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This chapter is from the book

1.9 A Road Map through the Book

Performance metrics are described in Chapter 2. One needs performance metrics to be able to define the desired performance characteristics of a system, and to describe the characteristics of the performance of an existing system. In the absence of metrics, the performance requirements of a system can be discussed only in vague terms, and the requirements cannot be specified, tested, or enforced.

Basic performance modeling and analysis are discussed in Chapter 3. We show how to establish upper bounds on system throughput and lower bounds on system response time given the amount of time it takes to do processing and I/O. We also show how rudimentary queueing models can be used to make predictions about system response time when a workload has the system to itself and when it is sharing the system with other workloads.

In Chapter 4 we explore methods of characterizing the workload of a system. We explain that workload characterization involves understanding what the system does, how often it is required to do it, why it is required to do it, and the performance implications of the nature of the domain of application and of variation in the workload over time.

Once the workload of the system has been identified and understood, we are in a position to identify performance requirements. The correct formulation of performance requirements is crucial to the choice of a sound, cost-effective architecture for the desired system. In Chapter 5 we describe the necessary attributes of performance requirements, including linkage to business and engineering needs, traceability, clarity, and the need to express requirements unambiguously in terms that are measurable, testable, and verifiable. These are preconditions for enforcement. Since performance requirements may be spelled out in contracts between a buyer and a supplier, enforceability is essential. If the quantities specified in a performance requirement cannot be measured, the requirement is deficient and unenforceable and should either be flagged as such or omitted. In Chapter 6 we discuss specific types of the ability of a system to sustain a given load, the metrics used to describe performance requirements, and performance requirements related to networking and to specific domains of application. In Chapter 7 we go into detail about how to express performance requirements clearly and how they can be managed.

One must be able to measure a system to see how it is functioning, to identify hardware and software bottlenecks, and to determine whether it is meeting performance requirements. In Chapter 8 we describe performance measurement tools and instrumentation that can help one do this. Instrumentation that is native to the operating system measures resource usage (e.g., processor utilization and memory usage) and packet traffic through network ports. Tools are available to measure activity and resource usage of particular system components such as databases and web application servers. Application-level measurements and load drivers can be used to measure system response times. We also discuss measurement pitfalls, the identification of incorrect measurements, and procedures for conducting experiments in a manner that helps us learn about system performance in the most effective way.

Performance testing is discussed in Chapter 9. We show how performance test planning is linked to both performance requirements and performance modeling. We show how elementary performance modeling methods can be used to interpret performance test results and to identify system problems if the tests are suitably structured. Among the problems that can be identified are concurrent programming bugs, memory leaks, and software bottlenecks. We discuss suitable practices for the documentation of performance test plans and results, and for the organization of performance test data.

In Chapter 10 we use examples to illustrate the progression from system understanding to model formulation and validation. We look at cases in which the assumptions underlying a conventional performance model might deviate from the properties of the system of interest. We also look at the phases of a performance modeling study, from model formulation to validation and performance prediction.

Scalability is a desirable attribute of systems that is frequently mentioned in requirements without being defined. In the absence of definitions, the term is nothing but a buzzword that will engender confusion at best. In Chapter 11 we look in detail at ways of characterizing the scalability of a system in different dimensions, for instance, in terms of its ability to handle increased loads, called load scalability, or in terms of the ease or otherwise of expanding its structure, called structural scalability. In this chapter we also provide examples of cases in which scalability breaks down and discuss how it can be supported.

Intuition does not always lead to correct performance engineering decisions, because it may be based on misconceptions about what scheduling algorithms or the addition of multiple processors might contribute to system performance. This is the reason Chapter 12, which contains a discussion of performance engineering pitfalls, appears in this book. In this chapter we will learn that priority scheduling does not increase the processing capacity of a system. It can only reduce the response times of jobs that are given higher priority than others and hence reduce the times that these jobs hold resources. Doubling the number of processors need not double processing capacity, because of increased contention for the shared memory bus, the lock for the run queue, and other system resources. In Chapter 12 we also explore pitfalls in system measurement, performance requirements engineering, and other performance-related topics.

The use of agile development processes in performance engineering is discussed in Chapter 13. We will explore how agile methods might be used to develop a performance testing environment even if agile methods have not been used in the development of the system as a whole. We will also learn that performance engineering as part of an agile process requires careful advance planning and the implementation of testing tools. This is because the time constraints imposed by short sprints necessitate the ready availability of load drivers, measurement tools, and data reduction tools.

In Chapter 14 we explore ways of learning, influencing, and telling the performance story to different sets of stakeholders, including architects, product managers, business executives, and developers.

Finally, in Chapter 15 we point the reader to sources where more can be learned about performance engineering and its evolution in response to changing technologies.

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