Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture

Project Controls and Management Systems : current practice and how it has changed over the past decade

Project Controls and Management System (PCMS) refers to an ecosystem of processes, tools and personnel required for the proper planning and execution of capital projects throughout the different phases of design, procurement, construction and startup. This can be divided into different focus areas (functions) that would include Estimating, Planning, Scheduling, Cost Control, Change Management, Progressing, and Forecasting. Various trends such as globalization, contractor specialization and information technology developments have impacted the way PCMS are implemented and made it the subject of extensive research over the past years to investigate how to best utilize those trends. Replicating the research methodology used in a 2011 report published by the Construction Research Institute (CII), this work aims to investigate the current status of PCMS implementation and how it has changed over the past decade. It was concluded that while the original PCMS principles are still valid, adoption has drastically changed in terms of efficiency for the majority of the functions. The research also identifies areas of potential concerns and provides recommendations for further improvement.Civil, Architectural, and Environmental Engineerin

“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

Records management capacity and compliance toolkits : a critical assessment.

This article seeks to present the results of a project that critically evaluated a series of toolkits for assessing records management capacity and/or compliance. These toolkits have been developed in different countries and sectors within the context of the e-environment and provide evidence of good corporate and information governance. Design/methodology/approach - A desk-based investigation of the tools was followed by an electronic Delphi with toolkit developers and performance measurement experts to develop a set of evaluation criteria. Different stakeholders then evaluated the toolkits against the criteria using cognitive walkthroughs and expert heuristic reviews. The results and the research process were reviewed via electronic discussion. Findings - Developed by recognised and highly respected organisations, three of the toolkits are software tools, whilst the fourth is a methodology. They are all underpinned by relevant national/international records management legislation, standards and good practice including, either implicitly or explicitly, ISO 15489. They all have strengths, complementing rather than competing with one another. They enable the involvement of other staff, thereby providing an opportunity for raising awareness of the importance of effective records management. Practical implications - These toolkits are potentially very powerful, flexible and of real value to organisations in managing their records. They can be used for a "quick and dirty" assessment of records management capacity or compliance as well as in-depth analysis. The most important criterion for selecting the appropriate one is to match the toolkit with the scenario. Originality/value - This paper aims to raise awareness of the range and nature of records management toolkits and their potential for varied use in practice to support more effective management of records