Cloud Networking for Big Data下载

weixin_39822095 2020-06-24 04:30:20

Based on the understanding of cloud networking technology, we further present
two case studies to provide high-level insights on how cloud networking technology
can benefit big data application on the perspective of cost-efficiency. With the
rising number of data centers all over the world, the elec
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大数据中的云网络（Cloud Networking for Big Data）-2015年Springer英文原版，0积分

Based on the understanding of cloud networking technology, we further present two case studies to provide high-level insights on how cloud networking technology can benefit big data application on the perspective of cost-efficiency. With the rising number of data centers all over the world, the electricity consumption and communication cost have been increasing drastically as the main operational expenditure (OPEX) to data centers.

Recent years have witnessed a deluge of network data propelled by the emerging online social media, user-generated video contents, and global-scale communi- cations, bringing people into the era of big data. Such network big data holds much critical and valuable information including customer experiences, user behaviors, service levels, and other contents, which could significantly improve the efficiency, effectiveness, and intelligence on the optimization of the current Internet, facilitate the smart network operation and management, and help service providers and content providers reduce capital expenditure (CapEx) and opera- tional expenditure (OpEx) while maintaining a relatively high-level quality of service (QoS) and quality of experience (QoE). Typical examples of network intelligence received from network big data include rapid QoE impairment detection and mitigation, optimization of network asset utilization, proactive maintenance, rapid outage restoration, and graceful disaster recovery. These aims can be achieved from high-level computational intelligence based on emerging analytical techniques such as big data pro- cessing, Web analytics, and network analytics employing software tools from advanced analytics disciplines such as machine learning, data mining, and pre- dictive analytics. The computational intelligence for big data analysis is playing an ever-increasingly important role in supporting the evolution of the current Internet toward the next-generation intelligent Internet. However, the unstructured, heterogeneous, sheer volume and complex nature of network big data pose great challenges on the computational intelligence of these emerging analytical techniques due to high computational overhead and communication cost, non-real-time response, sparse matrix-vector multi- plications, and high convergence time. It is therefore of critical importance to understand network big data and design novel solutions of computational intelligence, scaling up for big data analytics of large-scale networks to auto- matically discover the hidden and valuable information available for smart network operations, management, and optimization. This has been established as ix x Preface a new cross-discipline research topic in computer science, requiring anticipation of technical and practical challenges faced by mixed methods across multiple disciplines. In this book, we have invited world experts in this area to contribute the chapters that cover the following four parts: 1. Part1:BasicsofNetworkedBigData:Thisparthelpsunderstandtheprop- erties, characteristics, challenges, and opportunities of networked big data, geospatial data, and wireless big data. This part covers the following: a. Mathematical properties: A variety of aspects related to networks, including their topological and dynamical properties, as well as their applications to real-world examples b. Geospatial data and geospatial semantic web: Challenges and opportunities of the geospatial semantic web brought for sharing and utilizing big geospatial data c. Big data over wireless networks: Typical scenarios, various chal- lenges, and potential solutions for wireless transmission of big data 2. Part 2: Network Architecture for Big Data Transmissions: This part presents new proposals and network architectures to ensure efficient big data transmissions and streaming big data processing. a. Big data transfer: Challenges of bandwidth reservation service for efficient big data transfer and the potential solutions b. Internet of Things (IoT): A dynamic and independent Cloud com- puting architecture based on a service-oriented architecture for IoT devices, to allow users to freely transfer their IoT devices from one vendor to another c. Streamingbigdataprocessing:HowtomaximizeQoSandminimize OpEx when performing task scheduling and resource allocation in geo-distributed Clouds 3. Part3:AnalysisandProcessingofNetworkedBigData:Thispartexplains how to perform big data analytics based on emerging analytical techniques such as big data analytics, Web analytics, network analytics, and advanced analytics disciplines such as machine learning, data mining, and predictive analytics. This part covers the following areas: a. Alternatingdirectionmethodofmultiplier(ADMM):Itsapplications to large-scale network optimizations b. Dynamicnetworkmanagementandoptimization:Rethinkofcurrent network analysis, management and operation practices; impact of Preface xi network evolution on the computation of key network metrics; hyperbolic big data analytics c. Predictiveanalyticsandsmartretrieval:Utilizethenetworkbigdata by performing a data, information, knowledge, and wisdom (DIKW) hierarchy to the product of its processes d. Recommendation systems: Key challenges and solutions for data sparsity problem, data scale issue, and cold-start problem e. Coordinate gradient descent methods: Unconstrained convex mini- mization problems with differentiable objective function in network problems f. MapReduce: Data locality and dependency analysis; dependency- aware locality for MapReduce g. Distributed machine learning: Big data and big models for network big data; how to parallelize parameter updates on multiple work- ers; how to synchronize concurrent parameter updates performed by multiple workers h. Biggraph:Biggraphdecomposition;real-timeandlarge-scalegraph processing; big data security 4. Part 4: Emerging Applications of Networked Big Data: This part covers some emerging applications on the following: a. Intelligent mall shopping: Location-based mobile augmented real- ity applications; using network data to enable intelligent shopping; robust feature learning in cold-start heterogeneous-device localiza- tion; learning to query in the cold-start retailer content b. Networkanomalydetection:Howtoefficientlyusenetworkbigdata to perform accurate anomaly detection c. Transportation: Advances of spatial network big data (SNBD) tech- niques; challenges posed by SNBD in transportation applications and the potential solutions d. Biomedical and social media domain: Graph as a representation schema for big data; graph-based models and analyses in social text mining, and bioinformatics and biomedical e. Smart manufacturing: Big data characteristics in manufacturing; data collection and data mining in manufacturing; applications of big data in manufacturing. This book presents the state-of-the-art solutions to the theoretical and prac- tical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimiza- tion. In particular, the technical focus covers the comprehensive understanding xii Preface of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence. Targeted audiences: This book targets both academia and industry readers. Grad- uate students can select promising research topics from this book that are suitable for their thesis or dissertation research. Researchers will have a deep under- standing of the challenging issues and opportunities of network big data and can thus easily find an unsolved research problem to pursue. Industry engineers from IT companies, service providers, content providers, network operators, and equipment manufacturers can get to know the engineering design issues and cor- responding solutions after reading some practical schemes described in some chapters. We have required all chapter authors to provide as much technical detail as possible. Each chapter also includes references for readers’ further studies and investigations. If you have any comments or questions on certain chapters, please contact the chapter authors for more information. Thank you for reading this book. We wish that this book will help you with the scientific research and practical problems of network big data.

