There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[서평]「The Unified Modeling Language User Guide」

Over the last decade, a lot of research has been directed toward integrating performance analysis into the software development process. Traditional software development methods focus on software correctness, introducing performance issues later in the development process. This approach does not take into account the fact that performance problems may require considerable changes in design, for example, at the software architecture level, or even worse at the requirement analysis level. Several approaches were proposed in order to address early software performance analysis. Although some of them have been successfully applied, we are still far from seeing performance analysis integrated into ordinary software development. In this paper, we present a comprehensive review of recent research in the field of model-based performance prediction at software development time in order to assess the maturity of the field and point out promising research directions.

Model-based performance prediction in software development: a survey

The Palladio component model for model-driven performance prediction

is a desirable attribute of a network, system, or process. Poor scalability can result in poor system performance, necessitating the reengineering or duplication of systems. While scalability is valued, its characteristics and the characteristics that undermine it are usually only apparent from the context. Here, we attempt to define different aspects of scalability, such as structural scalability and load scalability. Structural scalability is the ability of a system to expand in a chosen dimension without major modifications to its architecture. Load scalability is the ability of a system to perform gracefully as the offered traffic increases. It is argued that systems with poor load scalability may exhibit it because they repeatedly engage in wasteful activity, because they are encumbered with poor scheduling algorithms, because they cannot fully take advantage of parallelism, or because they are algorithmically inefficient. We qualitatively illustrate these concepts with classical examples from the literature of operating systems and local area networks, as well as an example of our own. Some of these are accompanied by rudimentary delay analysis.

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Characteristics of scalability and their impact on performance

