Advanced security infrastructures for grid education

This paper describes the research conducted into advanced authorization infrastructures at the National e-Science Centre (NeSC) at the University of Glasgow and their application to support a teaching environment as part of the Dynamic Virtual Organisations in e-Science Education (DyVOSE) project. We outline the lessons learnt in teaching Grid computing and rolling out the associated security authorisation infrastructures, and describe our plans for a future, extended security infrastructure for dynamic establishment of inter-institutional virtual organisations (VO) in the education domain

Advanced security infrastructures for grid education

This paper describes the research conducted into advanced authorization infrastructures at the National e-Science Centre (NeSC) at the University of Glasgow and their application to support a teaching environment as part of the Dynamic Virtual Organisations in e-Science Education (DyVOSE) project. We outline the lessons learnt in teaching Grid computing and rolling out the associated security authorisation infrastructures, and describe our plans for a future, extended security infrastructure for dynamic establishment of inter-institutional virtual organisations (VO) in the education domain

Advanced Security Infrastructures for Grid Education

This paper describes the research conducted into advanced authorization infrastructures at the National e-Science Centre (NeSC) at the University of Glasgow and their application to support a teaching environment as part of the Dynamic Virtual Organisations in e-Science Education (DyVOSE) project. We outline the lessons learnt in teaching Grid computing and rolling out the associated security authorisation infrastructures, and describe our plans for a future, extended security infrastructure for dynamic establishment of inter-institutional virtual organisations (VO) in the education domain

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

Supporting the clinical trial recruitment process through the grid

Patient recruitment for clinical trials and studies is a large-scale task. To test a given drug for example, it is desirable that as large a pool of suitable candidates is used as possible to support reliable assessment of often moderate effects of the drugs. To make such a recruitment campaign successful, it is necessary to efficiently target the petitioning of these potential subjects. Because of the necessarily large numbers involved in such campaigns, this is a problem that naturally lends itself to the paradigm of Grid technology. However the accumulation and linkage of data sets across clinical domain boundaries poses challenges due to the sensitivity of the data involved that are atypical of other Grid domains. This includes handling the privacy and integrity of data, and importantly the process by which data can be collected and used, and ensuring for example that patient involvement and consent is dealt with appropriately throughout the clinical trials process. This paper describes a Grid infrastructure developed as part of the MRC funded VOTES project (Virtual Organisations for Trials and Epidemiological Studies) at the National e-Science Centre in Glasgow that supports these processes and the different security requirements specific to this domain

Experiences in teaching grid computing to advanced level students

The development of teaching materials for future software engineers is critical to the long term success of the grid. At present however there is considerable turmoil in the grid community both within the standards and the technology base underpinning these standards. In this context, it is especially challenging to develop teaching materials that have some sort of lifetime beyond the next wave of grid middleware and standards. In addition, the current way in which grid security is supported and delivered has two key problems. Firstly in the case of the UK e-Science community, scalability issues arise from a central certificate authority. Secondly, the current security mechanisms used by the grid community are not line grained enough. In this paper we outline how these issues are being addressed through the development of a grid computing module supported by an advanced authorisation infrastructure at the University of Glasgow

Performance metrics and auditing framework using application kernels for high‐performance computer systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97468/1/cpe2871.pd

Utility computing: Certification model, costing model, and related architecture development

The purpose of the thesis was to propose one set of solutions to some of the challenges that are delaying the adoption of utility computing on a wider scale. These components enable effective deployment of utility computing, efficient look-up, and comparison of service offerings of different utility computing resource centers connected to the utility computing network