Evalutation criteria to run scientific applications in the cloud / Eduardo Roloff, Alexandre da Silva Carissimi, and Philippe Olivier Alexandre Navaux -- Cloud-based infrastructure for data-intensive e-science applications: requirements and architecture / Yuri Demchenko, Canh Ngo, Paola Grosso, Cees de Laat, and Peter Membrey -- Securing cloud data / Sushmita Ruj and Rajat Saxena -- Adaptive execution of scientific workflow applications on clouds / Rodrigo N. Calheiros, Henry Kasim, Terence Hung, Xiaorong Li, Sifei Lu, Long Wang, Henry Palit, Gary Lee, Tuan Ngo, and Rajkumar Buyya -- Migrating e-science applications to the cloud: methodology and evaluation / Steve Strauch, Vasilios Andrikopoulos, Dimka Karastoyanova, and Karolina Vukojevic-Haupt -- Closing the gap between cloud providers and scientific users / David Susa, Harold Castro, and Mario Villamizar -- Assembling cloud-based geographic information systems: a pragmatic approach using off-the-shelf components / Muhammad Akmal, Ian Allison, and Horacio Gonz�alez-V�elez -- Hcloud, a healthcare-oriented cloud system with improved efficiency in biomedical data processing / Ye Li, Chenguang He, Xiaomao Fan, Xucan Huang, and Yunpeng Cai -- RPig: concise programming framework by integrating R with pig for big data analytics / MingXue Wang and Sidath B. Handurukande -- Autodock gateway for molecular docking simulations in cloud systems / Zolt�an Farkas, P�eter Kacsuk, Tam�as Kiss, P�eter Borsody, �Akos Hajnal, �Akos Balask�o, and Kriszti�an Kar�oczkai -- SaaS clouds supporting biology and medicine / Philip Church, Andrzej Goscinski, Adam Wong, and Zahir Tari -- Energy-aware policies in ubiquitous computing facilities / Marina Zapater, Patricia Arroba, Jos�e Luis Ayala Rodrigo, Katzalin Olcoz Herrero, and Jos�e Manuel Moya Fernandez
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The amount of data in everyday life has been exploding. This data increase has been especially significant in scientific fields, where substantial amounts of data must be captured, communicated, aggregated, stored, and analyzed. This book describes how cloud computing can significantly improve data management in different scientific fields, such as bioinformatics, earth science, and computer science. This book focuses on how cloud computing helps in the areas of resource control, vertical and horizontal scalability, and adaptive scheduling