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@InProceedings{woods:icalepcs2019-mompr007,
author = {K. Woods and F. Barnsely and R.J. Clegg and C. Jones and R. Millward},
title = {{Scalable High Demand Analytics Environments with Heterogeneous Clouds}},
booktitle = {Proc. ICALEPCS'19},
pages = {171--174},
paper = {MOMPR007},
language = {english},
keywords = {data-analysis, experiment, scattering, operation, software},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-MOMPR007},
url = {https://jacow.org/icalepcs2019/papers/mompr007.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-MOMPR007},
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.},
}