Queueing Petri Nets-A formalism for the combined qualitative and quantitative analysis of systems

Bause, Falko

doi:10.1109/pnpm.1993.393439

Cited by 98 publications

(76 citation statements)

References 5 publications

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“…The next step is transforming the generated architectural models into performance models, and populating the performance models with input values such as operation durations and loop iteration counts. We perform this step in the context of performance models based on the QPN formalism [4].…”

Section: Constructing Performance Modelsmentioning

confidence: 99%

“…We start by analyzing the requirements on the automatic software system generation in Section 2 and detailing a particular generation mechanism in Section 3. In Section 4, we describe how we transform the architecture model of the generated software system into a performance model based on the Queueing Petri Nets (QPN) formalism [4]. Section 5 presents the initial results of the validation using the generation mechanism and the performance model outlined in the previous two sections.…”

mentioning

confidence: 99%

“…This gives rise to a multitude of dependencies that influence performance. Perhaps most notable are dependencies due to locking, which are usually captured by performance models, for example as special tasks in LQN [30] or special places in QPN [4]. Less frequently captured are dependencies due to argument passing, so far only captured in performance models solved through simulation [19].…”

mentioning

confidence: 99%

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Validating Model-Driven Performance Predictions on Random Software Systems

Babka

Tůma

Bulej

2010

Research Into Practice – Reality and Gaps

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Abstract. Software performance prediction methods are typically validated by taking an appropriate software system, performing both performance predictions and performance measurements for that system, and comparing the results. The validation includes manual actions, which makes it feasible only for a small number of systems. To significantly increase the number of systems on which software performance prediction methods can be validated, and thus improve the validation, we propose an approach where the systems are generated together with their models and the validation runs without manual intervention. The approach is described in detail and initial results demonstrating both its benefits and its issues are presented.Key words: performance modeling, performance validation, MDD MotivationState of the art in model-driven software performance prediction builds on three related factors: the availability of architectural and behavioral software models, the ability to solve performance models, and the ability to transform the former models into the latter. This is illustrated for example by the survey of modeldriven software performance prediction [3], which points out that the typical approach is to use UML diagrams for specifying both the architecture and the behavior of the software system, and to transform these diagrams into performance models based on queueing networks.Both the models and the methods involved in the prediction process necessarily include simplifying assumptions that help abstract away from some of the complexities of the modeled system, e.g., approximating real operation times with probability distributions or assuming independence of operation times. These simplifications are necessary to make the entire prediction process tractable, but the complexity of the modeled system usually makes it impossible to say how the simplifications influence the prediction precision.Self-archived copy. The original publication is available at www.springerlink.com,

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Section: Constructing Performance Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Validating Model-Driven Performance Predictions on Random Software Systems

Babka

Tůma

Bulej

2010

Research Into Practice – Reality and Gaps

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“…However, simply using a FCFS queue would not resolve the problem since arriving tokens would be served immediately and moved to the depository where their order of arrival will still be lost. To address this, we could exploit the generalized queue definition in [Bause, 1993] to define the scheduling strategy of place G's queue in such a way that tokens are served immediately according to FCFS, but only if the depository is empty. If there is a token in the depository, all tokens are blocked in their current position until the depository becomes free.…”

Section: Introducing Qpn Departure Disciplinesmentioning

confidence: 99%

“…The available tools and techniques for QPN analysis, including SimQPN, do not support queues with scheduling strategy dependent on the state of the depository. Indeed, the generalized queue definition given in [Bause, 1993], while theoretically powerful, is impractical to implement, so in practice it is rarely used and queues in QPNs are usually treated as conventional queues from queueing network theory. The way we address the problem is by introducing departure disciplines, which are a simple yet powerful feature we have added to SimQPN.…”

Section: Introducing Qpn Departure Disciplinesmentioning

confidence: 99%

On the Use of Queueing Petri Nets for Modeling and Performance Analysis of Distributed Systems

Kounev¹,

Buchmann²

2008

Petri Net, Theory and Applications

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Performance analysis of the internet‐protocol multimedia‐subsystem's control layer using a detailed queueing Petri‐net model

Roshani

Khorsandi

2018

Int J Communication

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In the current trend in telecommunications industry towards all-internetprotocol (IP) infrastructures, IP multimedia subsystem (IMS) plays a critical role by providing a coherent data and control-plane solution for large-scale live multimedia applications in a flexible and cost-effective manner. On the other hand, such a large-scale service platform would inevitably fail without effective support for the quality-of-service (QoS) requirements perceived by its users. Among the most important factors that influence user QoS are system performance and scalability. In this paper, a performance model for IMS systems is developed using the queueing Petri nets (QPNs) as the modeling formalism.The model's parameters are tuned based on the measurements carried out using a well-known IMS implementation. The model is validated against the real system. During the model calibration, the Java garbage-collector process used in the home subscriber-server (HSS) implementation was found to be a main factor in the discrepancy between the model and the reality. In addition, the effects of other factors such as the network stack in the operating system are investigated. The validated model is employed to give insights into the scalability of every single instance of IMS implementation. The model is extended to study load balancing among multiple instances of HSS to remove the main bottleneck in the system. It provides a valuable platform for resource management of various components of the IMS ecosystem to support the intended level of QoS for the users.

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Queueing Petri Nets-A formalism for the combined qualitative and quantitative analysis of systems

Cited by 98 publications

References 5 publications

Validating Model-Driven Performance Predictions on Random Software Systems

Validating Model-Driven Performance Predictions on Random Software Systems

On the Use of Queueing Petri Nets for Modeling and Performance Analysis of Distributed Systems

Performance analysis of the internet‐protocol multimedia‐subsystem's control layer using a detailed queueing Petri‐net model

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