DNI

DNI - Descartes Network Infrastructures Modeling

DNI is a family of meta-models designed for modeling the performance of communication networks. DNI is tightly related to DML and the main modeling domain are data center networks. A user can model network topology, switches, routers, servers, virtual machines, deployment of software, network protocols, routes, flow-based configuration and other relevant network parts. DNI can be used to model any type of network as it was designed to be as generic as possible.

The DNI Meta-model has been designed to support describing the most relevant performance influencing factors that occur in practice while abstracting fine-granular low level protocol details. Instances of the DNI Meta-model (DNI models) are automatically transformed to predictive stochastic models (e.g., product-form queueing networks or stochastic simulation models) by means of model-to-model transformations.

The DNI consists of the following elements:

DNI and miniDNI meta-models – written in Emfatic and Ecore
Scripts for generating DNI model instances – written in EOL
Set of model-to-model transformations between the meta-models and to selected predictive simulation models (solvers) – written in ETL
Default tree editor for specifying the DNI and miniDNI models manually
HUTN (Human Usable Textual Notation) text editor for DNI
Traffic model extraction library TrafficMSD@Github (full tooling for DNI extraction: coming soon)

Links:

Download meta-models, transformations, and examples: DNI Git repository
Manual that explains the meta-models and transformations

If you have any questions, please contact Piotr Rygielski.

Mailing List

2018[ to top ]

{Model-Based Network Analysis and Optimization}. S. Herrnleben; (2018, Februar).
- [ BibTeX ]
- [ EndNote ]
@misc{BestMasterThesisMMB18, author = {Herrnleben, Stefan}, keywords = {DNI}, month = {February}, note = {<b>Invited Talk for Best Master Thesis Award 2018 of GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB).</b>}, title = {{Model-Based Network Analysis and Optimization}}, year = 2018 }

%0 Generic %1 BestMasterThesisMMB18 %A Herrnleben, Stefan %D 2018 %T {Model-Based Network Analysis and Optimization}

2017[ to top ]

Flexible Modeling of Data Center Networks for Capacity Management. P. Rygielski; Thesis; University of W{\"u}rzburg, Germany. (2017, März).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@phdthesis{Rygielski2017-phd, author = {Rygielski, Piotr}, keywords = {DNI}, month = {March}, school = {University of W{\"u}rzburg, Germany}, title = {Flexible Modeling of Data Center Networks for Capacity Management}, year = 2017 }

%0 Thesis %1 Rygielski2017-phd %A Rygielski, Piotr %D 2017 %T Flexible Modeling of Data Center Networks for Capacity Management %U https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/14623

2016[ to top ]

{Performance Analysis of SDN Switches with Hardware and Software Flow Tables}. P. Rygielski; M. Seliuchenko; S. Kounev; M. Klymash; in Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools (ValueTools 2016) (2016).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
{Nowadays data centers are increasingly becoming larger and dynamic due to virtualization. Software-Defined Networking is the leading approach to network virtualization and flexible management. The wide variety of hardware implementations have brought strong heterogeneity to the market of networking devices which are different in terms of OpenFlow features and performance. In this paper we address the issue of heterogeneity of four hardware OpenFlow switches by characterizing selected performance relevant parameters for the hardware and software flow tables. We characterize maximum size of hardware flow tables for each switch including the behavior of a rule promotion engine that moves the rules between tables. We show that in the worst case forwarding packets using software table decreases the throughput by two orders of magnitude (from 940 to 14 Mbit/s). Our results can help the developers of SDN applications to account performance limitations of hardware and software processing as well as limited hardware support for a specific rule types. }

