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.

Automatic Feedback Generation in Software Performance Engineering: A Review

Automation in generation of architectural feedback from performance indexes like probability distributions, mean values and variances has been of interest to the researchers from last decade. It is well established that due to the complexity in interpreting the performance indices obtained from performance analysis of software architecture and short time to the market, an automated approach is vital for acceptance of architecture based software performance engineering approach by software industry. In last decade some work has beendone in this direction. Aim of this paper is to explore the existing research in the field, which will be valuable for researchers looking forward to contributing to this research.