Parametric Stochastic Well-Formed Nets and Compositional Modelling

Ballarini, Paolo; Donatelli, Susanna; Franceschinis, Giuliana

doi:10.1007/3-540-44988-4_5

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…Nevertheless, the endeavor to build a complete and monolithic model to capture the whole system behaviors also confronts largeness problem (also known as state-space explosion) in modeling. To deal with this issue, one may adopt different modeling techniques and methodologies such as state truncation [61], state aggregation [62], model decomposition [63,64], state exploration [65,66], and model composition [67,68]. Other different methodologies have been also adopted popularly in literature, which are also appropriate to deal with scalability and largeness problems of modeling a large DCN system such as (i) hierarchical models, which partition a complex model into a hierarchy of submodels [69] or combine combinatorial models and state-space models [70][71][72], (ii) interactive models [22,73,74], which divide a large monolithic model into a number of smaller scale models with comprehensive interactions and dependencies, (iii) fixed-point iterative models [75], and (iv) discrete-event simulation [76].…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Sensitivity Analysis of a Data Center Network with Server Virtualization for Business Continuity

Nguyen

Dai

Park

2015

Mathematical Problems in Engineering

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Sensitivity assessment of availability for data center networks (DCNs) is of paramount importance in design and management of cloud computing based businesses. Previous work has presented a performance modeling and analysis of a fat-tree based DCN using queuing theory. In this paper, we present a comprehensive availability modeling and sensitivity analysis of a DCell-based DCN with server virtualization for business continuity using stochastic reward nets (SRN). We use SRN in modeling to capture complex behaviors and dependencies of the system in detail. The models take into account (i) two DCell configurations, respectively, composed of two and three physical hosts in a DCell0unit, (ii) failure modes and corresponding recovery behaviors of hosts, switches, and VMs, and VM live migration mechanism within and between DCell0s, and (iii) dependencies between subsystems (e.g., between a host and VMs and between switches and VMs in the same DCell0). The constructed SRN models are analyzed in detail with regard to various metrics of interest to investigate system’s characteristics. A comprehensive sensitivity analysis of system availability is carried out in consideration of the major impacting parameters in order to observe the system’s complicated behaviors and find the bottlenecks of system availability. The analysis results show the availability improvement, capability of fault tolerance, and business continuity of the DCNs complying with DCell network topology. This study provides a basis of designing and management of DCNs for business continuity.

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Sensitivity Analysis of a Data Center Network with Server Virtualization for Business Continuity

Nguyen

Dai

Park

2015

Mathematical Problems in Engineering

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“…The replicate and join operators for SANs make several copies of a SAN, and connect SANs by merging places and transitions, respectively. Recently, a composition mechanism [11] has been defined for SWNs. While smaller and more understandable modules can be composed into one large model, even the developers admit that the composition tool is not easy to use, there are no methods for validating the composition, and the resulting composed models are hardly readable [47].…”

Section: Formal Methods and Industrial-sized Modelsmentioning

confidence: 99%

Performance Analysis using Coloured Petri Nets

Wells

2002

DPB

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Performance is often a central issue in the design, development, and configuration of systems. It is not always enough to know that systems work properly, they must also work effectively. There are numerous studies, e.g. in the areas of computer and telecommunication systems, manufacturing, military, health care, and transportation, that have shown that time, money, and even lives can be saved if the performance of a system is improved. Performance analysis studies are conducted to evaluate existing or planned systems, to compare alternative configurations, or to find an optimal configuration of a system. There are three alternative techniques for analysing the performance of a system: measurement, analytical models, and simulation models.This dissertation focuses on the the use of coloured Petri nets for simulationbased performance analysis of industrial-sized systems. Coloured Petri nets are particularly well suited for modelling and analysing large and complex systems for several reasons: they have an intuitive graphical representation; they are executable; hierarchical models can be constructed; it is possible to model the time used by different activities in a system; and mature and well-tested tools exist for creating, simulating, and analysing coloured Petri net models.The dissertation consists of two parts. Part II is composed of four individual papers and constitutes the core of the dissertation. All four papers have been published or accepted for publication as workshop papers. Part I is the obligatory overview paper with summarises the work that has been done. The overview paper introduces the research field of performance analysis using coloured Petri nets, and it summarises the contents and contributions of the four individual papers.The first paper presents an overview of improved facilities for performance analysis using coloured Petri nets. Personal experience has shown that people with different backgrounds have very different needs with regards to tools supporting simulation-based performance analysis. Inexperienced data analysts will have a tendency to believe what a tool tells them, therefore care must be taken to avoid generating misleading results. More experienced data analysts generally require that more sophisticated kinds of data are generated for specific purposes. The paper presents new performance-related facilities such as support for running multiple simulations, calculating confidence intervals, generating organised and systematic simulation output, simulating and comparing alternative system configurations, and variance reduction techniques.The second paper presents a framework for implementing monitoring facilities that observe, inspect, and control simulations of coloured Petri nets. During v the past decade, a number of libraries and tool extensions have been developed for tools supporting coloured Petri nets, including support for data collection, message sequence charts, updating domain-specific graphics, and communication between simulators and other processes. Whil...

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“…This can lead to a largeness problem in analytical modeling of large networked systems. Nonetheless, one can use the techniques and algorithms proposed in [51] to reduce complexity in system modeling at RG models, or can apply typical solutions to avoid state-space explosion such as state truncation [102], state aggregation [103], model decomposition [104], state exploration [105], [106], and model composition [107], [108] in SRN modeling at components level. The attempt to predict different metrics of interest of a system by using analytical models is essentially to provide a reliable theoretical basis to facilitate system design processes, as well as to enhance system performance control processes in the long run.…”

Section: ) Practical Implementationmentioning

confidence: 99%

Reliability and Availability Evaluation for Cloud Data Center Networks Using Hierarchical Models

et al. 2019

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Modeling a cloud computing center is crucial to evaluate and predict its inner connectivity reliability and availability. Many previous studies on system availability/reliability assessment of virtualized systems consisting of singular servers in cloud data centers have been reported. In this paper, we propose a hierarchical modeling framework for the reliability and availability evaluation of tree-based data center networks. The hierarchical model consists of three layers, including 1) reliability graphs in the top layer to model the system network topology; 2) a fault-tree to model the architecture of the subsystems; and 3) stochastic reward nets to capture the behaviors and dependency of the components in the subsystems in detail. Two representative data center networks based on three-tier and fat-tree topologies are modeled and analyzed in a comprehensive manner. We specifically consider a number of case-studies to investigate the impact of networking and management on cloud computing centers. Furthermore, we perform various detailed analyses with regard to reliability and availability measures for the system models. The analysis results show that appropriate networking to optimize the distribution of nodes within the data center networks can enhance the reliability/availability. The conclusion of this paper can be used toward the practical management and the construction of cloud computing centers. INDEX TERMS Data center network (DCN), reliability, availability, hierarchical modeling, reliability graph (RG), fault tree (FT), stochastic reward net (SRN).

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Parametric Stochastic Well-Formed Nets and Compositional Modelling

Cited by 16 publications

References 8 publications

A Comprehensive Sensitivity Analysis of a Data Center Network with Server Virtualization for Business Continuity

A Comprehensive Sensitivity Analysis of a Data Center Network with Server Virtualization for Business Continuity

Performance Analysis using Coloured Petri Nets

Reliability and Availability Evaluation for Cloud Data Center Networks Using Hierarchical Models

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