@inproceedings{RySeKoKl2016-valuetools2016-sdn-switches-hw-sw-flow-tables, abstract = {{Nowadays data centers are increasingly becoming larger and dynamic due to virtualization. Software-Defined Networking is the leading approach to network virtualization and flexible management. The wide variety of hardware implementations have brought strong heterogeneity to the market of networking devices which are different in terms of OpenFlow features and performance. In this paper we address the issue of heterogeneity of four hardware OpenFlow switches by characterizing selected performance relevant parameters for the hardware and software flow tables. We characterize maximum size of hardware flow tables for each switch including the behavior of a rule promotion engine that moves the rules between tables. We show that in the worst case forwarding packets using software table decreases the throughput by two orders of magnitude (from 940 to 14 Mbit/s). Our results can help the developers of SDN applications to account performance limitations of hardware and software processing as well as limited hardware support for a specific rule types. }}, author = {Rygielski, Piotr and Seliuchenko, Marian and Kounev, Samuel and Klymash, Mykhailo}, booktitle = {Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools (ValueTools 2016)}, keywords = {DNI}, month = {October}, title = {{Performance Analysis of SDN Switches with Hardware and Software Flow Tables}}, year = 2016 }

%0 Conference Paper %1 RySeKoKl2016-valuetools2016-sdn-switches-hw-sw-flow-tables %A Rygielski, Piotr %A Seliuchenko, Marian %A Kounev, Samuel %A Klymash, Mykhailo %B Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools (ValueTools 2016) %D 2016 %T {Performance Analysis of SDN Switches with Hardware and Software Flow Tables} %X {Nowadays data centers are increasingly becoming larger and dynamic due to virtualization. Software-Defined Networking is the leading approach to network virtualization and flexible management. The wide variety of hardware implementations have brought strong heterogeneity to the market of networking devices which are different in terms of OpenFlow features and performance. In this paper we address the issue of heterogeneity of four hardware OpenFlow switches by characterizing selected performance relevant parameters for the hardware and software flow tables. We characterize maximum size of hardware flow tables for each switch including the behavior of a rule promotion engine that moves the rules between tables. We show that in the worst case forwarding packets using software table decreases the throughput by two orders of magnitude (from 940 to 14 Mbit/s). Our results can help the developers of SDN applications to account performance limitations of hardware and software processing as well as limited hardware support for a specific rule types. }
{Modeling and Prediction of Software-Defined Networks Performance using Queueing Petri Nets}. P. Rygielski; M. Seliuchenko; S. Kounev; in Proceedings of the Ninth International Conference on Simulation Tools and Techniques (SIMUTools 2016) (2016). 66–75.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
{Using various modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. Descartes Network Infrastructure (DNI) is a data center network performance modeling approach that addresses this challenge by offering multiple performance models but requiring to use only a single modeling language. In this paper, we thoroughly extend DNI to support new networking paradigms like, among others, Software-Defined Networking (SDN) and Network-Function Virtualization (NFV). Additionally, we demonstrate how SDN-based networks can be modeled using DNI and how are they transformed later into Queueing Petri Nets (QPN) using a model-to-model transformation. In the analysis of the performance prediction accuracy, we show that automatically generated QPN models represent the performance of heterogeneous SDN hardware with maximal prediction accuracy error of 12 percent.}

@inproceedings{RySeKo2016-SIMUTools-SDN-switches-QPN, abstract = {{Using various modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. Descartes Network Infrastructure (DNI) is a data center network performance modeling approach that addresses this challenge by offering multiple performance models but requiring to use only a single modeling language. In this paper, we thoroughly extend DNI to support new networking paradigms like, among others, Software-Defined Networking (SDN) and Network-Function Virtualization (NFV). Additionally, we demonstrate how SDN-based networks can be modeled using DNI and how are they transformed later into Queueing Petri Nets (QPN) using a model-to-model transformation. In the analysis of the performance prediction accuracy, we show that automatically generated QPN models represent the performance of heterogeneous SDN hardware with maximal prediction accuracy error of 12 percent.}}, author = {Rygielski, Piotr and Seliuchenko, Marian and Kounev, Samuel}, booktitle = {Proceedings of the Ninth International Conference on Simulation Tools and Techniques (SIMUTools 2016)}, keywords = {QPN}, month = {August}, pages = {66–75}, title = {{Modeling and Prediction of Software-Defined Networks Performance using Queueing Petri Nets}}, year = 2016 }

