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Title Scalable High Demand Analytics Environments with Heterogeneous Clouds
  • K. Woods, R.J. Clegg, R. Millward
    Tessella, Abingdon, United Kingdom
  • F. Barnsely, C. Jones
    STFC/RAL, Chilton, Didcot, Oxon, United Kingdom
Abstract The Ada Lovelace Centre (ALC) at STFC provides on-demand, data analysis, interpretation and analytics services to scientists using UK research facilities. ALC and Tessella have built software systems to scale analysis environments to handle peaks and troughs in demand as well as to reduce latency by provision environments closer to scientists around the world. The systems can automatically provision infrastructure and supporting systems within compute resources around the world and in different cloud types (including commercial providers). The system then uses analytics to dynamically provision and configure virtual machines in various locations ahead of demand so that users experience as little delay as possible. In this poster, we report on the architecture and complex software engineering used to automatically scale analysis environments to heterogeneous clouds, make them secure and easy to use. We then discuss how analytics was used to create intelligent systems in order to allow a relatively small team to focus on innovation rather than operations.
Funding UK Research and Innovation - Science & Technology Facilities Council (UK SBS IT18160)
Paper download MOMPR007.PDF [0.331 MB / 4 pages]
Poster download MOMPR007_POSTER.PDF [1.650 MB]
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Conference ICALEPCS2019
Series International Conference on Accelerator and Large Experimental Physics Control Systems (17th)
Location New York, NY, USA
Date 05-11 October 2019
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Karen S. White (ORNL, Oak Ridge, TN, USA); Kevin A. Brown (BNL, Upton, NY, USA); Philip S. Dyer (BNL, Upton, NY, USA); Volker RW Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-209-7
Online ISSN 2226-0358
Received 30 September 2019
Accepted 09 October 2019
Issue Date 30 August 2020
DOI doi:10.18429/JACoW-ICALEPCS2019-MOMPR007
Pages 171-174
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.