Current Issue : January - March Volume : 2014 Issue Number : 1 Articles : 5 Articles
The Architecture, Engineering & Construction (AEC) sector is a highly fragmented, data intensive, project based\r\nindustry, involving a number of very different professions and organisations. Projects carried out within this sector\r\ninvolve collaboration between various people, using a variety of different systems. This, along with the industry�s\r\nstrong data sharing and processing requirements, means that the management of building data is complex and\r\nchallenging. This paper presents a solution to data sharing requirements of the AEC sector by utilising Cloud\r\nComputing. Our solution presents two key contributions, first a governance model for building data, based on\r\nextensive research and industry consultation. Second, a prototype implementation of this governance model, utilising\r\nthe CometCloud autonomic Cloud Computing engine based on the Master/Worker paradigm. We have integrated\r\nour prototype with the 3D modelling software Google Sketchup. The approach and prototype presented has\r\napplicability in a number of other eScience related applications involving multi-disciplinary, collaborative working\r\nusing Cloud Computing infrastructure....
Cloud computing services rely on electricity to power compute-servers, network\r\nequipment, cooling systems, and other supporting infrastructure. As such, energy\r\ncosts are a substantial outgoing to public providers of cloud computing services.\r\nOn-demand pricing, where consumers are not required to give advance notice of\r\nrequirements, does not aid the provider in planning future demand, and therefore\r\nmakes it more difficult to purchase energy at discounted rates. In this paper, we\r\npropose an advance pricing mechanism for cloud computing resources based on\r\nprovision-point contracts, commonly used by deal-of-the-day websites such as\r\nGroupon. We show how our Contributory Provision Point (CPP) contracts reward\r\nconsumers with reduced prices for advance reservations, while allowing providers to\r\nmake accurate forecasts of energy usage. We show how CPP contracts are risk-free\r\nfor the provider, guaranteeing to be at least as profitable as on-demand mechanisms\r\nwhere electricity is purchased ad-hoc by the provider. Through a computer\r\nsimulation, we demonstrate that CPP contracts can be more profitable for the\r\nprovider compared to a traditional method of hedging electricity futures using a\r\npopular forecasting algorithm. Furthermore, we show that CPP contracts encourage\r\nconsumers to forecast honestly by rewarding them with discounted rates, while\r\nremaining profitable for the provider, even when forecasts are not completely\r\naccurate....
Urban flood risk modelling is a highly topical example of intensive computational processing. Such processing is\r\nincreasingly required by a range of organisations including local government, engineering consultancies and the\r\ninsurance industry to fulfil statutory requirements and provide professional services. As the demands for this type of\r\nwork become more common, then ownership of high-end computational resources is warranted but if use is more\r\nsporadic and with tight deadlines then the use of Cloud computing could provide a cost-effective alternative.\r\nHowever, uptake of the Cloud by such organisations is often thwarted by the perceived technical barriers to entry. In\r\nthis paper we present an architecture that helps to simplify the process of performing parameter sweep work on an\r\nInfrastructure as a Service Cloud. A parameter sweep version of the urban flood modelling, analysis and visualisation\r\nsoftware ââ?¬Å?CityCatââ?¬Â was developed and deployed to estimate spatial and temporal flood risk at a whole city scale ââ?¬â?? far\r\nlarger than had previously been possible. Performing this work on the Cloud allowed us access to more computing\r\npower than we would have been able to purchase locally for such a short time-frame (~21 months of processing in a\r\nsingle calendar month). We go further to illustrate the considerations, both functional and non-functional, which need\r\nto be addressed if such an endeavour is to be successfully achieved....
The present research investigated the different characteristics of data provenance. Provenance means “the origin” or “lineage”. It provides authenticity to the user. Secure provenance that records ownership and process history of data objects is essential to the success of data. In this paper we create taxonomy of data provenance techniques, and apply the classification to current research efforts in the field. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. Our synthesis can help those building scientific and business metadata-management systems to understand existing provenance system designs. The survey culminates with an identification of open research problems in the field....
Trust is a critical factor in cloud computing; in present practice it depends largely on perception of reputation, and self\r\nassessment by providers of cloud services. We begin this paper with a survey of existing mechanisms for establishing\r\ntrust, and comment on their limitations. We then address those limitations by proposing more rigorous mechanisms\r\nbased on evidence, attribute certification, and validation, and conclude by suggesting a framework for integrating\r\nvarious trust mechanisms together to reveal chains of trust in the cloud....
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