%0 Conference Paper %1 RySeKo2016-SIMUTools-SDN-switches-QPN %A Rygielski, Piotr %A Seliuchenko, Marian %A Kounev, Samuel %B Proceedings of the Ninth International Conference on Simulation Tools and Techniques (SIMUTools 2016) %D 2016 %P 66--75 %T {Modeling and Prediction of Software-Defined Networks Performance using Queueing Petri Nets} %U http://dl.acm.org/citation.cfm?id=3021426.3021437 %X {Using various modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. Descartes Network Infrastructure (DNI) is a data center network performance modeling approach that addresses this challenge by offering multiple performance models but requiring to use only a single modeling language. In this paper, we thoroughly extend DNI to support new networking paradigms like, among others, Software-Defined Networking (SDN) and Network-Function Virtualization (NFV). Additionally, we demonstrate how SDN-based networks can be modeled using DNI and how are they transformed later into Queueing Petri Nets (QPN) using a model-to-model transformation. In the analysis of the performance prediction accuracy, we show that automatically generated QPN models represent the performance of heterogeneous SDN hardware with maximal prediction accuracy error of 12 percent.}
{Automated Extraction of Network Traffic Models Suitable for Performance Simulation}. P. Rygielski; V. Simko; F. Sittner; D. Aschenbrenner; S. Kounev; K. Schilling; in Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) (2016). 27–35.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
{Data centers are increasingly becoming larger and dynamic due to virtualization. In order to leverage the performance modeling and prediction techniques, such as Palladio Component Model or Descartes Modeling Language, in such a dynamic environments, it is necessary to automate the model extraction. Building and maintaining such models manually is not feasible anymore due to their size and the level of details. This paper is focused on traffic models that are an essential part of network infrastructure. Our goal is to decompose real traffic dumps into models suitable for performance prediction using Descartes Network Infrastructure modeling approach. The main challenge was to efficiently encode an arbitrary signal in the form of simple traffic generators while maintaining the shape of the original signal. We show that a typical 15 minute long tcpdump trace can be compressed to 0.4--15% of its original size whereas the relative median of extraction error is 0% for the most of the 69 examined traces.}

@inproceedings{RySiSiAschKoSch2016-dni-traffic-extraction-icpe2016, abstract = {{Data centers are increasingly becoming larger and dynamic due to virtualization. In order to leverage the performance modeling and prediction techniques, such as Palladio Component Model or Descartes Modeling Language, in such a dynamic environments, it is necessary to automate the model extraction. Building and maintaining such models manually is not feasible anymore due to their size and the level of details. This paper is focused on traffic models that are an essential part of network infrastructure. Our goal is to decompose real traffic dumps into models suitable for performance prediction using Descartes Network Infrastructure modeling approach. The main challenge was to efficiently encode an arbitrary signal in the form of simple traffic generators while maintaining the shape of the original signal. We show that a typical 15 minute long tcpdump trace can be compressed to 0.4–15% of its original size whereas the relative median of extraction error is 0% for the most of the 69 examined traces.}}, author = {Rygielski, Piotr and Simko, Viliam and Sittner, Felix and Aschenbrenner, Doris and Kounev, Samuel and Schilling, Klaus}, booktitle = {Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016)}, keywords = {DNI}, month = {March}, note = {{Acceptance rate (Full Paper): 19/57 = 33\%}}, pages = {27–35}, publisher = {ACM}, title = {{Automated Extraction of Network Traffic Models Suitable for Performance Simulation}}, year = 2016 }

