000			03845nam a22005055i 4500
001			978-1-4939-1905-5
003			DE-He213
005			20200421112544.0
007			cr nn 008mamaa
008			141202s2014 xxu| s |||| 0|eng d
020			_a9781493919055 _9978-1-4939-1905-5
024	7		_a10.1007/978-1-4939-1905-5 _2doi
050		4	_aQA75.5-76.95
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072		7	_aCOM032000 _2bisacsh
082	0	4	_a005.7 _223
245	1	0	_aCloud Computing for Data-Intensive Applications _h[electronic resource] / _cedited by Xiaolin Li, Judy Qiu.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2014.
300			_aVIII, 427 p. 180 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aScalable Deployment of a LIGO Physics Application on Public Clouds:Workflow Engine and Resource Provisioning Techniques -- The FutureGrid Testbed for Big Data -- Cloud Networking to Support Data Intensive Applications -- IaaS cloud benchmarking: approaches, challenges, and experience -- Adaptive Workload Partitioning and Allocation for Data Intensive Scientific Applications -- Federating Advanced CyberInfrastructures with Autonomic Capabilities -- Executing Storm Surge Ensembles on PAAS Cloud -- Migrating Scientific Workflow Management Systems from the Grid to the Cloud -- Efficient Task-Resource Matchmaking Using Self-Adaptive Combinatorial Auction -- Cross-Phase Optimization in MapReduce -- DRAW: A New Data-gRouping-AWare Data Placement Scheme for Data Intensive Applications with Interest Locality -- Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation -- GPU-Accelerated Cloud Computing Data-Intensive Applications -- Big Data Storage and Processing on Azure Clouds: Experiments at Scale and Lessons Learned -- Storage and Data Lifecycle Management in Cloud  Environments with FRIEDA -- DTaaS: Data Transfer as a Service in the Cloud -- Supporting a Social Media Observatory with Customizable Index Structures - Architecture and Performance.
520			_aThis book presents a range of cloud computing platforms for data-intensive scientific applications. It covers systems that deliver infrastructure as a service, including: HPC as a service; virtual networks as a service; scalable and reliable storage; algorithms that manage vast cloud resources and applications runtime; and programming models that enable pragmatic programming and implementation toolkits for eScience applications. Many scientific applications in clouds are also introduced, such as bioinformatics, biology, weather forecasting and social networks. Most chapters include case studies. Cloud Computing for Data-Intensive Applications targets advanced-level students and researchers studying computer science and electrical engineering. Professionals working in cloud computing, networks, databases and more will also find this book useful as a reference.
650		0	_aComputer science.
650		0	_aComputer communication systems.
650		0	_aComputers.
650		0	_aDatabase management.
650	1	4	_aComputer Science.
650	2	4	_aInformation Systems and Communication Service.
650	2	4	_aComputer Communication Networks.
650	2	4	_aInformation Systems Applications (incl. Internet).
650	2	4	_aDatabase Management.
700	1		_aLi, Xiaolin. _eeditor.
700	1		_aQiu, Judy. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781493919048
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4939-1905-5
912			_aZDB-2-SCS
942			_cEBK
999			_c58472 _d58472