Analysis of CIM performance using different LAN structures a simulation approach

This research illustrates a systematic procedure for modeling and performance analysis of the integration effect of communication network to the physical system. The concept is to model different layouts of Computer Integration Manufacturing (CIM) using different Local Area Network(LAN) structures. The steps to accomplish this concepts are, a) To determine the performance measures for physical layouts and the communication network, in order to obtain a performance analysis. b) Modeling the physical layout using Promodel simulation package. c) Extracting results from the outcome of the simulation of the physical layout and using this as input to the communication network simulation. d) Modeling the communication network using LNET simulation package. e) Comparing the output of each simulation run and determine which is most acceptable. Having different performance measures for both physical layout and networks, the proposed research objective is to illustrate the effectiveness of network structures on physical systems performance. Throughput, utilization, and delay are used as measures for both the physical layouts and network structures. Using these measures the optimum layout and network is selected

JMT – Performance Engineering Tools for System Modeling

We present the Java Modelling Tools (JMT) suite, an integrated framework of Java tools for performance evaluation of computer systems using queueing models. The suite offers a rich user interface that simplifies the definition of performance models by means of wizard dialogs and of a graphical design workspace. The performance evaluation features of JMT span a wide range of state-of-the-art methodologies including discrete-event simulation, mean value analysis of product-form networks, analytical identification of bottleneck resources in multiclass environments, and workload characterization with fuzzy clustering. The discrete-event simulator supports several advanced modeling features such as finite capacity regions, load-dependent service times, bursty processes, fork-and-join nodes, and implements spectral estimation for analysis of simulative results. The suite is open-source, released under the GNU general public license (GPL), and it is available for free download at http://jmt.sourceforge.net

NetMod: A Design Tool for Large-Scale Heterogeneous Campus Networks

The Network Modeling Tool (NetMod) uses simple analytical models to provide the designers of large interconnected local area networks with an in-depth analysis of the potential performance of these systems. This tool can be used in either a university, industrial, or governmental campus networking environment consisting of thousands of computer sites. NetMod is implemented with a combination of the easy-to-use Macintosh software packages HyperCard and Excel. The objectives of NetMod, the analytical models, and the user interface are described in detail along with its application to an actual campus-wide network.http://deepblue.lib.umich.edu/bitstream/2027.42/107971/1/citi-tr-90-1.pd

On the Effect of Channel Impairments on VANETs Performance

The primary means of studying the performance of vehicular ad hoc networks (VANETs) are computer simulations. Nowadays, the development of analytical models and the use of hybrid simulations that combine analytical modeling with discrete-event simulation are of great interest due to the significant reduction in computational cost. In this paper, we extend previous work in the area by suggesting an analytical model that includes distance-dependent losses, shadowing and small-scale fading. Closed-form expressions for the packet reception probability and the packet forwarding distance in the absence of simultaneous transmissions are presented. Numerical simulations validate the proposed formulation. The impact of path loss and fading on network throughput is explored. Interesting results that shows the efficacy of the approach are provided. The derived formulation is a useful tool for the modeling and analysis of vehicular communication systems

Analytical Modeling of a Large Local Area Network - Part I: Internet Traffic Characterization

The goal of both IP network operators and the end users is to get the highest performance from the system for a given cost. This makes Performance a key criterion in the design, procurement, and use of computer and communication systems. In order to address problems associated with performance degradation of operational communicationssystems, over the last decade, traffic engineering techniques have emerged in an attempt to optimize communication systems’ performance and ensure more efficient use of their resources. One of these techniques is analytical modeling. Analytic performance models are an excellent tool for quickly evaluating the performance of operational or new systems. They are also well suited to comparing the performance of several alternative designs. However, analytical models can only be developed once detailed knowledge of characteristics of trafficcarried by a network is available. In Part I of this paper, traffic characterization of traffic carried by the largest Local Area Network (LAN) in Tanzania, University of Dar es Salaam Network (UDSMNET) is carried out. In Part II of this paper, an analytical model based on the Discrete Time Markov Modulated Poisson Process is proposed and validated for performance analysis of IP networks