%0 Conference Paper %1 RySiSiAschKoSch2016-dni-traffic-extraction-icpe2016 %A Rygielski, Piotr %A Simko, Viliam %A Sittner, Felix %A Aschenbrenner, Doris %A Kounev, Samuel %A Schilling, Klaus %B Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) %D 2016 %I ACM %P 27--35 %T {Automated Extraction of Network Traffic Models Suitable for Performance Simulation} %U http://dl.acm.org/citation.cfm?doid=2851553.2851570 %X {Data centers are increasingly becoming larger and dynamic due to virtualization. In order to leverage the performance modeling and prediction techniques, such as Palladio Component Model or Descartes Modeling Language, in such a dynamic environments, it is necessary to automate the model extraction. Building and maintaining such models manually is not feasible anymore due to their size and the level of details. This paper is focused on traffic models that are an essential part of network infrastructure. Our goal is to decompose real traffic dumps into models suitable for performance prediction using Descartes Network Infrastructure modeling approach. The main challenge was to efficiently encode an arbitrary signal in the form of simple traffic generators while maintaining the shape of the original signal. We show that a typical 15 minute long tcpdump trace can be compressed to 0.4--15% of its original size whereas the relative median of extraction error is 0% for the most of the 69 examined traces.}

2015[ to top ]

{Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models}. P. Rygielski; S. Kounev; P. Tran-Gia; in Proceedings of the Eighth International Conference on Simulation Tools and Techniques (SIMUTools 2015) (2015). 119–128.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
{Using different modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. In this paper, we propose a generic approach to modeling the performance of data center networks. The approach offers multiple performance models but requires to use only a single modeling language. We propose a two-step modeling methodology, in which a high-level descriptive model of the network is built in the first step, and in the second step model-to-model transformations are used to automatically transform the descriptive model to different network simulation models. We automatically generate three performance models defined at different levels of abstraction to analyze network throughput. By offering multiple simulation models in parallel, we provide flexibility in trading-off between the modeling accuracy and the simulation overhead. We analyze the simulation models by comparing the prediction accuracy with respect to the simulation duration. We observe, that in the investigated scenarios the solution duration of coarser simulation models is up to 300 times shorter, whereas the average prediction accuracy decreases only by 4 percent.}

@inproceedings{RyKoTr2015-SIMUTools-FlexPerformancePrediction, abstract = {{Using different modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. In this paper, we propose a generic approach to modeling the performance of data center networks. The approach offers multiple performance models but requires to use only a single modeling language. We propose a two-step modeling methodology, in which a high-level descriptive model of the network is built in the first step, and in the second step model-to-model transformations are used to automatically transform the descriptive model to different network simulation models. We automatically generate three performance models defined at different levels of abstraction to analyze network throughput. By offering multiple simulation models in parallel, we provide flexibility in trading-off between the modeling accuracy and the simulation overhead. We analyze the simulation models by comparing the prediction accuracy with respect to the simulation duration. We observe, that in the investigated scenarios the solution duration of coarser simulation models is up to 300 times shorter, whereas the average prediction accuracy decreases only by 4 percent.}}, author = {Rygielski, Piotr and Kounev, Samuel and Tran-Gia, Phuoc}, booktitle = {Proceedings of the Eighth International Conference on Simulation Tools and Techniques (SIMUTools 2015)}, keywords = {QPN}, month = {August}, pages = {119–128}, title = {{Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models}}, year = 2015 }

%0 Conference Paper %1 RyKoTr2015-SIMUTools-FlexPerformancePrediction %A Rygielski, Piotr %A Kounev, Samuel %A Tran-Gia, Phuoc %B Proceedings of the Eighth International Conference on Simulation Tools and Techniques (SIMUTools 2015) %D 2015 %P 119--128 %T {Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models} %X {Using different modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. In this paper, we propose a generic approach to modeling the performance of data center networks. The approach offers multiple performance models but requires to use only a single modeling language. We propose a two-step modeling methodology, in which a high-level descriptive model of the network is built in the first step, and in the second step model-to-model transformations are used to automatically transform the descriptive model to different network simulation models. We automatically generate three performance models defined at different levels of abstraction to analyze network throughput. By offering multiple simulation models in parallel, we provide flexibility in trading-off between the modeling accuracy and the simulation overhead. We analyze the simulation models by comparing the prediction accuracy with respect to the simulation duration. We observe, that in the investigated scenarios the solution duration of coarser simulation models is up to 300 times shorter, whereas the average prediction accuracy decreases only by 4 percent.}

