Virtual Infrastructure Management Framework for Cloud Computing
In this paper, we presented challenges involved in computational biology applications where enormous computing power and storage is required. In this context, we presented virtual infrastructure management framework Phoenix that aims to extend existing co
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Virtual Infrastructure Management Framework for Cloud Computing Guang He, KaiTian Chen, XuXiang Chen, ShiGui Cai and ShiWen Mao
Abstract In this paper, we presented challenges involved in computational biology applications where enormous computing power and storage is required. In this context, we presented virtual infrastructure management framework Phoenix that aims to extend existing command-line-based management tools into a Webbased tool with security added. This is an open source framework built above OpenNebula to support VirtualBox and other standard hypervisors. The drivers developed are seamlessly integrated with current codebase with the Web-based graphical front end. The Phoenix framework is to manage GenomicsCloud, the Cloud Computing environment aligned to specific needs of OMIC sciences applications. Performance results of Phoenix indicate that the overhead induced is very less compared with functionalities introduced in this ecosystem. Keywords Cloud Computing
Virtualization Next-generation sequencing
19.1 Introduction Cloud Computing demands high computing, storage and networking capabilities. The raw data generated by any experiment or in OMIC sciences often exceed terabytes of data and are already challenging the computational infrastructure typically available in many laboratories. Furthermore, biologic datasets are having exponential growth that doubles every 18 months [1]. These data are used to understand the genetic structure of a species and how to engineer them to the G. He (&) K. Chen X. Chen S. Cai S. Mao China Mobile Communications Group, Guangdong Co., Ltd, Zhuhai 519015, China e-mail: [email protected]
Y. Yang and M. Ma (eds.), Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 4, Lecture Notes in Electrical Engineering 226, DOI: 10.1007/978-3-642-35440-3_19, Springer-Verlag Berlin Heidelberg 2013
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benefit of mankind by either to cure some disease or engineering high-yield crops. The only long-term solution to the challenges posed by the massive datasets being generated is to combine computational biology research with advances from Cloud Computing. Systems biology is another faculty of research where multiple datasets from variety of species are examined. Large-scale data integration in systems biology has catalyzed identification of nearly all yeast mitochondrial proteins and many of their functional interactions, as well as how this knowledge has aided the search for new disease genes. The human candidate genes proposed can be tested back in the yeast, where cell-based assays can be performed in a high-throughput manner. All these types of research in systems biology and applications in OMIC sciences demand high-performance computing (HPC) and high storage capacity [2]. Cloud Computing [3] is emerging as a new style of distributed computing that adapts to dynamically scalable system resource requirements. With Cloud Computing resources, platform, infrastructure and software are dynamic
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