Queueing networks: solutions and applications

During the pasttwo decades queueing network models have proven to be a versatile tool for computer system and computer communication system performance evaluation. This chapter provides a survey of th field with a particular emphasis on applications. We start with a brief historical retrospective which also servesto introduce the majr issues and application areas. Formal results for product form queuenig networks are reviewed with particular emphasis on the implications for computer systems modeling. Computation algorithms, sensitivity analysis and optimization techniques are among the topics covered. Many of the important applicationsof queueing networks are not amenableto exact analysis and an (often confusing) array of approximation methods have been developed over the years. A taxonomy of approximation methods is given and used as the basis for for surveing the major approximation methods that have been studied. The application of queueing network to a number of areas is surveyed, including computer system cpacity planning, packet switching networks, parallel processing, database systems and availability modeling.Durante as últimas duas décadas modelos de redes de filas provaram ser uma ferramenta versátil para avaliação de desempenho de sistemas de computação e sistemas de comunicação. Este capítulo faz um apanhado geral da área, com ênfase em aplicações. Começamos com uma breve retrospectiva histórica que serve também para introduzir os pontos mais importantes e as áreas de aplicação. Resultados formais para redes de filas em forma de produto são revisados com ênfase na modelagem de sistemas de computação. Algoritmos de computação, análise de sensibilidade e técnicas de otimização estão entre os tópicos revistos. Muitas dentre importantes aplicações de redes de filas não são tratáveis por análise exata e uma série (frequentemente confusa) de métodos de aproximação tem sido desenvolvida. Uma taxonomia de métodos de aproximação é dada e usada como base para revisão dos mais importantes métodos de aproximação propostos. Uma revisão das aplicações de redes de filas em um número de áreas é feita, incluindo planejamento de capacidade de sistemas de computação, redes de comunicação por chaveamento de pacotes, processamento paralelo, sistemas de bancos de dados e modelagem de confiabilidade

Neural Network Modelling of Constrained Spatial Interaction Flows

Fundamental to regional science is the subject of spatial interaction. GeoComputation - a new research paradigm that represents the convergence of the disciplines of computer science, geographic information science, mathematics and statistics - has brought many scholars back to spatial interaction modeling. Neural spatial interaction modeling represents a clear break with traditional methods used for explicating spatial interaction. Neural spatial interaction models are termed neural in the sense that they are based on neurocomputing. They are clearly related to conventional unconstrained spatial interaction models of the gravity type, and under commonly met conditions they can be understood as a special class of general feedforward neural network models with a single hidden layer and sigmoidal transfer functions (Fischer 1998). These models have been used to model journey-to-work flows and telecommunications traffic (Fischer and Gopal 1994, Openshaw 1993). They appear to provide superior levels of performance when compared with unconstrained conventional models. In many practical situations, however, we have - in addition to the spatial interaction data itself - some information about various accounting constraints on the predicted flows. In principle, there are two ways to incorporate accounting constraints in neural spatial interaction modeling. The required constraint properties can be built into the post-processing stage, or they can be built directly into the model structure. While the first way is relatively straightforward, it suffers from the disadvantage of being inefficient. It will also result in a model which does not inherently respect the constraints. Thus we follow the second way. In this paper we present a novel class of neural spatial interaction models that incorporate origin-specific constraints into the model structure using product units rather than summation units at the hidden layer and softmax output units at the output layer. Product unit neural networks are powerful because of their ability to handle higher order combinations of inputs. But parameter estimation by standard techniques such as the gradient descent technique may be difficult. The performance of this novel class of spatial interaction models will be demonstrated by using the Austrian interregional traffic data and the conventional singly constrained spatial interaction model of the gravity type as benchmark. References Fischer M M (1998) Computational neural networks: A new paradigm for spatial analysis Environment and Planning A 30 (10): 1873-1891 Fischer M M, Gopal S (1994) Artificial neural networks: A new approach to modelling interregional telecommunciation flows, Journal of Regional Science 34(4): 503-527 Openshaw S (1993) Modelling spatial interaction using a neural net. In Fischer MM, Nijkamp P (eds) Geographical information systems, spatial modelling, and policy evaluation, pp. 147-164. Springer, Berlin

Performance model for two-tier mobile wireless networks with macrocells and small cells

