循环排队网络的极大代数描述、稳定性与扰动分析

在极大代数框架下对循环排队网络给出了状态方程, 分析了系统的稳定性, 证明了系统性能函数的求导算子与求数学期望算子的可交换性, 然后给出了性能函数关于可控参数灵敏度的计算方法. 最后进行了仿真, 验证了算法的有效性.     

Stochastic models for performance analysis of multistate flexible manufacturing cells

Performance analysis of flexible manufacturing cells (FMCs) can help companies find the pros and cons of production processes. However, the emphasis has been on issues like cell formation, layout design and scheduling optimization. Little seems to have been done to assess the reliability of an FMC. In this paper, we develop the stochastic models for the performance analysis mainly on the reliability of two different FMCs configured from a set of teaching intelligent flexible manufacturing system (TIFMS). The closed form solutions of probabilities of system states are obtained. Then, utilization rate of equipment in the cell and productivities of the two FMCs as the performance indexes are calculated and optimized. Compared to simulation methods, the closed form solutions make calculations of the performance indexes faster and more accurate. When random variables in the stochastic models are assumed to follow non-exponential distributions, the effects of them on the performance indexes are discussed. The objective of this paper is to fill up the gap that the closed form solutions are difficult to obtain as the number of machine tool increases. Another objective is to optimize the performance indexes to help engineers better evaluate the performance of FMC. Numerical analysis cases are used to illustrate the proposed stochastic models.     

Exact analysis of performance models by the Method of Moments

Performance evaluation of distributed systems and service-oriented architectures is often based on stochastic models, such as closed queueing networks which are commonly solved by the Mean Value Analysis (MVA) algorithm. However, the MVA is unable to solve models with hundreds or thousands of users accessing services of multiple classes, a configuration that is often useful to predict the performance of real-world applications. This paper introduces the Method of Moments (MoM), the first exact algorithm for solving closed queueing networks with large population sizes.Compared to the MVA algorithm, which is based on a recursive evaluation of mean queue-lengths, MoM defines a recursion on higher-order moments of queue-lengths that is solved at each step by a linear system of equations. This approach dramatically decreases the costs of an exact analysis compared to the MVA approach. We prove that MoM requires log-quadratic time and log-linear space in the total population size, whereas MVA complexity expressions grow combinatorially as the product of class populations. This extends the feasibility of exact methods to a much larger family of multiclass performance models than those that can be solved by the MVA algorithm.     

Stochastic modelling and analysis of 802.11 DCF with heterogeneous non-saturated nodes

The Distributed Coordination Function (DCF) in the IEEE 802.11 protocol is a random access scheme based on the carrier sense multiple access with collision avoidance (CSMA/CA). In recent years, there have been numerous research on the performance analysis and modelling of DCF under the assumption that the transmission queue is always nonempty (i.e. saturation state). In this paper, we propose a stochastic analysis approach to study the operation of the DCF in the non-saturation state. We also consider the fact that most 802.11 deployments use the infrastructure mode of operation in which all traffic is routed through an access point; this implies that access points will have much more traffic to transmit than the clients. within this realm, we allow for asymmetric finite rate clients to account for the heterogeneous nature of the wireless Access points. Our modelling approach is an extension of that described in Winands et al. [E. Winands, T. Denteneer, J. Resing, R. Rietman, A finite-source feedback queueing network as a model for the IEEE 802.11 DCF, in: Eur. Trans. Telecommun. 16 (1) (2005) 77–89], which is an adaptation of the homogeneous finite-source machine repair queueing model.     

基于1坚持指数退避算法的时隙CSMA/CD协议的排队模型的建立与分析

建立了1坚持指数退避算法的CSMA/CD协议的离散时间排队模型,基于该模型分析了网络吞吐量(S)、等待时间(W)等性能指标,并通过建立相应的马尔可夫链(MarkovChain),计算了信道忙的概率及产生冲突的概率。     

A performance model for analysis of heterogeneous multi-cluster systems

This paper addresses the problem of performance modeling for large-scale heterogeneous distributed systems with emphases on multi-cluster computing systems. Since the overall performance of distributed systems is often depends on the effectiveness of its communication network, the study of the interconnection networks for these systems is very important. Performance modeling is required to avoid poorly chosen components and architectures as well as discovering a serious shortfall during system testing just prior to deployment time. However, the multiplicity of components and associated complexity make performance analysis of distributed computing systems a challenging task. To this end, we present an analytical performance model for the interconnection networks of heterogeneous multi-cluster systems. The analysis is based on a parametric family of fat-trees, the m-port n-tree, and a deterministic routing algorithm, which is proposed in this paper. The model is validated through comprehensive simulation, which demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.     

