Reliability Assurance Of Big Data In The Cloud

With the rapid growth of Cloud computing, the size of Cloud data is expanding at a dramatic speed. A huge amount of data is generated and processed by Cloud applications, putting a higher demand on cloud storage. While data reliability should already be a requirement, data in the Cloud needs to be stored in a highly cost-effective manner. This book focuses on the trade-off between data storage cost and data reliability assurance for big data in the Cloud. Throughout the whole Cloud data lifecycle, four major features are presented: first, a novel generic data reliability model for describing data reliability in the Cloud; second, a minimum replication calculation approach for meeting a given data reliability requirement to facilitate data creation; third, a novel cost-effective data reliability assurance mechanism for big data maintenance, which could dramatically reduce the storage space needed in the Cloud; fourth, a cost-effective strategy for facilitating data creation and recovery, which could significantly reduce the energy consumption during data transfer. Captures data reliability with variable disk rates and compares virtual to physical disks Offers methods for reducing cloud-based storage cost and energy consumption Presents a minimum replication benchmark for data reliability requirements to evaluate various replication-based data storage approaches

With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data.

CLOUD COMPUTING SOLUTIONS The main purpose of this book is to include all the cloud-related technologies in a single platform, so that researchers, academicians, postgraduate students, and those in the industry can easily understand the cloud-based ecosystems. This book discusses the evolution of cloud computing through grid computing and cluster computing. It will help researchers and practitioners to understand grid and distributed computing cloud infrastructure, virtual machines, virtualization, live migration, scheduling techniques, auditing concept, security and privacy, business models, and case studies through the state-of-the-art cloud computing countermeasures. This book covers the spectrum of cloud computing-related technologies and the wide-ranging contents will differentiate this book from others. The topics treated in the book include: The evolution of cloud computing from grid computing, cluster computing, and distributed systems; Covers cloud computing and virtualization environments; Discusses live migration, database, auditing, and applications as part of the materials related to cloud computing; Provides concepts of cloud storage, cloud strategy planning, and management, cloud security, and privacy issues; Explains complex concepts clearly and covers information for advanced users and beginners. Audience The primary audience for the book includes IT, computer science specialists, researchers, graduate students, designers, experts, and engineers who are occupied with research.

Explores key challenges and solutions to assured cloud computing today and provides a provocative look at the face of cloud computing tomorrow This book offers readers a comprehensive suite of solutions for resolving many of the key challenges to achieving high levels of assurance in cloud computing. The distillation of critical research findings generated by the Assured Cloud Computing Center of Excellence (ACC-UCoE) of the University of Illinois, Urbana-Champaign, it provides unique insights into the current and future shape of robust, dependable, and secure cloud-based computing and data cyberinfrastructures. A survivable and distributed cloud-computing-based infrastructure can enable the configuration of any dynamic systems-of-systems that contain both trusted and partially trusted resources and services sourced from multiple organizations. To assure mission-critical computations and workflows that rely on such systems-of-systems it is necessary to ensure that a given configuration does not violate any security or reliability requirements. Furthermore, it is necessary to model the trustworthiness of a workflow or computation fulfillment to a high level of assurance. In presenting the substance of the work done by the ACC-UCoE, this book provides a vision for assured cloud computing illustrating how individual research contributions relate to each other and to the big picture of assured cloud computing. In addition, the book: Explores dominant themes in cloud-based systems, including design correctness, support for big data and analytics, monitoring and detection, network considerations, and performance Synthesizes heavily cited earlier work on topics such as DARE, trust mechanisms, and elastic graphs, as well as newer research findings on topics, including R-Storm, and RAMP transactions Addresses assured cloud computing concerns such as game theory, stream processing, storage, algorithms, workflow, scheduling, access control, formal analysis of safety, and streaming Bringing together the freshest thinking and applications in one of today’s most important topics, Assured Cloud Computing is a must-read for researchers and professionals in the fields of computer science and engineering, especially those working within industrial, military, and governmental contexts. It is also a valuable reference for advanced students of computer science.

Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques Presents case studies involving enterprise, business, and government service deployment of big data applications Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

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

This vital work for researchers and graduate students focuses on resilience estimation and control of cyber-physical networked systems using attacker-defender game theory. It presents attack and defense strategies and describes the design and resilience of control systems to withstand cyberattacks. Complex control systems, including cyber-physical and cloud control systems, are in open network environments and are often confronted with threats from cyberspace, physical space and even cloud service. With diversified and intelligent attack patterns and improvements in attack capabilities, non-contact damage can be widespread. In this book, the authors use a formal, mathematical approach to introduce their recent research findings to describe and design attack and defense strategies using game theoretic method. The book is divided into three sections, focusing on strategies for resilience against deception attacks and DoS attacks, and protecting cloud control systems against threats. In these sections, the authors address topics such as secure and distributed filtering, attack detection and disturbance rejection, resilient state estimation, and resilient control, and techniques such as Stackelberg games, hierarchical games, and active eavesdropping. Through this book readers will be able to design effective defense strategies for complex control system to achieve resilience for closed-control cyber physical systems, network and cloud systems. This book is a vital resource for graduate students and academic researchers who are familiar with the concepts related to cyberattack and defense and who have a related research background. To maximize their benefit from this book, readers are recommended to have a strong mathematical foundation as the book takes a mathematical approach to the concepts and strategies described within.

Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details-making them unsuitable to R&D people. To fill such a need, Big Data: Storage, Sharing, and Security examines Big Data management from an R&D perspective. It covers the 3S desi