[EN] A new analytical model is proposed to evaluate the performance of two-tier cellular networks composed of macrocells (MCs) and small cells (SCs), where terminals roam across the service area. Calls being serviced by MCs may retain their channel when entering a SC service area, if no free SC channels are available. Also, newly offered SC calls can overflow to the MC. However, in both situations channels may be repacked to vacate MC channels. The cardinality of the state space of the continuous-time Markov chain (CTMC) that models the system dynamics makes the exact system analysis unfeasible. We propose an approximation based on constructing an equivalent CTMC for which a product-form solution exist that can be obtained with very low computational complexity. We determine performance parameters such as the call blocking probabilities for the MC and SCs, the probability of forced termination, and the carried traffic. We validate the analytical model by simulation. Numerical results show that the proposed analytical model achieves very good precision in scenarios with diverse mobility rates and MCs and SCs loads, as well as when MCs overlay a large number of SCs.Authors would like to thank you the anonymous reviewers for the review comments provided to our work that have decisively contributed to improve the paper. Most of the contribution of V. Casares-Giner was done while visiting the Huazhong University of Science and Technolgy (HUST), Whuhan, China. This visit was supported by the European Commission, 7FP, S2EuNet project. The authors from the Universitat Politecnica de Valencia are partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2013-47272-C2-1-R and TEC2015-71932-REDT. The research of Xiaohu Ge was supported by the National Natural Science Foundation of China (NSFC) grant 61210002, the Fundamental Research Funds for the Central Universities grant 2015XJGH011, and China International Joint Research Center of Green Communications and Networking grant 2015B01008.Casares-Giner, V.; Martínez Bauset, J.; Ge, X. (2018). Performance model for two-tier mobile wireless networks with macrocells and small cells. Wireless Networks. 24(4):1327-1342. https://doi.org/10.1007/s11276-016-1407-8S13271342244ABIresearch. (2016). In-building mobile data traffic forecast. ABIreseach, Technical Report.NGMN Alliance. (2015). Recommendations for small cell development and deployment. NGMN Alliance, Technical Report.Chandrasekhar, V., Andrews, J., & Gatherer, A. (2008). Femtocell networks: A survey. IEEE Communications Magazine, 46(9), 59–67.Yamamoto, T., & Konishi, S. (2013). Impact of small cell deployments on mobility performance in LTE-Advanced systems. In IEEE PIMRC workshops (pp. 189–193).Balakrishnan, R., & Akyildiz, I. (2016). Local anchor schemes for seamless and low-cost handover in coordinated small cells. IEEE Transactions on Mobile Computing, 15(5), 1182–1196.Zahir, T., Arshad, K., Nakata, A., & Moessner, K. (2013). Interference management in femtocells. IEEE Communications Surveys & Tutorials, 15(1), 293–311.Yassin, M., AboulHassan, M. A., Lahoud, S., Ibrahim, M., Mezher, D., Cousin, B., & Sourour, E. A. (2015). Survey of ICIC techniques in LTE networks under various mobile environment parameters. Wireless Networks, 1–16.Andrews, M., & Zhang, L. (2015). Utility optimization in heterogeneous networks via CSMA-based algorithms. Wireless Networks, 1–14.El-atty, S. M. A., & Gharsseldien, Z. M. (2016). Performance analysis of an advanced heterogeneous mobile network architecture with multiple small cell layers. Wireless Networks, 1–22.Huang, Q., Huang, Y.-C., Ko, K.-T., & Iversen, V. B. (2011). Loss performance modeling for hierarchical heterogeneous wireless networks with speed-sensitive call admission control. IEEE Transactions on Vehicular Technology, 60(5), 2209–2223.Bonald, T., & Roberts, J. W. (2003). Congestion at flow level and the impact of user behaviour. Computer Networks, 42, 521–536.Lee, Y. L., Chuah, T. C., Loo, J., & Vinel, A. (2014). Recent advances in radio resource management for heterogeneous LTE/LTE-A networks. IEEE Communications Surveys & Tutorials, 16(4), 2142–2180.Rappaport, S. S., & Hu, L.-R. (1994). Microcellular communication systems with hierarchical macrocell overlays: Traffic performance models and analysis. Proceedings of the IEEE, 82(9), 1383–1397.Ge, X., Han, T., Zhang, Y., Mao, G., Wang, C.-X., Zhang, J., et al. (2014). Spectrum and energy efficiency evaluation of two-tier femtocell networks with partially open channels. IEEE Transactions on Vehicular Technology, 63(3), 1306–1319.Song, W., Jiang, H., & Zhuang, W. (2007). Performance analysis of the WLAN-first scheme in cellular/WLAN interworking. IEEE Transactions on Wireless Communications, 6(5), 1932–1952.Ge, X., Martinez-Bauset, J., Gasares-Giner, V., Yang, B., Ye, J., & Chen, M. (2013). Modeling and performance analysis of different access schemes in two-tier wireless networks. In IEEE Globecom (pp. 4402–4407).Tsai, H.-M., Pang, A.-C., Lin, Y.-C., & Lin, Y.-B. (2005). Repacking on demand for hierarchical cellular networks. Wireless Networks, 11(6), 719–728.Maheshwari, K., & Kumar, A. (2000). Performance analysis of microcellization for supporting two mobility classes in cellular wireless networks. IEEE Transactions on Vehicular Technology, 49(2), 321–333.Whiting, P., & McMillan, D. (1990). Modeling for repacking in cellular radio. In 7th UK Teletraffic Symposium, IEE, Durham.Kelly, F. (1989). Fixed point models of loss networks. The Journal of the Australian Mathematical Society. Series B. Applied Mathematics, 31(02), 204–218.McMillan, D. (1991). Traffic modelling and analysis for cellular mobile networks. In A. Jensen & V. Iversen (Eds.), Proceedigs of ITC-13 (pp. 627–632). IAC. Copenhaguen: Elsevier Science.Fu, H.-L., Lin, P., & Lin, Y.-B. (2012). Reducing signaling overhead for femtocell/macrocell networks. IEEE Transactions on Mobile Computing, 12(8), 1587–1597.Eklundh, B. (1986). Channel utilization and blocking probability in a cellular mobile telephone system with directed retry. IEEE Transactions on Communications, 37, 329–337.Karlsson, J., & Eklundh, B. (1989). A cellular telephone system with load sharing—An enhancement of directed retry. IEEE Transactions on Communications, 37(5), 530–535.Nelson, R. (1995). Probability, stochastic processes and queueing theory. New York: Springer.Iversen, V.B. (Aug. 1987). The exact evaluation of multi-service loss systems with access control. In Proceedings of the Seventh Nordic Teletraffic Seminar (NTS-7) (Vol. 31, pp. 56–61) Lund, (Sweden).Ross, K. W. (1995). Multiservice loss models for broadband telecommunication networks. New York: Springer.Lin, Y.-B., & Mak, V. W. (1994). Eliminating the boundary effect of a large-scale personal communication service network simulation. ACM Transactions on Modeling and Computer Simulation (TOMACS), 4(2), 165–190.Karray, M.K. (2010). Evaluation of the blocking probability and the throughput in the uplink of wireless cellular networks. In IEEE ComNet (pp. 1–8)

