Data Center Network Throughput Analysis Using Queueing Petri Nets

Rygielski, Piotr; Kounev, Samuel

doi:10.1109/icdcsw.2014.11

Cited by 13 publications

(8 citation statements)

References 19 publications

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“…The authors of [27] present an approach to transform models of network infrastructures in modern data centers into queueing Petri net models. The selected meta-model for the network infrastructures is the Descartes Network Infrastructure (DNI) meta-model for which the Ecore definition is presented in [28].…”

Section: Literature Reviewmentioning

confidence: 99%

On Utilizing Model Transformation for the Performance Analysis of Queueing Networks

Al-Azzoni¹

2018

JSEA

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In this paper, we present an approach for model transformation from Queueing Network Models (QNMs) into Queueing Petri Nets (QPNs). The performance of QPNs can be analyzed using a powerful simulation engine, SimQPN, designed to exploit the knowledge and behavior of QPNs to improve the efficiency of simulation. When QNMs are transformed into QPNs, their performance can be analyzed efficiently using SimQPN. To validate our approach, we apply it to analyze the performance of several queueing network models including a model of a database system. The evaluation results show that the performance analysis of the transformed QNMs has high accuracy and low overhead. In this context, model transformation enables the performance analysis of queueing networks using different ways that can be more efficient.

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Section: Literature Reviewmentioning

confidence: 99%

On Utilizing Model Transformation for the Performance Analysis of Queueing Networks

Al-Azzoni¹

2018

JSEA

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“…Initial ideas and sketches of the transformations DNI-to-QPN and DNI-to-OMNeT++ were presented in the workshop papers [29,27]. Since then, the transformations have been finalized and extended.…”

Section: Transformationsmentioning

confidence: 99%

“…A single unit of traffic is a message of a given size (that in reality represents a, so called, packet train or flow) and the traffic patterns can be modeled at the granularity of messages. In the QPN model, we support distinction between end-and intermediate nodes (see [27]), however, every virtual node is modeled as a VM hosted on a physical node (independent of its nature: switch or server).…”

Section: Semantic Gapsmentioning

confidence: 99%

“…Transformation from DNI to miniDNI is performed automatically, so only an instance of the DNI must be provided at the input. (iii) We compare the three predictive models against existing performance models obtained by using the DNI model and the previous transformations partially introduced in [27,29]. We reveal the semantic gaps between the model abstractions and the different model solving techniques.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models

Rygielski

Kounev

Tran-Gia

2015

Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques

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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.

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“…Software contention effects, such as synchronization, simultaneous resource possession and blocking, can be easily described using QPNs [23]. In recent years, QPNs have been successfully used to model the performance of different types of software systems (e.g., component-based systems, event-based systems, database systems, computer networks, or multi-tenant systems [22,38,34,37,39], see 1 for more). The respective case studies use QPN models for the quantitative analysis of the performance and the scalability of a system under test.…”

Section: Introductionmentioning

confidence: 99%

Parallel Simulation of Queueing Petri Nets

Walter¹,

Spinner²,

Kounev³

2015

Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques

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Queueing Petri Nets (QPNs) are a powerful formalism to model the performance of software systems. Such models can be solved using analytical or simulation techniques. Analytical techniques suffer from scalability issues, whereas simulation techniques often require very long simulation runs. Existing simulation techniques for QPNs are strictly sequential and cannot exploit the parallelism provided by modern multi-core processors. In this paper, we present an approach to parallel discrete-event simulation of QPNs using a conservative synchronization algorithm. We consider the spatial decomposition of QPNs as well as the lookahead calculation for different scheduling strategies. Additionally, we propose techniques to reduce the synchronization overhead when simulating performance models describing systems with open workloads. The approach is evaluated in three case studies using performance models of real-world software systems. We observe speedups between 1.9 and 2.5 for these case studies. We also assessed the maximum speedup that can be achieved with our approach using synthetic models.

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Data Center Network Throughput Analysis Using Queueing Petri Nets

Cited by 13 publications

References 19 publications

On Utilizing Model Transformation for the Performance Analysis of Queueing Networks

On Utilizing Model Transformation for the Performance Analysis of Queueing Networks

Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models

Parallel Simulation of Queueing Petri Nets

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