis and Diophantine decomposition, and the method to solve the indicator through operation data is given. The effectiveness of the proposed method is verified by FAST-MATLAB co-simulation platform. And the results show that the stochastic performance of the system can be improved by optimizing the parameters of the controller and the DC bus voltage.

Response time analysis of a manufacturing supply chain with performance evaluation process algebra

The supply and demand shocks brought on by the Covid-19 crisis are forcing manufacturers everywhere to reassess or even reorganise their supply chains. This paper proposes a general framework that can achieve fast compositional modelling with stochastic performance evaluation process algebra (PEPA) for supply chain systems. The key feature of such a framework is the PEPA-based quantitative analysis approach which includes a new method for calculating response time based on compositional PEPA models. The framework aims to provide an efficient modelling prototype for supply chain systems while supporting accurate response time-based quantitative analysis. To our knowledge, this is the first work using process algebra-based compositional modelling to quantitatively analyse supply chain systems.

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity.

Simulation-based approach for estimation of stochastic performances of deteriorating engineering systems

Engineering systems suffer from deterioration over time due to either aging, regular operation, or extreme loading/environmental conditions. It is critical to model and incorporate deteriorations in the stochastic performance assessment of deteriorating engineering systems. However, deterioration modeling usually entails complex models and large number of uncertainties, where closed-form solutions are not available for estimation of the stochastic performance measures. Some of the traditional approaches face challenges in handling nonlinear models and large number of uncertainties. This paper discusses the use of general simulation-based approach for estimation of the stochastic performance of deteriorating engineering systems. The simulation-based approach allows consideration of various uncertainties associated with the external conditions, deterioration models, performance valuation models, and puts no constraints on the complexity of the adopted models. Simulation-based estimation of performance measures such as instantaneous failure probability, number of shocks to failure, and failure time are established. Also, simulation-based evaluation of various life-cycle performance quantities is discussed with a focus on simulating samples from the probability distributions needed for this evaluation. As key steps in the approach and also the novel contributions, the paper develops explicit simulation steps and equations for the simulation of the stochastic load occurrence, realizations of deterioration processes considering both gradual and shock deteriorations with state-dependent deterioration models, as well as samples for estimating life-cycle performance quantities. Adoption of advanced simulation techniques (e.g., Importance Sampling) is also discussed to further improve the estimation efficiency and reduce the computational effort. The developed simulation-based approach is applied to the stochastic performance evaluation of a reinforced concrete (RC) bridge considering deterioration caused by both earthquakes and chloride-induced corrosion. The computational performance is discussed in detail within the context of the example.

Stochastic performance evaluation of horizontal axis wind turbine blades using non-deterministic CFD simulations

In this paper, non-deterministic CFD (computational fluid dynamics) simulations have been performed to investigate the uncertain effects of stochastic boundary conditions on the aerodynamic performance of wind turbines. A NIPRC (non-intrusive probabilistic collocation) method is employed, which is coupled with a commercial flow solver. A 2D (two-dimensional) airfoil case is used to validate the non-deterministic simulation, where the angle of attack is considered as an uncertain parameter in a Gaussian distribution. The simulation results are compared with Monte Carlo simulation results. Based on the validation, non-deterministic CFD simulations were performed on a 3D (three-dimensional) wind turbine blades case, where the wind speed is considered as an uncertain parameter. The discussions mainly focus on the total performance variations and the uncertainty propagation in the fluid field. The simulation results show that the input uncertainty of the inlet velocity results in a high variation zone in the pressure distribution near the blade root, and which decreases from the root to the tip. With the wind speed increases, flow separation is observed. The separation vortex regions correspond to the maximum variation area, and the maximum variation extends to the trailing edge even to the whole suction side.

Stochastic approach for performance evaluation regarding water distribution systems

A traditional procedure for performance evaluation of systems is to test approaches on one or more case studies. However, it is well known that the investigation of real case studies is a tedious task. Moreover, due to the limited amount of case studies available it is not certain that all aspects of a problem can be covered in such procedure. With increasing computer power an alternative methodology has emerged, that is the investigation of a multitude of virtual case studies by means of a stochastic consideration of the overall performance. Within the frame of this approach we develop here a modular design system (MDS) for water distribution systems (WDSs). With the algorithmic application of such a MDS it is possible to create a variety of different WDSs. As an example of stochastic performance evaluation the impact of pipe breakages on WDSs is estimated applying a pressure driven performance indicator. This performance indicator is evaluated stochastically. Likewise the performance evaluation of a variety of WDSs is also performed stochastically. Cumulative distribution function, histogram and other statistical properties of 2,280x1,000 performance results of the different WDSs are calculated to highlight the applicability of the introduced stochastic approach.

Stochastic performance evaluation for multi‐task processing system with software availability model

PurposeThe paper seeks to provide new stochastic measures for evaluating software reliability and performance characteristics comprehensively.Design/methodology/approachThe time‐dependent behavior of the software system itself alternating between operable and inoperable states is described by a Markov process. Then several software performance measures when the arrival process of tasks follows a homogeneous Poisson process can be derived with the infinite‐server queuing theory.FindingsProvides a new evaluation method for understanding software reliability‐related performance quantitatively. Presents a way to decide the testing time duration to satisfy the software performance requirement based on the model.Research limitations/implicationsProduction of the reasonable method for deciding the model parameters characterizing the processing time and the arrival process of the task remains as future study.Practical implicationsMost of the past models have paid attention only to the software failure‐occurrence phenomenon. On the other hand, this model includes the software restoration and the task‐processing characteristics as well and can evaluate customer‐oriented quality. In software project management, this model can provide more useful and comprehensive information than before for software development managers.Originality/valueThis paper introduces the concept of performability into the stochastic software reliability modeling and provides new quantitative evaluation measures considering reliability and performance simultaneously.

Stochastic performance evaluation of hierarchical routing for large networks

In its present form, distributed routing extracts a prohibitive price when used in large networks because of the processing time, nodal storage and line capacity required to update, store and exchange routing information among network nodes. In an earlier paper we have shown that hierarchical routing schemes with optimally selected clustering structures yield enormous reductions in routing table length and hence in routing cost, at the price of an increase in network path length. That increase was shown to be negligible in the limit of very large networks. In this paper, we evaluate the tradeoff between the reduction in routing table length and the increase in network path length in terms of the more meaningful network performance measures of delay and throughput. Extended queueing models are developed to exhibit the interrelationships which exist between network variables such as delay, throughput, channel capacity, nodal storage, network path length, routing table length, etc. These models are an extension of the classic model for networks in that they account for line overhead and storage requirements due to routing. The models demonstrate the enormous efficiency of optimized hierarchical routing for a class of large networks.