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MenascT (computer science, George Mason U.) and Almeida (computer science, U. of Minas Gerais, Brazil) provide a quantitative analysis of Web service availability and a framework for understanding and planning Web services. They discuss benchmarking, load testing, workload forecasting, and performan
Under today’s shortened fiscal horizons and contracted time-to-market schedules, traditional approaches to capacity planning are seen by management as inflating production schedules. In the face of relentless pressure to get things done faster, this book facilitates rapid forecasting of capacity requirements, based on opportunistic use of available performance data and tools so that management insight is expanded but production schedules are not. The book introduces such concepts as an iterative cycle of improvement called "The Wheel of Capacity Planning," and Virtual Load Testing, which provides a highly cost-effective method for assessing application scalability.
A Blueprint Guide to capacity planning in a Solaris Environment by the foremost authority and bestselling author, Adrian Cockcroft.
Scalable and Secure Internet Services and Architecture provides an in-depth analysis of many key scaling technologies. Topics include: server clusters and load balancing; QoS-aware resource management; server capacity planning; Web caching and prefetching; P2P overlay network; mobile code and security; and mobility support for adaptive grid computi
Success on the web is measured by usage and growth. Web-based companies live or die by the ability to scale their infrastructure to accommodate increasing demand. This book is a hands-on and practical guide to planning for such growth, with many techniques and considerations to help you plan, deploy, and manage web application infrastructure. The Art of Capacity Planning is written by the manager of data operations for the world-famous photo-sharing site Flickr.com, now owned by Yahoo! John Allspaw combines personal anecdotes from many phases of Flickr's growth with insights from his colleagues in many other industries to give you solid guidelines for measuring your growth, predicting trends, and making cost-effective preparations. Topics include: Evaluating tools for measurement and deployment Capacity analysis and prediction for storage, database, and application servers Designing architectures to easily add and measure capacity Handling sudden spikes Predicting exponential and explosive growth How cloud services such as EC2 can fit into a capacity strategy In this book, Allspaw draws on years of valuable experience, starting from the days when Flickr was relatively small and had to deal with the typical growth pains and cost/performance trade-offs of a typical company with a Web presence. The advice he offers in The Art of Capacity Planning will not only help you prepare for explosive growth, it will save you tons of grief.
The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use
In this dissertation, a new spare capacity planning methodology is proposed utilizing path restoration. The approach is based on forcing working flows/traffic which are on paths that are disjoint to share spare backup capacity. The algorithm for determining the spare capacity assignment is based on genetic algorithms and is capable of incorporating non-linear variables such as non-linear cost function and QoS variables into the objective and constraints. The proposed methodology applies to a wider range of fault scenarios than most of the current literature. It can tolerate link-failures, node-failures, and link-and-node failures. It consists of two stages: the first stage generates a set of network topologies that maximize the sharing between backup paths by forcing them to use a subset of the original network. The second stage utilizes a genetic algorithm to optimize the set of solutions generated by the first stage to achieve an even better final solution. It can optimize the solution based on either minimizing spare capacity or minimizing the total network cost. In addition, it can incorporate QoS variables in both the objective and constraints to design a survivable network that satisfies QoS constraints. Numerical results comparing the proposed methodology to Integer Programming techniques and heuristics from the literature are presented showing the advantages of the technique. The proposed methodology was applied on 4 different size networks based on spare capacity optimization criteria and it was found that it achieved solutions that were on average 9.3% better than the optimal solution of the IP design that is based on link-restoration. It also achieved solutions that were on average 22.2 % better than the previous heuristic SLPA. The proposed methodology is very scalable. It was applied on networks with different sizes ranging from a 13-node network to a 70-node network. It was able to solve the 70-node network in less than one hour on a Pentium II PC. The curve-fitting of the empirical execution time of the methodology was found to be O(n3).
An oft-repeated adage among telecommunication providers goes, “There are ve things that matter: reliability, reliability, reliability, time to market, and cost. If you can’t do all ve, at least do the rst three. ” Yet, designing and operating reliable networks and services is a Herculean task. Building truly reliable components is unacceptably expensive, forcing us to c- struct reliable systems out of unreliable components. The resulting systems are inherently complex, consisting of many different kinds of components running a variety of different protocols that interact in subtle ways. Inter-networkssuch as the Internet span multiple regions of administrative control, from campus and cor- rate networks to Internet Service Providers, making good end-to-end performance a shared responsibility borne by sometimes uncooperative parties. Moreover, these networks consist not only of routers, but also lower-layer devices such as optical switches and higher-layer components such as rewalls and proxies. And, these components are highly con gurable, leaving ample room for operator error and buggy software. As if that were not dif cult enough, end users understandably care about the performance of their higher-level applications, which has a complicated relationship with the behavior of the underlying network. Despite these challenges, researchers and practitioners alike have made trem- dous strides in improving the reliability of modern networks and services.
In their early days, Twitter, Flickr, Etsy, and many other companies experienced sudden spikes in activity that took their web services down in minutes. Today, determining how much capacity you need for handling traffic surges is still a common frustration of operations engineers and software developers. This hands-on guide provides the knowledge and tools you need to measure, deploy, and manage your web application infrastructure before you experience explosive growth. In this thoroughly updated edition, authors Arun Kejariwal (MZ) and John Allspaw provide a systematic, robust, and practical approach to capacity planning—rather than theoretical models—based on their own experiences and those of many colleagues in the industry. They address the vast sea change in web operations, especially cloud computing. Understand issues that arise on heavily trafficked websites or mobile apps Explore how capacity fits into web/mobile app availability and performance Use tools for measuring and monitoring computer performance and usage Turn measurement data into robust forecasts and learn how trending fits into the planning process Examine related deployment concepts: installation, configuration, and management automation Learn how cloud autoscaling enables you to scale your app’s capacity up or down