Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science.

With the fast development of networking and software technologies, information processing infrastructure and applications have been growing at an impressive rate in both size and complexity, to such a degree that the design and development of high performance and scalable data processing systems and networks have become an ever-challenging issue. As a result, the use of performance modeling and m- surementtechniquesas a critical step in designand developmenthas becomea c- mon practice. Research and developmenton methodologyand tools of performance modeling and performance engineering have gained further importance in order to improve the performance and scalability of these systems. Since the seminal work of A. K. Erlang almost a century ago on the mod- ing of telephone traf c, performance modeling and measurement have grown into a discipline and have been evolving both in their methodologies and in the areas in which they are applied. It is noteworthy that various mathematical techniques were brought into this eld, including in particular probability theory, stochastic processes, statistics, complex analysis, stochastic calculus, stochastic comparison, optimization, control theory, machine learning and information theory. The app- cation areas extended from telephone networks to Internet and Web applications, from computer systems to computer software, from manufacturing systems to s- ply chain, from call centers to workforce management.

“If this book had been available to Healthcare.gov’s contractors, and they read and followed its life cycle performance processes, there would not have been the enormous problems apparent in that application. In my 40+ years of experience in building leading-edge products, poor performance is the single most frequent cause of the failure or cancellation of software-intensive projects. This book provides techniques and skills necessary to implement performance engineering at the beginning of a project and manage it throughout the product’s life cycle. I cannot recommend it highly enough.” –Don Shafer, CSDP, Technical Fellow, Athens Group, LLC Poor performance is a frequent cause of software project failure. Performance engineering can be extremely challenging. In Foundations of Software and System Performance Engineering, leading software performance expert Dr. André Bondi helps you create effective performance requirements up front, and then architect, develop, test, and deliver systems that meet them. Drawing on many years of experience at Siemens, AT&T Labs, Bell Laboratories, and two startups, Bondi offers practical guidance for every software stakeholder and development team participant. He shows you how to define and use metrics; plan for diverse workloads; evaluate scalability, capacity, and responsiveness; and test both individual components and entire systems. Throughout, Bondi helps you link performance engineering with everything else you do in the software life cycle, so you can achieve the right performance–now and in the future–at lower cost and with less pain. This guide will help you • Mitigate the business and engineering risk associated with poor system performance • Specify system performance requirements in business and engineering terms • Identify metrics for comparing performance requirements with actual performance • Verify the accuracy of measurements • Use simple mathematical models to make predictions, plan performance tests, and anticipate the impact of changes to the system or the load placed upon it • Avoid common performance and scalability mistakes • Clarify business and engineering needs to be satisfied by given levels of throughput and response time • Incorporate performance engineering into agile processes • Help stakeholders of a system make better performance-related decisions • Manage stakeholders’ expectations about system performance throughout the software life cycle, and deliver a software product with quality performance André B. Bondi is a senior staff engineer at Siemens Corp., Corporate Technologies in Princeton, New Jersey. His specialties include performance requirements, performance analysis, modeling, simulation, and testing. Bondi has applied his industrial and academic experience to the solution of performance issues in many problem domains. In addition to holding a doctorate in computer science and a master’s in statistics, he is a Certified Scrum Master.

Poor performance is one of the main quality-related shortcomings that cause software projects to fail. Thus, the need to address performance concerns early during the software development process is fully acknowledged, and there is a growing interest in the research and software industry communities towards techniques, methods and tools that permit to manage system performance concerns as an integral part of software engineering. Model-based software performance analysis introduces performance concerns in the scope of software modeling, thus allowing the developer to carry on performance analysis throughout the software lifecycle. With this book, Cortellessa, Di Marco and Inverardi provide the cross-knowledge that allows developers to tackle software performance issues from the very early phases of software development. They explain the basic concepts of performance analysis and describe the most representative methodologies used to annotate and transform software models into performance models. To this end, they go all the way from performance primers through software and performance modeling notations to the latest transformation-based methodologies. As a result, their book is a self-contained reference text on software performance engineering, from which different target groups will benefit: professional software engineers and graduate students in software engineering will learn both basic concepts of performance modeling and new methodologies; while performance specialists will find out how to investigate software performance model building.

As computers become more complex, the number and complexity of the tasks facing the computer architect have increased. Computer performance often depends in complex way on the design parameters and intuition that must be supplemented by performance studies to enhance design productivity. This book introduces computer architects to computer system performance models and shows how they are relatively simple, inexpensive to implement, and sufficiently accurate for most purposes. It discusses the development of performance models based on queuing theory and probability. The text also shows how they are used to provide quick approximate calculations to indicate basic performance tradeoffs and narrow the range of parameters to consider when determining system configurations. It illustrates how performance models can demonstrate how a memory system is to be configured, what the cache structure should be, and what incremental changes in cache size can have on the miss rate. A particularly deep knowledge of probability theory or any other mathematical field to understand the papers in this volume is not required.

Initially, computer systems performance analyses were carried out primarily because of limited resources. Due to ever increasing functional complexity of computational systems and user requirements, performance engineering continues to play a major role in software development. This book assesses the state of the art in performance engineering. Besides revised chapters drawn from two workshops on performance engineering held in 2000, additional chapters were solicited in order to provide complete coverage of all relevant aspects. The first part is devoted to the relation between software engineering and performance engineering; the second part focuses on the use of models, measures, and tools; finally, case studies with regard to concrete technologies are presented. Researchers, professional software engineers, and advanced students interested in performance analysis will find this book an indispensable source of information and reference.