2014[ to top ]

{Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples} P. Rygielski; S. Kounev; (2014).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
The goal of this document is to present the DNI Meta-Model, its instances, sub-meta-models, and model transformations in more details than in an usual conference or journal paper. We describe the meta-models and the transformations in details, give example of systems modeled using the meta-models, and present the transformations in a step-by-step manner.

@techreport{RyKo-DNI-Manual-TR, abstract = {The goal of this document is to present the DNI Meta-Model, its instances, sub-meta-models, and model transformations in more details than in an usual conference or journal paper. We describe the meta-models and the transformations in details, give example of systems modeled using the meta-models, and present the transformations in a step-by-step manner.}, address = {Am Hubland, 97074 W{\"u}rzburg}, author = {Rygielski, Piotr and Kounev, Samuel}, institution = {Chair of Software Engineering, University of W{\"u}rzburg}, keywords = {DNI}, month = {September}, title = {{Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples}}, type = {{Technical Report v.0.3}}, year = 2014 }

%0 Report %1 RyKo-DNI-Manual-TR %A Rygielski, Piotr %A Kounev, Samuel %C Am Hubland, 97074 W{\"u}rzburg %D 2014 %T {Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples} %U http://se.informatik.uni-wuerzburg.de/staff/piotr_rygielski/dni_manual/ %X The goal of this document is to present the DNI Meta-Model, its instances, sub-meta-models, and model transformations in more details than in an usual conference or journal paper. We describe the meta-models and the transformations in details, give example of systems modeled using the meta-models, and present the transformations in a step-by-step manner.
{Data Center Network Throughput Analysis using Queueing Petri Nets}. P. Rygielski; S. Kounev; in 34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, (DCPerf 2014) (2014). 100–105.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In this paper, we address the problem of performance analysis in computer networks. We present a metamodel designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. In this paper, we present the transformation to Queueing Petri Nets (QPNs) and simulate them using SimQPN simulator. We show that despite the high level of abstraction of the QPNs, we are able to estimate the utilization of resources with good accuracy within a short time.

@inproceedings{RyKo2014-DCPerf-DNI2QPN, abstract = {In this paper, we address the problem of performance analysis in computer networks. We present a metamodel designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. In this paper, we present the transformation to Queueing Petri Nets (QPNs) and simulate them using SimQPN simulator. We show that despite the high level of abstraction of the QPNs, we are able to estimate the utilization of resources with good accuracy within a short time.}, author = {Rygielski, Piotr and Kounev, Samuel}, booktitle = {34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, (DCPerf 2014)}, keywords = {DNI}, month = {June}, pages = {100–105}, title = {{Data Center Network Throughput Analysis using Queueing Petri Nets}}, year = 2014 }

%0 Conference Paper %1 RyKo2014-DCPerf-DNI2QPN %A Rygielski, Piotr %A Kounev, Samuel %B 34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, (DCPerf 2014) %D 2014 %P 100--105 %T {Data Center Network Throughput Analysis using Queueing Petri Nets} %X In this paper, we address the problem of performance analysis in computer networks. We present a metamodel designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. In this paper, we present the transformation to Queueing Petri Nets (QPNs) and simulate them using SimQPN simulator. We show that despite the high level of abstraction of the QPNs, we are able to estimate the utilization of resources with good accuracy within a short time.

2013[ to top ]

{Model-Based Throughput Prediction in Data Center Networks}. P. Rygielski; S. Kounev; S. Zschaler; in {Proceedings of the 2nd IEEE International Workshop on Measurements and Networking (M\&N 2013)} (2013). 167–172.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In this paper, we address the problem of performance analysis in computer networks. We present a new meta-model designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. We evaluate the approach in a case study of a road traffic monitoring system. We compare the performance prediction results against the real system and a benchmark. The presented results show that our approach, despite of introducing many modeling abstractions, delivers predictions with errors less than 32% and correctly detects bottlenecks in the modeled network.