Building Your Next Big Thing with Google Cloud Platform shows you how to take advantage of the Google Cloud Platform technologies to build all kinds of cloud-hosted software and services for both public and private consumption. Whether you need a simple virtual server to run your legacy application or you need to architect a sophisticated high-traffic web application, Cloud Platform provides all the tools and products required to create innovative applications and a robust infrastructure to manage them. Google is known for the scalability, reliability, and efficiency of its various online products, from Google Search to Gmail. And, the results are impressive. Google Search, for example, returns results literally within fractions of second. How is this possible? Google custom-builds both hardware and software, including servers, switches, networks, data centers, the operating system’s stack, application frameworks, applications, and APIs. Have you ever imagined what you could build if you were able to tap the same infrastructure that Google uses to create and manage its products? Now you can! Building Your Next Big Thing with Google Cloud Platform shows you how to take advantage of the Google Cloud Platform technologies to build all kinds of cloud-hosted software and services for both public and private consumption. Whether you need a simple virtual server to run your legacy application or you need to architect a sophisticated high-traffic web application, Cloud Platform provides all the tools and products required to create innovative applications and a robust infrastructure to manage them. Using this book as your compass, you can navigate your way through the Google Cloud Platform and turn your ideas into reality. The authors, both Google Developer Experts in Google Cloud Platform, systematically introduce various Cloud Platform products one at a time and discuss their strengths and scenarios where they are a suitable fit. But rather than a manual-like "tell all" approach, the emphasis is on how to Get Things Done so that you get up to speed with Google Cloud Platform as quickly as possible. You will learn how to use the following technologies, among others: Google Compute Engine Google App Engine Google Container Engine Google App Engine Managed VMs Google Cloud SQL Google Cloud Storage Google Cloud Datastore Google BigQuery Google Cloud Dataflow Google Cloud DNS Google Cloud Pub/Sub Google Cloud Endpoints Google Cloud Deployment Manager Author on Google Cloud Platform Google APIs and Translate API Using real-world examples, the authors first walk you through the basics of cloud computing, cloud terminologies and public cloud services. Then they dive right into Google Cloud Platform and how you can use it to tackle your challenges, build new products, analyze big data, and much more. Whether you’re an independent developer, startup, or Fortune 500 company, you have never had easier to access to world-class production, product development, and infrastructure tools. Google Cloud Platform is your ticket to leveraging your skills and knowledge into making reliable, scalable, and efficient products—just like how Google builds its own products. What youll learn A brief introduction to Cloud Computing Distinctive characteristics of Google Cloud Platform The right way to do Authentication, Authorization to access Google Cloud Platform resources and user’s data An overview of Google Cloud Platform technologies including compute, storage, networking, Big Data and application services. Build, maintain and iterate over a backend infrastructure on Google Cloud Platform. Optimize and scale existing projects on Google Cloud Platform. Perform Big Data analytics using Google technologies. Host web services in Cloud Platform and orchestrate complex applications with ease. Architecture recipes using several Google Cloud Platform harmoniously Who this book is for Application Developers and Enterprise Architects seeking a robust and powerful cloud-hosted, backend, infrastructure for apps, services, research, and more. Table of Contents Part I: Introducing Cloud Computing and Google Cloud Platform Chapter 1: The Google Cloud Platform Difference Chapter 2: Getting Started with Google Cloud Platform Chapter 3: Using Google APIs Part II: Google Cloud Platform - Compute Products Chapter 4: Google Compute Engine Chapter 5: Google App Engine Chapter 6: Next Generation DevOps Initiatives Part III: Google Cloud Platform - Storage Products Chapter 7: Google Cloud SQL Chapter 8: Cloud Storage Chapter 9: Google Cloud Datastore Part IV: Google Cloud Platform - Big Data Products Chapter 10: Google BigQuery Chapter 11: Google Cloud Dataflow Chapter 12: Google Cloud Pub/Sub Part V: Google Cloud Platform - Networking and Services Chapter 13: Google Cloud DNS Chapter 14: Google Cloud Endpoints Part VI: Google Cloud Platform - Management and Recipes Chapter 15: Cloud Platform DevOps Toolbox Chapter 16: Architecture Recipes for Google Cloud Platform

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