Response Time Analysis of Distributed Web Systems Using QPNs

Rak, Tomasz

doi:10.1155/2015/490835

Cited by 12 publications

(19 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is highly flexible and low-cost. Experimental results [2] show that this method could be applied to more complex Web systems. Quantitative analysis of QPNs is susceptible to the state space explosion problem because it is based on Markov chains.…”

Section: Introductionmentioning

confidence: 96%

“…We prepared a simulation model to estimate customer response time. You can find in [2] validation of the developed method.…”

Section: Introductionmentioning

confidence: 99%

“…Followingly, we implement this model using the QPME tool [3] and verify system performance. Earlier, we proposed a kind of potential application of this method to bigger structure consumer-to-business interactions [2,5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling Web Client and System Behavior

Rak

2020

Information

Self Cite

View full text Add to dashboard Cite

Web systems are becoming more and more popular. An efficiently working network system is the basis for the functioning of every enterprise. Performance models are powerful tools for performance prediction. The creation of performance models requires significant effort. In the article, we want to present various performance models of customer and Web systems. In particular, we want to examine a system behaviour related to different flow routes of clients in the system. Therefore we propose Queueing Petri Nets, the new modeling methodology for dealing with performance issues of production systems. We follow the simulation-based approach. We consider 25 different models to check performance. Then we evaluate them based on the proposed metrics. The validation results show that the model is able to predict the performance with a relative error lower than 20%. Our evaluation shows that prepared models can reduce the effort of production system preparation. The resulting performance model can predict the system behaviour in a particular layer at the indicated load.

show abstract

Section: Introductionmentioning

confidence: 96%

“…We prepared a simulation model to estimate customer response time. You can find in [2] validation of the developed method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Web Client and System Behavior

Rak

2020

Information

Self Cite

View full text Add to dashboard Cite

show abstract

“…plied in a generic context for stream processing (Nalepa et al, 2015b) or distributed systems (Samolej and Rak, 2009;Rak, 2015). Generalised stochastic Petri nets (Chiola et al, 1993), the formalism for performance analysis that we adopt here, have been already used for the performance assessment of Apache Hadoop MapReduce (et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Apache Storm Applications: The NewsAsset Case Study

et al. 2018

View full text Add to dashboard Cite

The development of Information Systems today faces the era of Big Data. Large volumes of information need to be processed in realtime, for example, for Facebook or Twiter analysis. This paper addresses the redesign of NewsAsset, a commercial product that helps journalists by providing services, which analyze millions of media items from the social network in realtime. Technologies like Apache Storm can help enormously in this context. We have quantitatively analyzed the new design of NewsAsset to assess whether the introduction of Apache Storm can meet the demanding performance requirements of this media product. Our assessment approach, guided by the Unified Modeling Language (UML), takes advantage, for performance analysis, of the software designs already used for development. In addition, we converted UML into a domain-specific modeling language (DSML) for Apache Storm, thus creating a profile for Storm. Later, we transformed said DSML into an appropriate language for performance evaluation, specifically, stochastic Petri nets. The assessment ended with a successful software design that certainly met the scalability requirements of NewsAsset.

show abstract

“…Several approaches have been already presented in the literature for the modeling and performance assessment of stream applications [3], [4] or big data platforms [5], [6]. Some of these studies use variants of Petri nets, and they are applied in a generic context for stream processing [4] or distributed systems [7], [8]. Definitely, to the best of our knowledge, this is the first work devoted to the Apache Storm performance evaluation using formal methods.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Apache Storm Applications Using Stochastic Petri Nets

Requeno

Merseguer

Bernardi

2017

2017 IEEE International Conference on Information Reuse and Integration (IRI)

View full text Add to dashboard Cite

Real-time data-processing applications, such as those developed using Apache Storm, need to address highly demanding performance requirements. Engineers should assess these performance requirements while they configure their Storm designs to specific execution contexts, i.e., multiuser private or public cloud infrastructures. To this end, we propose a quality-driven framework for Apache Storm, that covers the following steps. The design with UML, using a novel profile for Apache Storm, allowing performance metrics definition. The transformation of the design into a performance model, concretely stochastic Petri nets. Last but not least, the simulation of the performance model and the retrieval of performance results.

show abstract

Response Time Analysis of Distributed Web Systems Using QPNs

Cited by 12 publications

References 17 publications

Modeling Web Client and System Behavior

Modeling Web Client and System Behavior

Quantitative Analysis of Apache Storm Applications: The NewsAsset Case Study

Performance Analysis of Apache Storm Applications Using Stochastic Petri Nets

Contact Info

Product

Resources

About