Recommendations for Using QPN Formalism for Preparation of Incoming Request Stream Generator in Modeled System

Simulation models are elements of science that use software tools to solve complex mathematical problems. They are beneficial in areas such as performance engineering and communications systems. Nevertheless, to achieve more accurate results, researchers should use more detailed models. Having an analysis of the system operations in the early modeling phases could help one make better decisions relating to the solution. In this paper, we introduce the use of the QPME tool, based on queueing Petri nets, to model the system stream generator. This formalism was not considered during the first tool development. As a result of the analysis, an alternative design model is proposed. By comparing the behavior of the proposed generator against the one already developed, a better adjustment of the stream to the customer’s needs was obtained. The study results show that appropriately adjusting queueing Petri net models can help produce better streams of data (tokens).

Extending Alkenes’ Value Chain to Functionalized Polyolefins

Naphtha is one of the crude oil distillation products, bringing almost the lowest value-addition to crude oil, compared to other refinery products such as liquid petroleum gas, gasoline, and diesel. However, Naphtha can be converted to one of the highest value products at the end of the value chain, i.e., polyolefins. Although the production of conventional commodity polyolefins from crude oil, is considered as one of the final products in alkenes’ value chain, there are specialty polyolefins with higher values. Specialty polyolefins are small volume, high-performance thermoplastics with high-profit margins compared to traditional commodity polyolefins. Recently, some special purpose functionalized polyolefins have been developed as efficient substituents for high-performance engineering thermoplastics. Polyolefins are exploited as cost-effective platforms to produce these functionalized thermoplastics. They are promising candidates for replacing high-performance polymers with high-cost raw materials and elaborate production processes. So, functional polyolefins have introduced a new paradigm in the production of high-performance thermoplastics, extending the alkenes’ value chain and increasing profitability. High-performance specialty polyolefins may find exceptional markets in niche applications. In this chapter, the commercial specialty and functional polyolefins’ current situation and prospects are reviewed.

Cloud Function Performance: a component modeling approach

Cloud Functions are a trend in cloud computing in which developers are allowed to install code in a Function-as-a-Service (FaaS) platform able to manage provisioning, execution, monitoring and automatic scaling. The underlying infrastructure in FaaS platforms is hidden from the developers and designers and, since the inuence of the infrastructure is unknown, this makes it di_cult to apply software performance engineering approaches on cloud functions, which could lead to wrong or inaccurate performance estimations. In this study, we explore the use of component-based modeling and simulation in order to generate performance estimations of an exemplar cloud function which was exercised using a variety of workloads. A cloud function was both implemented and instrumented to record performance datain a log _le, associated with its invocations; using the log _le as an input, we extracted a performance model in a Palladio Component Model format suitable for running simulations to validate whether the generated model could explain the runtime behavior of the function. Using this approach and further tunings in the model, we were able to validate that the simulations could explain more than 95% of the function's behavior and that component-based modeling and simulation can be considered a serious option when trying to explain the behavior of a cloud function.

Early Performance Prediction in Bioinformatics Systems Using Palladio Component Modeling

Bioinformatics is a branch of science that uses computers, algorithms, and databases to solve biological problems. To achieve more accurate results, researchers need to use large and complex datasets. Sequence alignment is a well-known field of bioinformatics that allows the comparison of different genomic sequences. The comparative genomics field allows the comparison of different genomic sequences, leading to benefits in areas such as evolutionary biology, agriculture, and human health (e.g., mutation testing connects unknown genes to diseases). However, software engineering best practices, such as software performance engineering, are not taken into consideration in most bioinformatics tools and frameworks, which may lead to serious performance problems. Having an estimate of the software performance in the early phases of the Software Development Life Cycle (SDLC) is beneficial in making better decisions relating to the software design. Software performance engineering provides a reliable and observable method to build systems that can achieve their required performance goals. In this paper, we introduce the use of the Palladio Component Modeling (PCM) methodology to predict the performance of a sequence alignment system. Software performance engineering was not considered during the original system development. As a result of the performance analysis, an alternative design is proposed. Comparing the performance of the proposed design against the one already developed, a better response time is obtained. The response time of the usage scenario is reduced from 16 to 8.6 s. The study results show that using performance models at early stages in bioinformatics systems can help to achieve better software system performance.