Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study

Cloud computing has emerged as a popular paradigm and an attractive model for providing a reliable distributed computing model.it is increasing attracting huge attention both in academic research and industrial initiatives. Cloud deployments are paramount for institution and organizations of all scales. The availability of a flexible, free open source cloud platform designed with no propriety software and the ability of its integration with legacy systems and third-party applications are fundamental. Open stack is a free and opensource software released under the terms of Apache license with a fragmented and distributed architecture making it highly flexible. This project was initiated and aimed at designing a secured cloud infrastructure called BradStack, which is built on OpenStack in the Computing Laboratory at the University of Bradford. In this report, we present and discuss the steps required in deploying a secured BradStack Multi-node cloud infrastructure and conducting Penetration testing on OpenStack Services to validate the effectiveness of the security controls on the BradStack platform. This report serves as a practical guideline, focusing on security and practical infrastructure related issues. It also serves as a reference for institutions looking at the possibilities of implementing a secured cloud solution.Comment: 38 pages, 19 figures

Does "thin client" mean "energy efficient"?

The thick client –a personal computer with integral disk storage and local processing capability, which also has access to data and other resources via a network connection – is accepted as the model for providing computing resource in most office environments. The Further and Higher Education sector is no exception to that, and therefore most academic and administrative offices are equipped with desktop computers of this form to support users in their day to day tasks. This system structure has a number of advantages: there is a reduced reliance on network resources; users access a system appropriate to their needs, and may customise “their” system to meet their own personal requirements and working patterns. However it also has disadvantages: some are outside the scope of this project, but of most relevance to the green IT agenda is the fact that relatively complex and expensive (in first cost and in running cost) desktop systems and servers are underutilised – especially in respect of processing power. While some savings are achieved through use of “sleep” modes and similar power reducing mechanisms, in most configurations only a small portion of the overall total available processor resource is utilised. This realisation has led to the promotion of an alternative paradigm, the thin client. In a thin client system, the desktop is shorn of most of its local processing and data storage capability, and essentially acts as a terminal to the server, which now takes on responsibility for data storage and processing. The energy benefit is derived through resource sharing: the processor of the server does the work, and because that processor is shared by all users, a number of users are supported by a single system. Therefore – according to proponents of thin client – the total energy required to support a user group is reduced, since a shared physical resource is used more efficiently. These claims are widely reported: indeed there are a number of estimation tools which show these savings can be achieved; however there appears to be little or no actual measured data to confirm this. The community does not appear to have access to measured data comparing thin and thick client systems in operation in the same situation, allowing direct comparisons to be drawn. This is the main goal of this project. One specific question relates to the overall power use, while it would seem to be obvious that the thin client would require less electricity, what of the server? Two other variations are also considered: it is not uncommon for thin client deployments to continue to use their existing PCs as thin client workstations, with or without modification. Also, attempts by PC makers to reduce the power requirements of their products have given rise to a further variation: the incorporation of low power features in otherwise standard PC technology, working as thick clients. This project was devised to conduct actual measurements in use in a typical university environment. We identified a test area: a mixed administrative and academic office location which supported a range of users, and we made a direct replacement of the current thick client systems with thin client equivalents; in addition, we exchanged a number of PCs operating in thin and thick client mode with devices specifically branded as “low power” PCs and measured their power requirements in both thin and thick modes. We measured the energy consumption at each desktop for the duration of our experiments, and also measured the energy draw of the server designated to supporting the thin client setup, giving us the opportunity to determine the power per user of each technology. Our results show a significant difference in power use between the various candidate technologies, and that a configuration of low power PC in thick client mode returned the lowest power use during our study. We were also aware of other factors surrounding a change such as this: we have addressed the technical issues of implementation and management, and the non-technical or human factors of acceptance and use: all are reported within this document. Finally, our project is necessarily limited to a set of experiments carried out in a particular situation, therefore we use estimation methods to draw wider conclusions and make general observations which should allow others to select appropriate thick or thin client solutions in their situation

Great Plains Network - Kansas State University Agronomy Application Deep Dive

In May 2019, staff members from the Engagement and Performance Operations Center (EPOC) and members of the Great Plains Network (GPN) attending their Annual Meeting meeting met with researchers at Kansas State University (KSU) for the purpose of an Application Deep Dive training session. The goal of this training session was to help characterize the requirements for an agronomy application, to enable cyberinfrastructure support staff to better understand the needs of the researchers they support, and to offer training to GPN members to be able to conduct these on their own. Material for this event includes both the written documentation from the agronomy application at KSU, details about the infrastructure setup at KSU, and also a writeup of the discussion that took place in person on May 20, 2019. EPOC, GPN, and KSU recorded a set of action items for this use case, continuing the ongoing support and collaboration. These are a reflection of the case study report, and in person discussion

Modeling the Response of Black Walnut -dominant Mixed Hardwoods to Seasonal Climate Effects with UAV-based Hyperspectral Sensor and Aerial Photogrammetry

The development of compact sensors in recent years has inspired the use of UAS-based hyperspectral and aerial imaging techniques for small-scale remote sensing applications. With increasing concerns about climate change, spectrally-derived vegetation indices (VIs) have proven useful for quantifying stress-induced vegetation response. The goal of this study was to develop predictive models and assess methodology for modeling the biological response of a black walnut -dominant mixed hardwood stand to seasonal climate events using UAV-based hyperspectral remote-sensing. The derived VIs were evaluated against the means of four seasonal measures of climate calculated for a two-week period prior to the flight date. A best subsets regression was used to create best fitting linear regression models according to Bayesian Information Criterion (BIC). The highest-ranked model for total precipitation had an AdjR² of 0.0839 and RMSE of 0.0827 inches. The highest-ranked model for maximum air temperature had an AdjR² of 0.9922 and RMSE of 0.5485 °F. The highest-ranked model for average air temperature had an AdjR² of 0.9987 and RMSE of 0.2256 °F. The highest-ranked model for total solar radiation had an AdjR² of 0.9961 and RMSE of 0.06405 MJ/M². The results indicate that select VIs measured at the canopy level may be useful in estimating the response to at least some measures seasonal climate. The proposed regression models could help local researchers and landowners in making short-term management decisions, as well as further our understanding of climate-induced tree stress for maintaining sustainable forests in Missouri