@inproceedings{RyKoZs2013-ThroughputPrediction, abstract = {In this paper, we address the problem of performance analysis in computer networks. We present a new meta-model designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. We evaluate the approach in a case study of a road traffic monitoring system. We compare the performance prediction results against the real system and a benchmark. The presented results show that our approach, despite of introducing many modeling abstractions, delivers predictions with errors less than 32% and correctly detects bottlenecks in the modeled network.}, author = {Rygielski, Piotr and Kounev, Samuel and Zschaler, Steffen}, booktitle = {{Proceedings of the 2nd IEEE International Workshop on Measurements and Networking (M\&N 2013)}}, keywords = {DNI}, month = {October}, pages = {167–172}, title = {{Model-Based Throughput Prediction in Data Center Networks}}, year = 2013 }

%0 Conference Paper %1 RyKoZs2013-ThroughputPrediction %A Rygielski, Piotr %A Kounev, Samuel %A Zschaler, Steffen %B {Proceedings of the 2nd IEEE International Workshop on Measurements and Networking (M\&N 2013)} %D 2013 %P 167--172 %T {Model-Based Throughput Prediction in Data Center Networks} %X In this paper, we address the problem of performance analysis in computer networks. We present a new meta-model designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. We evaluate the approach in a case study of a road traffic monitoring system. We compare the performance prediction results against the real system and a benchmark. The presented results show that our approach, despite of introducing many modeling abstractions, delivers predictions with errors less than 32% and correctly detects bottlenecks in the modeled network.
{A Meta-Model for Performance Modeling of Dynamic Virtualized Network Infrastructures}. P. Rygielski; S. Zschaler; S. Kounev; in Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013) (2013). 327–330.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
In this work-in-progress paper, we present a new meta-model designed for the performance modeling of dynamic data center network infrastructures. Our approach models characteristic aspects of Cloud data centers which were not crucial in classical data centers. We present our meta-model and demonstrate its use for performance modeling and analysis through an example, including a transformation into OMNeT++ for performance simulation.

@inproceedings{RyZsKo2013-DNI-meta-model, abstract = {In this work-in-progress paper, we present a new meta-model designed for the performance modeling of dynamic data center network infrastructures. Our approach models characteristic aspects of Cloud data centers which were not crucial in classical data centers. We present our meta-model and demonstrate its use for performance modeling and analysis through an example, including a transformation into OMNeT++ for performance simulation.}, address = {New York, NY, USA}, author = {Rygielski, Piotr and Zschaler, Steffen and Kounev, Samuel}, booktitle = {Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013)}, keywords = {DNI}, month = {April}, pages = {327–330}, publisher = {ACM}, title = {{A Meta-Model for Performance Modeling of Dynamic Virtualized Network Infrastructures}}, year = 2013 }

%0 Conference Paper %1 RyZsKo2013-DNI-meta-model %A Rygielski, Piotr %A Zschaler, Steffen %A Kounev, Samuel %B Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013) %C New York, NY, USA %D 2013 %I ACM %P 327--330 %T {A Meta-Model for Performance Modeling of Dynamic Virtualized Network Infrastructures} %U http://icpe2013.ipd.kit.edu/ %X In this work-in-progress paper, we present a new meta-model designed for the performance modeling of dynamic data center network infrastructures. Our approach models characteristic aspects of Cloud data centers which were not crucial in classical data centers. We present our meta-model and demonstrate its use for performance modeling and analysis through an example, including a transformation into OMNeT++ for performance simulation.

DNI - Descartes Network Infrastructures Modeling

Mailing List

Hinweis zum Datenschutz

Hinweis zum Datenschutz

Bildnachweise

Your E-mail address:
Your Name (optional):