Foreword by Grady Booch. Foreword by Paul Clements. Preface. I. INTRODUCTION AND OVERVIEW. 1. Introduction. Software and Performance. Responsiveness. Scalability. The Importance of Performance. Consequences of Performance Failures. Causes of Performance Failures. Getting It Right. How Should You Manage Performance? Reactive Performance Management. Proactive Performance Management. Software Performance Engineering. SPE Modeling Strategies. SPE Models. SPE for Object-Oriented Systems. What Does It Cost? What Do You Need? Summary. 2: SPE Quick View. SPE Process for Object-Oriented Systems. Case Study. Assess Performance Risk (Step 1). Identify Critical Use Cases (Step 2). Select Key Performance Scenarios (Step 3). Establish Performance Objectives (Step 4). Construct Performance Models (Step 5). Determine Software Resource Requirements (Step 6). Add Computer Resource Requirements (Step 7). Evaluate the Models (Step 8). Verify and Validate the Models (Step 9). SPE in the Unified Software Process. Performance Solutions. Performance Principles. Performance Patterns. Performance Antipatterns. Implementation Solutions. Summary. 3: SPE and the UML. Overview. Extending the UML. Stereotypes. Tagged Values. Constraints. Use Cases and Scenarios. Use Cases. Scenarios. Extensions to Sequence Diagram Notation. Instance Decomposition. Looping, Alternation, and References. Specifying Time. Timing Marks. Time Expressions. Timing Constraints. Time in Sequence Diagrams. Concurrency. Threads and Processes. Coregions. Parallel Composition. Synchronization. Summary. II. SPE MODELS. 4: Software Execution Models. Purpose. Representing Software Execution Models. Execution Graphs. Execution Graph Restrictions. Model Solutions. Basic Solution Algorithms. More Advanced Solution Techniques. Analysis Procedures. Execution Graphs from Sequence Diagrams. ICAD Case Study. Architecture 1. Architecture 2. Analysis of Results. Architecture 3. Modeling Hints. Summary. 5: Web Applications and Other Distributed Systems. Introduction. Web Applications. Distributed Object Technology. Middleware. Limitations of Distributed Object Technology. Effective Development with Distributed Object Technology. Modeling Distributed System Interactions. Types of System Interactions. Software Execution Model Representation. Representing Middleware Overhead. Software Model Solution Approximations. Example: Web e-Commerce Application. Database Scenario. Order Process Scenario. Example Summary. Modeling Hints. Summary. 6: System Execution Models. Introduction. System Model Basics. Performance Metrics. Solving the Queueing Model. Networks of Queues. Deriving System Model Parameters from Software Model Results. Using the System Model for SPE. Advanced System Models. Alternate Solution Methods. Schedulability. Distributed System Case Study. Synchronization Model. Modeling Hints. Summary. III. DATA COLLECTION. 7: SPE Data Collection. Introduction. SPE Data Requirements. Key Performance Scenarios. Performance Objectives. Execution Environment. Software Resource Requirements. Computer Resource Requirements. Data Gathering Issues. Performance Walkthrough. Topics. When to Conduct Performance Walkthroughs. Example. Tips for a Successful Performance Walkthrough. Resource Estimation Techniques. Use Measurements. Study Measurements. Use a Mentor. Best-Worst Case Estimates. What to Estimate. Estimating I/O Requirements. Estimating Network Messages Obtaining Computer Resource Requirements. Summary. 8: Software Measurement and Instrumentation. Introduction. What Should You Measure? Workload Data and Data Characteristics. Path Characteristics. Software Resources and Processing Overhead. Computer Resource Usage. Planning for Performance Measurement. Key Considerations. Performance Benchmarks. Designing and Conducting Measurement Studies. Performance Measurement Concepts. Terminology. Factors That May Affect Measurements. Data Collection Techniques and Tools. Data Collection Techniques. Measuring SPE Data. Instrumentation. Instrumentation Design Considerations. Implementation Alternatives. Data Reporting. Application Resource Measurement. Summary. IV. PERFORMANCE SOLUTIONS. 9: Performance-Oriented Design. Principles for Performance-Oriented Design. Performance Control Principles. Performance Objectives Principle. Instrumenting Principle. Independent Principles. Centering Principle. Fixing-Point Principle. Locality Principle. Processing Versus Frequency Principle. Synergistic Principles. Shared Resources Principle. Parallel Processing Principle. Spread-the-Load Principle. Using the Principles. Summary. 10: Performance Patterns. Overview. Fast Path. Problem. Solution. Benefits. Consequences. First Things First. Problem. Solution. Benefits. Consequences. Coupling. Problem. Solution. Benefits. Consequences. Batching. Problem. Solution. Benefits. Consequences. Alternate Routes. Problem. Solution. Benefits. Consequences. Flex Time. Problem. Solution. Benefits. Consequences. Slender Cyclic Functions. Problem. Solution. Benefits. Consequences. Summary. 11: Performance Antipatterns. Overview. The "god" Class. Problem. Solution. Excessive Dynamic Allocation. Problem. Solution. Circuitous Treasure Hunt. Problem. Solution. The One-Lane Bridge. Problem. Solution. Traffic Jam. Problem. Solution. Summary. 12: Implementation Solutions. Overview. Performance Tuning. General Performance Solutions. Fast Path Speed-Up. Improving Scalability. Algorithm and Data Structure Choices. Time Versus Space Trade-Offs. Hardware/Software Platform Dependencies. Performance Solutions for Object-Oriented Software. Language-Independent Solutions. C++ Solutions. Java Solutions. Summary. V. APPLICATIONS. 13: Web Applications. Introduction. Performance Issues. SPE Models for Web Applications. Case Study: Nachtfliegen.com. Plan Itinerary Scenario. Software Model. Hardware/Software Environment. Resource Requirements. Software Model Solution. Performance Improvements. System Execution Model. Sensitivity and Scalability Analysis. Typical Performance Problems. Summary. 14: Embedded Real-Time Systems. Introduction. Embedded Real-Time Systems Background. Timing Requirements. Hardware Constraints. Real-Time Operating Systems. Distributed Systems. Database. Performance Issues. Response Time and Throughput. Schedulability. SPE Models for Embedded Real-Time Systems. Case Study: Telephony Switching. Overview. Architecture and Design. Typical Performance Problems. Summary. VI. MAKING SPE HAPPEN. 15: The SPE Process. Introduction. The SPE Process. Assess Performance Risk. Identify Critical Use Cases. Select Key Performance Scenarios. Establish Performance Objectives. Construct Performance Models. Determine Software Resource Requirements. Add Computer Resource Requirements. Evaluate the Models. Verify and Validate Models. Late Life Cycle SPE Activities. More Detailed Models. More Precise Data. Performance Testing. Baseline Models. Post-Deployment Performance Management. Evolutionary Changes. Capacity Management. SPE Artifacts. Performance Management Plans. Performance V&V Plan. SPE Configuration Management Plan. Performance Drivers. Performance Scenarios. Performance Objectives. Execution Environment Specifications. Performance Models. Model Results. Performance Instrumentation. Performance V&V Reports. Performance Test Plans. Performance Test Results. Integrating SPE Into Your Software Process. The Waterfall Model. The Spiral Model. SPE in the Unified Process. Summary. 16: Implementing SPE. Introduction. Tools. Modeling Tools. Development Tools. SPE Adoption and Use. Experience. Key Considerations. Pilot Projects. Critical Success Factors for Adoption and Use. SPE Implementation Strategies. Organizational Issues. Who Pays for SPE? Costs. Risks. Critical Factors for Successful Projects. SPE Future. Summary. VII. APPENDIXES. Appendix A: UML Notation. Use Case Diagrams. Sequence Diagrams. Basic Sequence Diagrams. Augmented Sequence Diagrams. Deployment Diagrams. Stereotypes, Tagged Values, and Constraints. Stereotypes. Tagged Values. Constraints. Appendix B: SPE Modeling Notations. Execution Graph Notation. Basic Nodes. Synchronization Nodes. Information Processing Graph Notation. Bibliography. Index. 0201722291T09102001

