This book constitutes the refereed post-conference proceedings of the 11th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2019, held in conjunction with the 45th International Conference on Very Large Databases (VLDB 2019) in August 2019. The 11 papers presented were carefully reviewed and focus on topics such as blockchain; big data and analytics; complex event processing; database Optimizations; data Integration; disaster tolerance and recovery; artificial Intelligence; emerging storage technologies (NVMe, 3D XPoint Memory etc.); hybrid workloads; energy and space efficiency; in-memory databases; internet of things; virtualization; enhancements to TPC workloads; lessons learned in practice using TPC workloads; collection and interpretation of performance data in public cloud environments.

This book constitutes the thoroughly refereed post-conference proceedings of the 8th TPC Technology Conference, on Performance Evaluation and Benchmarking, TPCTC 2017, held in conjunction with the43rd International Conference on Very Large Databases (VLDB 2017) in August/September 2017. The 12 papers presented were carefully reviewed and selected from numeroussubmissions. The TPC remains committed to developing new benchmark standards to keep pace with these rapid changes in technology.

Cloud service benchmarking can provide important, sometimes surprising insights into the quality of services and leads to a more quality-driven design and engineering of complex software architectures that use such services. Starting with a broad introduction to the field, this book guides readers step-by-step through the process of designing, implementing and executing a cloud service benchmark, as well as understanding and dealing with its results. It covers all aspects of cloud service benchmarking, i.e., both benchmarking the cloud and benchmarking in the cloud, at a basic level. The book is divided into five parts: Part I discusses what cloud benchmarking is, provides an overview of cloud services and their key properties, and describes the notion of a cloud system and cloud-service quality. It also addresses the benchmarking lifecycle and the motivations behind running benchmarks in particular phases of an application lifecycle. Part II then focuses on benchmark design by discussing key objectives (e.g., repeatability, fairness, or understandability) and defining metrics and measurement methods, and by giving advice on developing own measurement methods and metrics. Next, Part III explores benchmark execution and implementation challenges and objectives as well as aspects like runtime monitoring and result collection. Subsequently, Part IV addresses benchmark results, covering topics such as an abstract process for turning data into insights, data preprocessing, and basic data analysis methods. Lastly, Part V concludes the book with a summary, suggestions for further reading and pointers to benchmarking tools available on the Web. The book is intended for researchers and graduate students of computer science and related subjects looking for an introduction to benchmarking cloud services, but also for industry practitioners who are interested in evaluating the quality of cloud services or who want to assess key qualities of their own implementations through cloud-based experiments.

This book constitutes the refereed post-conference proceedings of the 12th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2020, held in August 2020.The 8 papers presented were carefully reviewed and cover the following topics: testing ACID compliance in the LDBC social network benchmark; experimental performance evaluation of stream processing engines made easy; revisiting issues in benchmarking metric selection; performance evaluation for digital transformation; experimental comparison of relational and NoSQL document systems; a framework for supporting repetition and evaluation in the process of cloud-based DBMS performance benchmarking; benchmarking AI inference; a domain independent benchmark evolution model for the transaction processing performance council.

Okay, so you understand what Cloud Computing is and why it's important. Now you need to adapt the way your organization evaluates and procures IT services to the new realities of the cloud marketplace. This book presents the world's first comprehensive set of metrics designed specifically for evaluating cloud services. It is designed to be an invaluable resource for professionals in both IT and Procurement and combines existing leading practices from the sourcing world with the new approaches required for cloud. In addition to the metrics themselves, a wealth of templates, scoring guidelines, leading practices and examples are provided so that this tool can be put to use immediately for real-world decision making. For any service evaluation, from the most strategic partnerships down to simple one-off projects, it's time to start avoiding the potentially costly mistakes and false starts that have accompanied many of the early attempts to source the cloud. No one involved in selecting or procuring cloud services should be without it.

A comprehensive guide to Fog and Edge applications, architectures, and technologies Recent years have seen the explosive growth of the Internet of Things (IoT): the internet-connected network of devices that includes everything from personal electronics and home appliances to automobiles and industrial machinery. Responding to the ever-increasing bandwidth demands of the IoT, Fog and Edge computing concepts have developed to collect, analyze, and process data more efficiently than traditional cloud architecture. Fog and Edge Computing: Principles and Paradigms provides a comprehensive overview of the state-of-the-art applications and architectures driving this dynamic field of computing while highlighting potential research directions and emerging technologies. Exploring topics such as developing scalable architectures, moving from closed systems to open systems, and ethical issues rising from data sensing, this timely book addresses both the challenges and opportunities that Fog and Edge computing presents. Contributions from leading IoT experts discuss federating Edge resources, middleware design issues, data management and predictive analysis, smart transportation and surveillance applications, and more. A coordinated and integrated presentation of topics helps readers gain thorough knowledge of the foundations, applications, and issues that are central to Fog and Edge computing. This valuable resource: Provides insights on transitioning from current Cloud-centric and 4G/5G wireless environments to Fog Computing Examines methods to optimize virtualized, pooled, and shared resources Identifies potential technical challenges and offers suggestions for possible solutions Discusses major components of Fog and Edge computing architectures such as middleware, interaction protocols, and autonomic management Includes access to a website portal for advanced online resources Fog and Edge Computing: Principles and Paradigms is an essential source of up-to-date information for systems architects, developers, researchers, and advanced undergraduate and graduate students in fields of computer science and engineering.

MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.