| Description |
1 online resource (xvi, 372 pages). |
| Series |
Scientific and Engineering Computation |
|
Scientific and engineering computation.
|
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Orienting in the cloud universe. Cloud : computer assistant, and platform ; The cloud landscape ; A guide to this book ; Accessing the cloud : web, APIs, and SDKs ; Tools used in this book ; Summary ; Resources -- Storage as a service. Three motivating examples ; Storage models ; The cloud storage landscape ; Summary ; Resources -- Using cloud storage services. Two access methods : portals and APIs ; Using Amazon Cloud storage services ; Using Microsoft Azure storage services ; Using Google Cloud storage services ; Using OpenStack Cloud storage services ; Transferring and sharing data with Globus ; Summary ; Resources -- Computing as a service. Virtual machines and containers ; Advanced computing services ; Serverless computing ; Pros and cons of public cloud computing ; Summary ; Resources -- Using and managing virtual machines. Historical roots ; Amazon's Elastic Compute cloud ; Azure VMs ; Google Cloud VM services ; Jetstream VM services ; Summary ; Resources -- Using and managing containers. Container basics ; Docker and the hub ; Containers for science ; Creating your own container ; Summary ; Resources -- Scaling deployments. Paradigms of parallel computing in the cloud ; SPMD and HPC-style parallelism ; Many task parallelism ; MapReduce and bulk synchronous parallelism ; Graph dataflow execution and Spark ; Agents and microservices ; HTCondor ; Summary ; Resources -- Data analytics in the cloud. Hadoop and YARN ; Spark ; Amazon Elastic MapReduce ; Azure HDInsight and Data Lake ; Amazon Athena analytics ; Google Cloud Datalab ; Summary ; Resources -- Streaming data to the cloud. Scientific stream examples ; Basic design challenges of streaming systems ; Amazon Kinesis and Firehose ; Kinesis, Spark, and the Array of things ; Streaming data with Azure ; Kafka, Storm, and Heron streams ; Google Dataflow and Apache Beam ; Apache Flink ; Summary ; Resources -- Machine learning in the cloud. Spark Machine learning Library (MLlib) ; Azure machine Learning Workspace ; Amazon Machine Learning platform ; Deep learning : a shallow introduction ; Amazon MXNet virtual machine image ; Google TensorFlow in the cloud; Microsoft Cognitive Toolkit ; Summary ; Resources -- The Globus Research Data Management platform. Challenges and opportunities of distributed data ; The Globus platform ; Identity and credential management ; Building a remotely accessible service ; The research data portal design pattern ; The portal design pattern revisited ; Closing the loop : from portal to graph service ; Summary ; Resources -- Building your own cloud with Eucalyptus (with Rich Wolski). IMplementing cloud infrastructure abstractions ; Deployment planning ; Single-cluster Eucalyptus cloud ; Summary ; Resources -- Building your own cloud with OpenStack (with Stig Telfer). OpenStack Core services ; HPC in an OpenStack environment ; Considerations for scientific workloads ; OpenStack deployment ; Example deployment ; Summary ; Resources -- Building your own SaaS. The meaning of SaaS ; SaaS architecture ; SaaS and science ; The Globus Genomics Bioinformatics System ; The Globus Research Data Management Service ; Summary ; Resources -- Security and privacy. Thinking about security in the cloud ; Role-based access control ; Secure data in the cloud ; Secure your VMs and containers ; Secure access to cloud software services ; Summary ; Resources -- History, critiques, futures. Historical perspectives ; Critiques ; Futures ; Resources -- Jupyter notebooks. Environment ; The notebooks ; Resources -- Afterword : a discovery cloud. |
| Summary |
"The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors" -- From the publisher. |
| Note |
Print version record. |
| Local Note |
MIT Press DTL OA MIT Titles |
| Subject |
Cloud computing.
|
|
Engineering -- Data processing.
|
|
Science -- Data processing.
|
|
Cloud computing. (OCoLC)fst01745899
|
|
Engineering -- Data processing.
(OCoLC)fst00910334
|
|
Science -- Data processing.
(OCoLC)fst01108207
|
| Added Author |
Gannon, Dennis B., 1947- author.
|
| Other Form: |
Print version: Foster, Ian, 1959- Cloud computing for science and engineering. Cambridge, Massachusetts : The MIT Press, [2017] 0262037246 9780262037242 (DLC) 2017016889 (OCoLC)981167298 |
| ISBN |
9780262037242 hardcover alkaline paper |
|
0262037246 hardcover alkaline paper |
|
