High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Scenarios for the development of smart grids in the UK: synthesis report

‘Smart grid’ is a catch-all term for the smart options that could transform the ways society produces, delivers and consumes energy, and potentially the way we conceive of these services. Delivering energy more intelligently will be fundamental to decarbonising the UK electricity system at least possible cost, while maintaining security and reliability of supply. Smarter energy delivery is expected to allow the integration of more low carbon technologies and to be much more cost effective than traditional methods, as well as contributing to economic growth by opening up new business and innovation opportunities. Innovating new options for energy system management could lead to cost savings of up to £10bn, even if low carbon technologies do not emerge. This saving will be much higher if UK renewable energy targets are achieved. Building on extensive expert feedback and input, this report describes four smart grid scenarios which consider how the UK’s electricity system might develop to 2050. The scenarios outline how political decisions, as well as those made in regulation, finance, technology, consumer and social behaviour, market design or response, might affect the decisions of other actors and limit or allow the availability of future options. The project aims to explore the degree of uncertainty around the current direction of the electricity system and the complex interactions of a whole host of factors that may lead to any one of a wide range of outcomes. Our addition to this discussion will help decision makers to understand the implications of possible actions and better plan for the future, whilst recognising that it may take any one of a number of forms

Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments

Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process