Principled design of the modern Web architecture
ACM Transactions on Internet Technology (TOIT)
Live data center migration across WANs: a robust cooperative context aware approach
Proceedings of the 2007 SIGCOMM workshop on Internet network management
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Bioinformatics
Budget-constrained bulk data transfer via internet and shipping networks
Proceedings of the 8th ACM international conference on Autonomic computing
Making time-stepped applications tick in the cloud
Proceedings of the 2nd ACM Symposium on Cloud Computing
I/O performance of virtualized cloud environments
Proceedings of the second international workshop on Data intensive computing in the clouds
The Effect of Firewall Testing Types on Cloud Security Policies
International Journal of Strategic Information Technology and Applications
ClouDiA: a deployment advisor for public clouds
Proceedings of the VLDB Endowment
An IT-infrastructure capability model
Proceedings of the ACM International Conference on Computing Frontiers
A Study on Linear Elastic FEM by Cloud Computing
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
FAIRIO: A Throughput-oriented Algorithm for Differentiated I/O Performance
International Journal of Parallel Programming
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Cloud computing has evolved in the commercial space to support highly asynchronous web 2.0 applications. Scientific computing has traditionally been supported by centralized federally funded supercomputing centers and grid resources with a focus on bulk-synchronous compute and data-intensive applications. The scientific computing community has shown increasing interest in exploring cloud computing to serve e-Science applications, with the idea of taking advantage of some of its features such as customizable environments and on-demand resources. Magellan, a recently funded cloud computing project is investigating how cloud computing can serve the needs of mid-range computing and future data-intensive scientific workloads. This paper summarizes the application requirements and business model needed to support the requirements of both existing and emerging science applications, as learned from the early experiences on Magellan and commercial cloud environments. We provide an overview of the capabilities of leading cloud offerings and identify the existent gaps and challenges. Finally, we discuss how the existing cloud software stack may be evolved to better meet e-Science needs, along with the implications for resource providers and middleware developers.