Mixing logics and rewards for the component-oriented specification of performance measures

Formal notations for system performance modeling need to be equipped with suitable notations for specifying performance measures. These companion notations have been traditionally based on reward structures and, more recently, on temporal logics. In this paper we propose an approach that combines logics and rewards, together with a definition mechanism that allows performance measures to be specified in a component-oriented way, thus facilitating the task for non-experts. The resulting Measure Specification Language (MSL) is interpreted both on action-labeled continuous-time Markov chains and on stochastic process algebras. The latter interpretation provides a compositional framework for performance-sensitive model manipulations and emphasizes the increased expressiveness with respect to traditional reward structures for implicit-state modeling notations.     

网络数据流性能指标的仿真研究与分析

网络服务质量已经成为研究的热点,不同网络调度策略的应用会产生不同服务质量,目前对这些策略的定量分析却相对较少,本文根据排队论的思想对这些调度策略进行了分析,说明了它们对网络数据流的QoS性能指标的影响。最后还通过仿真证明了本文论述的正确性。     

System-level performance analysis of multiprocessor system-on-chips by combining analytical model and execution time variation

As the impact of the communication architecture on performance grows in a Multiprocessor System-on-Chip (MPSoC) design, the need for performance analysis in the early stage in order to consider various communication architectures is also increasing. While a simulation is commonly performed for performance evaluation of an MPSoC, it often suffers from a lengthy run time as well as poor performance coverage due to limited input stimuli or their ad hoc applications. In this paper, we propose a novel system-level performance analysis method to estimate the performance distribution of an MPSoC. Our approach consists of two techniques: (1) analytical model of on-chip crossbar-based communication architectures and (2) enumeration of task-level execution time variations for a target application. The execution time variation of tasks is efficiently captured by a memory access workload model. Thus, the proposed approach leads to better performance coverage for an MPSoC application in a reasonable computation time than the simulation-based approach. The experimental results validate the accuracy, efficiency, and practical usage of the proposed approach.     

On the use of MTBDDs for performability analysis and verification of stochastic systems

This paper describes how to employ multi-terminal binary decision diagrams (MTBDDs) for the construction and analysis of a general class of models that exhibit stochastic, probabilistic and non-deterministic behaviour. It is shown how the notorious problem of state space explosion can be circumvented by compositionally constructing symbolic (i.e. MTBDD-based) representations of complex systems from small-scale components. We emphasise, however, that compactness of the representation can only be achieved if heuristics are applied with insight into the structure of the system under investigation. We report on our experiences concerning compact representation, performance analysis and verification of performability properties.     

A Dirichlet process mixture model for the analysis of correlated binary responses

The multivariate probit model is a popular choice for modelling correlated binary responses. It assumes an underlying multivariate normal distribution dichotomized to yield a binary response vector. Other choices for the latent distribution have been suggested, but basically all models assume homogeneity in the correlation structure across the subjects. When interest lies in the association structure, relaxing this homogeneity assumption could be useful. The latent multivariate normal model is replaced by a location and association mixture model defined by a Dirichlet process. Attention is paid to the parameterization of the covariance matrix in order to make the Bayesian computations convenient. The approach is illustrated on a simulated data set and applied to oral health data from the Signal Tandmobiel^® study to examine the hypothesis that caries is mainly a spatially local disease.     

Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels

Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.     

Extending the information delivery manual approach to identify information requirements for performance analysis of HVAC systems

Heating, Ventilation and Air-Conditioning (HVAC) systems account for more than 15% of the total energy consumption in the US. In order to improve the energy efficiency of HVAC systems, researchers have developed hundreds of algorithms to automatically analyze their performance. However, the complex information, such as configurations of HVAC systems, layouts and materials of building elements and dynamic data from the control systems, required by these algorithms inhibits the process of deploying them in real-world facilities. To address this challenge, we envision a framework that automatically integrates the required information items and provides them to the performance analysis algorithms for HVAC systems. This paper presents an approach to identify and document the information requirements from the publications that describe these algorithms. We extend the Information Delivery Manual (IDM) approach so that the identified information requirements can be mapped to multiple information sources that use various formats and schemas. This paper presents the extensions to the IDM approach and the results of using it to identify information requirements for performance analysis algorithms of HVAC systems.     

