Cloud Computing for Data-Intensive Applications

Scalable 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.