^lxlt Scientific 2^Vl I Publishing

Transcription

1 Automated Experiments for Deriving Performance-relevant Properties of Software Execution Environments by Michael Hauck ^lxlt Scientific 2^Vl I Publishing

2 Contents 1. Introduction I l.l. Motivation I l.2. Problem Shortcomings of Existing Solutions Contributions Validation Outline Foundations Software Performance Analysis Software Performance Software Performance Engineering Performance Experiments and Benchmarking The Palladio Component Model Model-driven Software Development Models and Metamodels The Eclipse Modeling Project Operating System Scheduling and Virtualization Operating System Scheduling Detecting CPU and OS Scheduling Properties Virtualization 34 xi

3 3. An Approach For Deriving Execution Environment Properties Research Context A Definition of the Execution Environment Performance-relevant Properties of the Execution Environment Separating the Execution Environment Model from the Software Architecture Model Scientific Challenges A Method for Automated Derivation of Execution Environment Properties Experiment Design Experiment Execution Scenarios Limitations and Assumptions Summary Model-based Definition and Execution of Execution Environment Experiments Automated Execution Environment Experiments Requirements Experiment Structure Experiment Library and Experiment Domains Parametric Experiments A Metamodel for Specifying Experiments Experiments Experiment Logic Definition Experiment Tasks Experiment Sensors Example 90 xn

4 4.5. Experiment Execution and Results Analysis Experiment Execution Results Analysis A Template for Experiment Description Sections of the Experiment Template Describing the Experiment Logic Extensibility of the Approach Experiments Experiment domains Experiment tasks and sensors Analysis logic Experiment Performance Overhead Summary Deriving CPU and OS Scheduling Properties Experiments Overview Scientific Challenges Ill 5.3. CPU Simultaneous Multithreading Motivation Experiment Design Experiment Template Experiment Robustness and Performance Example Number of CPU Cores Motivation Experiment Design Experiment Template Experiment Robustness and Performance Example Operating System Scheduler Timeslice Length Motivation 134 xiii

5 Experiment Design Experiment Template Experiment Robustness Experiment Performance Example Operating System Scheduler Load-balancing Properties Motivation Initial Load-balancing Strategy Dynamic Load-balancing Strategy Including Experiment Results in Performance Prediction Validation Validation Scenario Execution Results Discussion Limitations and Assumptions Summary Deriving Visualization Properties Experiments Overview Scientific Challenges Virtualization Overhead Motivation Experiment Design Experiment Template Experiment Robustness Experiment Performance including Experiment Results in Performance Prediction Validation 188 xiv

6 6.4. Load-dependent Overhead Motivation Experiment Design Experiment Template Experiment Robustness and Performance Including Experiment Results in Performance Prediction Validation Discussion Additional Load Limitations and Assumptions Summary Related Work Modeling the Execution Environment for Performance Prediction Deriving Performance Models through Automated Measurements Performance Analysis Reflecting CPU and OS Scheduling Properties Performance Analysis of Virtualized Environments Summary Conclusions Summary Limitations and Assumptions Further Application Areas Future Work 257 A. Ginpex Metamodel 263 A. 1. Control Flow Tasks 264 A.2. Stop Conditions 267 xv

7 A.3. Machine Tasks 269 A.4. Distributions 271 A. 5. Sensors 272 B. Presented Experiments 275 B. l. CPU Simultaneous Multithreading 275 B.2. Detect Number of Available CPU Cores 276 B.3. Detect OS Scheduler Timeslice Length 278 B.4. Detect OS Scheduler Initial Load-balancing Strategy B.5. Detect OS Scheduler Dynamic Load-balancing Strategy. 282 B.6. Detect Virtualization Overhead 283 B.7. Detect Load-dependent Virtualization Overhead 285 List of Figures 289 xvi