Parallelization of the Ising model and its performance evaluation

We report on the parallelization of two widely used algorithms in computational physics: The Monte Carlo simulation of the Ising model and a cluster identification algorithm which is used for percolation or percolation-like problems. Both parallel algorithms were tested on a multi-transputer system using up to 128 processors. The results show that the algorithms can perform with a linear speedup. We propose a scaling law for the speedup and show that the speedup for both algorithms satisfies this scaling.     

Random network models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage supply chains

Supply chains in reality face a highly dynamic and uncertain environment, especially the uncertain end-customer demands and orders. Since the condition of product market changes frequently, the tasks of order management, product planning, and inventory management are complex and difficult. It is imperative for companies to develop new ways to manage the randomness and uncertainty in market demands. Based on the graphical evaluation and review technique, this paper provides a simple but integrated stochastic network mathematical model for supply chain ordering time distribution analysis. Then the ordering time analysis model is extended so that the analysis of inventory level distribution characteristics of supply chain members is allowed. Further, to investigate the effects of different end-customer demands on upstream orders and relative inventory levels, model-based sensitivity analysis algorithms for ordering fluctuations and inventory fluctuations are developed. A detailed numerical example is presented to illustrate the application of the proposed models to a multi-stage supply chain system, and the results of which shows the effectiveness and flexibility of the proposed stochastic network models and algorithms in order and inventory management.     

Development and performance analysis of real‐world applications for distributed and parallel architectures

Several large real‐world applications have been developed for distributed and parallel architectures. We examine two different program development approaches. First, the usage of a high‐level programming paradigm which reduces the time to create a parallel program dramatically but sometimes at the cost of a reduced performance; a source‐to‐source compiler, has been employed to automatically compile programs—written in a high‐level programming paradigm—into message passing codes. Second, a manual program development by using a low‐level programming paradigm—such as message passing—enables the programmer to fully exploit a given architecture at the cost of a time‐consuming and error‐prone effort. Performance tools play a central role in supporting the performance‐oriented development of applications for distributed and parallel architectures. SCALA—a portable instrumentation, measurement, and post‐execution performance analysis system for distributed and parallel programs—has been used to analyze and to guide the application development, by selectively instrumenting and measuring the code versions, by comparing performance information of several program executions, by computing a variety of important performance metrics, by detecting performance bottlenecks, and by relating performance information back to the input program. We show several experiments of SCALA when applied to real‐world applications. These experiments are conducted for a NEC Cenju‐4 distributed‐memory machine and a cluster of heterogeneous workstations and networks. Copyright © 2001 John Wiley & Sons, Ltd.     

Frontier-based performance analysis models for supply chain management: State of the art and research directions

Effective supply chain management relies on information integration and implementation of best practice techniques across the chain. Supply chains are examples of complex multi-stage systems with temporal and causal interrelations, operating multi-input and multi-output production and services under utilization of fixed and variable resources. Acknowledging the lack of system’s view, the need to identify system-wide and individual effects as well as incorporating a coherent set of performance metrics, the recent literature reports on an increasing, but yet limited, number of applications of frontier analysis models (e.g. DEA) for the performance assessment of supply chains or networks. The relevant models in this respect are multi-stage models with various assumptions on the intermediate outputs and inputs, enabling the derivation of metrics for technical and cost efficiencies for the system as well as the autonomous links. This paper reviews the state of the art in network DEA modeling, in particular two-stage models, along with a critical review of the advanced applications that are reported in terms of the consistency of the underlying assumptions and the results derived. Consolidating current work in this range using the unified notations and comparison of the properties of the presented models, the paper is closed with recommendations for future research in terms of both theory and application.     

A study on assigning performance shaping factors of the SPAR-H method for adequacy human reliability analysis of nuclear power plants

The performance shaping factors (PSFs) of the standardized plant analysis of risk-human reliability analysis (SPAR-H) method are unclearly defined, which contributes to the uncertainty of human reliability analysis (HRA) in nuclear power plants (NPPs). This work proposes an expert-based modification approach for redefining the PSFs based on four criteria in terms of less overlap, hierarchy, flexibility, and digitalization.For demonstration, the proposed approach is used to assign PSFs to three specific human failure events in NPPs. Three tests (Kendall's W Test, Jonckheere-Terpstra Test, and Paired Samples Test) are applied to analyze the assignments. Compared to the PSF assignment of SPAR-H, the results show that the redefined PSFs meet the four criteria and reduce the overestimation of human error probabilities (HEPs).