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A HPC based cloud model for real-time energy optimisation

Petri, Ioan ORCID:, Li, Haijiang ORCID:, Rezgui, Yacine ORCID:, Yang, Chunfeng, Yuce, Baris ORCID: and Jayan, Bejay 2016. A HPC based cloud model for real-time energy optimisation. Enterprise Information Systems 10 (1) , pp. 108-128. 10.1080/17517575.2014.919053

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Recent research has emphasised that an increasing number of enterprises need computation environments for executing HPC (High Performance Computing) applications. Rather than paying the cost of ownership and possess physical, fixed capacity clusters, enterprises can reserve or rent resources for undertaking the required tasks. With the emergence of new computation paradigms such as cloud computing it has become possible to solve a wider range of problems due to their capability to handle and process massive amounts of data. On the other hand, given the pressing regulatory requirement to reduce the carbon footprint of our built environment, significant researching efforts have been recently directed towards simulation-based building energy optimisation with the overall objective of reducing energy consumption. Energy optimisation in buildings represents a class of problems that requires significant computation resources and generally is a time consuming process especially when undertaken with building simulation software, such as EnergyPlus. In this paper we present how a HPC based cloud model can be efficiently used for running and deploying EnergyPlus simulation-based optimisation in order to fulfil a number of objectives related to energy consumption. We describe and evaluate the establishment of such an application-based environment, and consider a cost perspective to determine the efficiency over several cases we explore. This study identifies the following contributions: (i) a comprehensive examination of issues relevant to the HPC community, including performance, cost, user perspectives and range of user activities, (ii) a comparison of two different execution environments such as HTCondor and CometCloud and determine their effectiveness in supporting simulation-based optimisation and (iii) a detailed performance analysis to locate the limiting factors of these execution environments.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Uncontrolled Keywords: energy optimisation, high performance computing, cloud computing, CometCloud, HTCondor, EnergyPlus
Publisher: Taylor & Francis: STM, Behavioural Science and Public Health Titles
ISSN: 1751-7575
Funders: EU FP7
Date of Acceptance: 24 April 2014
Last Modified: 13 Dec 2022 09:36

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