New Book - Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software.

A pattern is a common solution to a problem that occurs in many different contexts. Patterns capture expert knowledge about “best practices” in software design in a form that allows that knowledge to be reused and applied in the design of many different types of software. Antipatterns are conceptually similar to patterns in that they document recurring solutions to common design problems. They are known as antipatterns because their use (or misuse) produces negative consequences. Antipatterns document common mistakes made during software development as well as their solutions. While both patterns and antipatterns can be found in the literature, they typically do not explicitly consider performance consequences. This paper explores antipatterns from a performance perspective. We discuss performance problems associated with one well-known design antipattern and show how to solve them. We also propose three new performance antipatterns that often occur in software systems.

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Software performance antipatterns

There is growing recognition of the importance of the role of architecture in determining the quality attributes, such as modifiability, reusability, reliability, and performance, of a software system. While a good architecture cannot guarantee attainment of quality goals, a poor architecture can prevent their achievement. This paper discusses assessment of the performance characteristics of software architectures. We describe the information required to perform such assessments and discuss how it can be extracted from architectural descriptions. The process of evaluating the performance characteristics of a software architecture is described and illustrated with a simple case study.

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Performance evaluation of software architectures

Architectural decisions are among the earliest made in a software development project. They are also the most costly to fix if, when the software is completed, the architecture is found to be inappropriate for meeting quality objectives. Thus, it is important to be able to assess the impact of architectural decisions on quality objectives such as performance and reliability at the time that they are made.This paper describes PASA, a method for performance assessment of software architectures. It was developed from our experience in conducting performance assessments of software architectures in a variety of application domains including web-based systems, financial applications, and real-time systems. PASA uses the principles and techniques of software performance engineering (SPE) to determine whether an architecture is capable of supporting its performance objectives. The method may be applied to new development to uncover potential problems when they are easier and less expensive to fix. It may also be used when upgrading legacy systems to decide whether to continue to commit resources to the current architecture or migrate to a new one. The method is illustrated with an example drawn from an actual assessment.

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PASASM: a method for the performance assessment of software architectures

Although object-oriented methods have been shown to help construct software systems that are easy to understand and modify, have a high potential for reuse, and are relatively quick and easy to implement, concern over performance of object-oriented systems represents a significant barrier to its adoption. Our experience has shown that it is possible to design object-oriented systems that have adequate performance and exhibit the other qualities, such as reusability, maintainability, and modifiability, that have made OOD so successful. However, doing this requires careful attention to performance goals throughout the life cycle. This paper describes the use of SPE·ED, a performance modeling tool that supports the SPE process, for early life cycle performance evaluation of object-oriented systems. The use of SPE·ED for performance engineering of object-oriented software is illustrated with a simple example.

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Lloyd G. Williams

Papers

New Book - Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software.

Software performance antipatterns

Performance evaluation of software architectures

PASASM: a method for the performance assessment of software architectures

Performance Engineering Evaluation of Object-Oriented Systems with SPE*ED