Tuesday, 12 December 2017

Tutorials

The Palladio Component Model

Authors:

Steffen Becker (Forschungszentrum Informatik)

Abstract:

The Palladio Component Model (PCM) has been developed over the last 5 years. Today it is a mature modeling language for modeling component-based or service-oriented software systems with a special focus on predicting extrafunctional properties of the system based on its constituting components. The PCM highly relies on model-driven software development techniques for this and uses automated transformations into well-known prediction models or simulation systems. It is supported by a mature, industry proven tool set based on the Eclipse platform. The tutorial presents the PCM’s foundational ideas from the area of component-based or service-oriented software development, its analysis capabilities, its tooling support, and possible extension points. In the component-based foundations, the tutorial defines the term component and presents components in different phases of their life-cycle. The discussion is completed by showing the PCM’s understanding of a typical component-based software development process and the developer roles involved into it. The way these developer roles collaborate highly impacts the way, how components are being modeled and parameterized in the PCM. The following part of the tutorial then focuses on performance predictions and the annotations necessary for this. It introduces the stochastic expression language (StoEx) which is used in the PCM to specify generally distributed stochastic and/or parametric performance annotations. Additionally, it shows how these annotations are being interpreted by the PCM’s analysis transformations. The last part of the tutorial introduces the PCM’s tool set and shows how to use it to create and analyze PCM models.

DOI: 10.1145/1712605.1712651

Full text: PDF

[#][]

Benchmarking Event Processing Systems: Current State and Future Directions

Authors:

Marcelo R. N. Mendes (Carleton University)
Pedro Bizarro (Carleton University)
Paulo Marques (Carleton University)

Abstract:

Complex Event Processing (CEP) has attracted a lot of interest from academia and industry in recent years. It has been employed in a variety of domains (e.g. financial, health-care, military) as a way of promptly detecting and reacting to the occurrence of certain events/situations of interest. However, as a relatively new area, many people are still unaware or unfamiliar with CEP.

The goal of this tutorial is therefore twofold: first to give a broad view of CEP to researchers and practitioners of the performance engineering community; second to share our experiences over the last months in the ambit of BiCEP, a research project at University of Coimbra that aims at devising standard benchmarks for CEP. We present the general principles behind the definition of benchmarks, the specific challenges and novelties found when benchmarking CEP systems, as well as the current state of the BiCEP project and its future directions. We also provide hands-on instruction on the FINCoS framework, a set of tools we have developed for carrying out experimental performance evaluation of CEP engines.

DOI: 10.1145/1712605.1712652

Full text: PDF

[#][]

Regression Techniques for Performance Parameter Estimation

Authors:

Murray Woodsid (Carleton University)

Abstract:

This tutorial describes how to use nonlinear regression techniques to fit the parameters of any kind of performance model to performance data measured at the boundaries of the system. The advantage of this approach, which has never been a standard practice in performance work, is that it avoids the need for intrusive monitoring of execution paths, such as profiling.

The topics covered will include:

  1. The estimation problem 
  2. Regression basics: normal equations, confidence intervals 
  3. Non-linear regression using iteration 
  4. Fitting a performance model into non-linear regression 
  5. Significance of model details (pruning insignificant details) 
  6. Examples

DOI: 10.1145/1712605.1712653

Full text: PDF

[#][]

Automatic Generation of Benchmark and Test Workloads

Authors:

Jozo Dujmovic San Francisco State University

Abstract:

In this tutorial, we describe techniques for automatic generation of benchmark and test workloads. Generated programs have adjustable parameters that are used to select the program size and structure, as well as the relative frequencies of basic operations (or program modules) that characterize the workload.

DOI: 10.1145/1712605.1712654

Full text: PDF

[#][]