GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed wa

GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed way

Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.Peer ReviewedPostprint (author's final draft

Virtual Environment for Next Generation Sequencing Analysis

Next Generation Sequencing technology, on the one hand, allows a more accurate analysis, and, on the other hand, increases the amount of data to process. A new protocol for sequencing the messenger RNA in a cell, known as RNA- Seq, generates millions of short sequence fragments in a single run. These fragments, or reads, can be used to measure levels of gene expression and to identify novel splice variants of genes. The proposed solution is a distributed architecture consisting of a Grid Environment and a Virtual Grid Environment, in order to reduce processing time by making the system scalable and flexibl

A Cloud Infrastructure for Optimization of a Massive Parallel Sequencing Workflow

Massive Parallel Sequencing is a term used to describe several revolutionary approaches to DNA sequencing, the so-called Next Generation Sequencing technologies. These technologies generate millions of short sequence fragments in a single run and can be used to measure levels of gene expression and to identify novel splice variants of genes allowing more accurate analysis. The proposed solution provides novelty on two fields, firstly an optimization of the read mapping algorithm has been designed, in order to parallelize processes, secondly an implementation of an architecture that consists of a Grid platform, composed of physical nodes, a Virtual platform, composed of virtual nodes set up on demand, and a scheduler that allows to integrate the two platform

Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics

The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al. (2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications