Forgalom modellezési módszerek fejlesztése = Advanced traffic modeling techniques

A projekt alapvetően sikeresen járult hozzá a vizsgált szakterület eredményeinek bővítéséhez és a résztvevő kutatócsoport fejlődéséhez. A projekt eredményihez kapcsolódó dolgozatok alapján PHD fokozatot szerzett Bodrog Levente és Saffer Zsolt. A projekt eredményeit összegző publikációk együttes impakt faktora ~22. A szakmai eredmenyek közül Markov érkezési folyamatok alapvető tulajdonságait összegző cikk (A minimal representation of Markov arrival processes and a moments matching method) emelhető ki, amelyik időközben az ezen folyamatok illesztési korlátait vizsgálló munkák alapjává vált. | The project successfully enhanced the field of traffic modeling of computer and communication systems and helped to improve the carrier of the involved research group. Based on the their theses summarizing parts of the results of the project Bodrog Levente and Saffer Zsolt were awarded the doctor of philosophy degree. The cumulated impact factor of the journal papers publish the research results of the project is about 22. The most remarkable research results are in the paper summarizing some basic properties of Markov arrival processes (A minimal representation of Markov arrival processes and a moments matching method), which become a basic reference for subsequent works dealing with the fitting properties of these processes

Fluid Petri Nets for the Performance Evaluation of MapReduce Applications

Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce jobs execution time. The experiments we performed at CINECA, the Italian supercomputing center, have shown that the achieved accuracy is within 16% of the actual measurements on average

Quality of service modeling and analysis for carrier ethernet

Today, Ethernet is moving into the mainstream evolving into a carrier grade technology. Termed as Carrier Ethernet it is expected to overcome most of the\ud shortcomings of native Ethernet. It is envisioned to carry services end-to-end serving corporate data networking and broadband access demands as well as backhauling wireless traffic. As the penetration of Ethernet increases, the offered Quality of Service (QoS) will become increasingly important and a distinguishing factor between different service providers. The challenge is to meet the QoS requirements of end applications such as response times, throughput, delay and jitter by managing the network resources at hand. Since Ethernet was not designed to operate in large public networks it does not possess functionalities to address this issue. In this thesis we propose and analyze mechanisms which improve the QoS performance of Ethernet enabling it to meet the demands of the current and next generation services and applications.\u

Scalable analysis of stochastic process algebra models

The performance modelling of large-scale systems using discrete-state approaches is fundamentally hampered by the well-known problem of state-space explosion, which causes exponential growth of the reachable state space as a function of the number of the components which constitute the model. Because they are mapped onto continuous-time Markov chains (CTMCs), models described in the stochastic process algebra PEPA are no exception. This thesis presents a deterministic continuous-state semantics of PEPA which employs ordinary differential equations (ODEs) as the underlying mathematics for the performance evaluation. This is suitable for models consisting of large numbers of replicated components, as the ODE problem size is insensitive to the actual population levels of the system under study. Furthermore, the ODE is given an interpretation as the fluid limit of a properly defined CTMC model when the initial population levels go to infinity. This framework allows the use of existing results which give error bounds to assess the quality of the differential approximation. The computation of performance indices such as throughput, utilisation, and average response time are interpreted deterministically as functions of the ODE solution and are related to corresponding reward structures in the Markovian setting. The differential interpretation of PEPA provides a framework that is conceptually analogous to established approximation methods in queueing networks based on meanvalue analysis, as both approaches aim at reducing the computational cost of the analysis by providing estimates for the expected values of the performance metrics of interest. The relationship between these two techniques is examined in more detail in a comparison between PEPA and the Layered Queueing Network (LQN) model. General patterns of translation of LQN elements into corresponding PEPA components are applied to a substantial case study of a distributed computer system. This model is analysed using stochastic simulation to gauge the soundness of the translation. Furthermore, it is subjected to a series of numerical tests to compare execution runtimes and accuracy of the PEPA differential analysis against the LQN mean-value approximation method. Finally, this thesis discusses the major elements concerning the development of a software toolkit, the PEPA Eclipse Plug-in, which offers a comprehensive modelling environment for PEPA, including modules for static analysis, explicit state-space exploration, numerical solution of the steady-state equilibrium of the Markov chain, stochastic simulation, the differential analysis approach herein presented, and a graphical framework for model editing and visualisation of performance evaluation results

Fluid models in performance analysis ⋆

Abstract. Stochastic fluid models have been applied to model and evaluate the performance of many important real systems. The automatic analysis tools to support of fluid models are still not as improved as the ones for discrete state Markov models, but there is a wide range of models which can be effectively described and analyzed with fluid models. Also the model support of hybrid models from various performance evaluation tools improves continuously. The aim of this work is to summarize the basic concepts and the potential use of Markov fluid models. The factors which determine the limits of solvability of fluid models are also discussed. Practical guidelines can be extracted from these factors to determine the applicability of fluid models in practical modeling examples. The work is supported by an example where Fluid Models, derived from an higher level modeling language (Fluid Stochastic Petri Nets), have been exploited to study the transfer time distribution in Peer-to-Peer file sharing applications.