Open AccessBook
Programming Google App Engine
Dan Sanderson
- 11 Oct 2012
91
TL;DR: This second edition is fully updated and expanded to cover Python 2.7 and Java 6 support, multithreading, asynchronous service APIs, and the use of frameworks such as Django 1.3 and webapp2.
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Abstract: Google App Engine makes it easy to create a web application that can serve millions of people as easily as serving hundreds, with minimal up-front investment. With Programming Google App Engine, Google engineer Dan Sanderson provides practical guidance for designing and developing your application on Googles vast infrastructure, using App Engines scalable services and simple development model.Through clear and concise instructions, youll learn how to get the most out of App Engines nearly unlimited computing power. This second edition is fully updated and expanded to cover Python 2.7 and Java 6 support, multithreading, asynchronous service APIs, and the use of frameworks such as Django 1.3 and webapp2.Understand how App Engine handles web requests and executes application code Learn about new datastore features for queries and indexes, transactions, and data modeling Create, manipulate, and serve large data files with the Blobstore Use task queues to parallelize and distribute computation across the infrastructure Employ scalable services for email, instant messaging, and communicating with web services Track resource consumption, and optimize your application for speed and cost effectiveness
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Citations
Cloud-Based Information Infrastructure for Next-Generation Power Grid: Conception, Architecture, and Applications
TL;DR: How different categories of the power applications can benefit from the cloud-based information infrastructure is discussed, including how to develop practical compute-intensive and data-intensive power applications by utilizing different layers provided by the state-of-the-art public cloud computing platforms.
77
A Survey of Cloud Storage Facilities
Hrishikesh Dewan,Ramesh C. Hansdah +1 more
- 04 Jul 2011
TL;DR: This paper describes storage services provided by three well-known cloud service providers and gives a comparison of their features with a view to characterize storage requirements of very large data sets as examples and it is hoped that it would act as a catalyst for the design of storage services forvery large data set requirements in future.
53
Cloud Architecture for Dynamic Service Composition
Mika Ylianttila,Jukka Riekki,Jiehan Zhou,Kumaripaba Athukorala,Ekaterina Gilman +4 more
- 01 Apr 2012
TL;DR: The authors introduce the CM4SC 'Composition as a Service' middleware layer into conventional Cloud architecture to allow automatic composition planning, service discovery and service composition and implement theCM4SC middleware prototype utilizing Windows Azure Cloud platform.
Evaluating High-Performance Computing on Google App Engine
TL;DR: An experimental approach employs the Google App Engine (GAE) for high-performance parallel computing and demonstrates good scalability of a Monte Carlo simulation algorithm.
37
Towards Migrating Genetic Algorithms for Test Data Generation to the Cloud
Sergio Di Martino,Filomena Ferrucci,Valerio Maggio,Federica Sarro +3 more
- 01 Jan 2013
TL;DR: This chapter shows how the use of the MapReduce paradigm can support the parallelization of Genetic Algorithms for test data generation and their migration in the Cloud, thus relieving software company from the management and maintenance of the overall IT infrastructure and developers from handling the communication and synchronization of parallel tasks.
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