SIMULATION MODEL OF LOCAL COMPUTER NETWORK WITH CHANNEL AGGREGATION AND RANDOM ACCESS METHOD AT REDUNDANT TRANSFER

Subject of Research.Computer network simulation model with random access to channels and redundant transfer is developed and researched. Efficiency of this model application on configurations with different redundancy coefficient is defined. The efficiency of redundant transfer in computer networks based on common bus topology is studied. Method. The efficiency analysis of the redundant packet transmissions is carried out on the basis of computer network simulation modeling. The performance index is determined on the basis of the multiplicative criterion, which takes into account the error-free transmission and the average time margin relative to the maximum permissible transmission delay. Main Results. Computer network model with common bus topology is developed. This model gives the possibility to transmit packets via several channels and provides redundant transfer of data. Intensity and redundancy coefficient are changed while experiments were carried out. Simulation model of computer network with redundant transfer opportunity is developed. On the basis of obtained results in simulation experiments the domain of application efficiency is defined for redundant transmissions in networks based on random access and limited in average time of delivery. Practical Relevance. The presented results can be used in the design of high-reliable computer systems including computer systems providing real-time services

Modeling and Analysis of the Performance of Exascale Photonic Networks

"This is the peer reviewed version of the following article: Duro, José, Jose A. Pascual, Salvador Petit, Julio Sahuquillo, and María E. Gómez. 2018. Modeling and Analysis of the Performance of Exascale Photonic Networks. Concurrency and Computation: Practice and Experience 31 (21). Wiley. doi:10.1002/cpe.4773, which has been published in final form at https://doi.org/10.1002/cpe.4773. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Photonics technology has become a promising and viable alternative for both on-chip and off-chip interconnection networks of future Exascale systems. Nevertheless, this technology is not mature enough yet in this context, so research efforts focusing on photonic networks are still required to achieve realistic suitable network implementations. In this regard, system-level photonic network simulators can help guide designers to assess the multiple design choices. Most current research is done on electrical network simulators, whose components work widely different from photonics components. In this work, we summarize and compare the working behavior of both technologies which includes the use of optical routers, wavelength-division multiplexing and circuit switching among others. After implementing them into a well-known simulation framework, an extensive simulation study has been carried out using realistic photonic network configurations with synthetic and realistic traffic. Experimental results show that, compared to electrical networks, optical networks can reduce the execution time of the studied real workloads in almost one order of magnitude. Our study also reveals that the photonic configuration highly impacts on the network performance, being the bandwidth per channel and the message length the most important parameters.This work was supported by the ExaNeSt project, funded by the European Union's Horizon 2020 Research and Innovation Program under grant 671553, and by the Spanish Ministerio de Economía y Competitividad (MINECO) and Plan E funds under grant TIN2015-66972-C5-1-R. Pascual was supported by a HiPEAC Collaboration Grant.Duro-Gómez, J.; Pascual Pérez, JA.; Petit Martí, SV.; Sahuquillo Borrás, J.; Gómez Requena, ME. (2019). Modeling and Analysis of the Performance of Exascale Photonic Networks. Concurrency and Computation Practice and Experience. 31(21):1-12. https://doi.org/10.1002/cpe.4773S1123121Top500 website. Accessed January2018.Kodi, A. K., Neel, B., & Brantley, W. C. (2014). Photonic Interconnects for Exascale and Datacenter Architectures. 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