Interactive real-time visualisation of large data sets plays an important role in scientific research. It is even more relevant for astronomy where new cutting edge large telescopes will generate tens of petabytes sky surveys. We describe our solution, developed in context of the Euclid space mission whose large astronomical imaging data will be distributed over several heterogeneous Science Data Centres (SDCs) across the world. In our visualisation architecture for distributed data, millions of survey images (HiPS) distributed over SDCs are efficiently transported and combined to deliver image(s) of interest at the desired resolution (up to pixel level) to the user. This is achieved by optimally utilising a combination of several modern tools consisting of http servers, a Front-End Node and load-balancer (FEN), reverse proxies, PHP/Python scripts, MySQL databases, including on the fly image generation/combination which all feed (only) the required information to the Aladin interactive visualisation tool at the remote user's Personal Computer (PC). It has potential applications for large projects (e.g., Square Kilometre Array) having data distributed across several locations

Belikov, A. N.

Buddelmeijer, H.

Roerdink, J. B. T. M.

Singh, S.

Valentijn, E. A.

English

ARTS repository - University of Groningen

   University of GroningenScientific Visualisation of Extremely Large Distributed Astronomical SurveysSingh, S.; Valentijn, E. A.; Belikov, A. N.; Buddelmeijer, H.; Roerdink, J. B. T. M.Published in:Astronomical Data Analysis Software and Systems XXIXIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Singh, S., Valentijn, E. A., Belikov, A. N., Buddelmeijer, H., & Roerdink, J. B. T. M. (2020). ScientificVisualisation of Extremely Large Distributed Astronomical Surveys. In R. Pizzo, E. R. Deul, & J. D. Mol(Eds.), Astronomical Data Analysis Software and Systems XXIX: Proceedings of a Conference Held atMartiniPlaza, Groningen, the Netherlands, 6-10 October 2019 (Vol. 527, pp. 201-204). ( ASP ConferenceSeries; Vol. 527). Astronomical Society of the Pacific. http://adsabs.harvard.edu/abs/2020ASPC..527..201SCopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 01-11-2023Astronomical Data Analysis Software and Systems XXIXASP Conference Series, Vol. 527R. Pizzo, E.R. Deul, J-D. Mol, J. de Plaa and H. Verkouter, eds.c©2020 Astronomical Society of the PacificScientific Visualisation of Extremely Large DistributedAstronomical SurveysS. Singh,1 E. A. Valentijn,2 A. N. Belikov,2 H. Buddelmeijer2 andJ. B. T. M. Roerdink11Bernoulli Institute for Mathematics, Computer Science and ArtificialIntelligence, University of Groningen, the Netherlands; rnsweta@gmail.com2Kapteyn Astronomical Institute, University of Groningen, the Netherlands;Abstract. Interactive real-time visualisation of large data sets plays an important rolein scientific research. It is even more relevant for astronomy where new cutting edgelarge telescopes will generate tens of petabytes sky surveys. We describe our solution,developed in context of the Euclid space mission whose large astronomical imagingdata will be distributed over several heterogeneous Science Data Centres (SDCs) acrossthe world. In our visualisation architecture for distributed data, millions of survey im-ages (HiPS) distributed over SDCs are efficiently transported and combined to deliverimage(s) of interest at the desired resolution (up to pixel level) to the user. This isachieved by optimally utilising a combination of several modern tools consisting ofhttp servers, a Front-End Node and load-balancer (FEN), reverse proxies, PHP/Pythonscripts, MySQL databases, including on the fly image generation/combination whichall feed (only) the required information to the Aladin interactive visualisation toolat the remote user’s Personal Computer (PC). It has potential applications for largeprojects (e.g., Square Kilometre Array) having data distributed across several locations.1. IntroductionEuclid (http://sci.esa.int/euclid) is a space mission led by Euclid Consortiumand the European Space Agency with the main scientific objective of carrying out vis-ible and near infrared surveys of the entire extra-galactic sky to investigate the originof the Universe’s accelerating expansion and the nature of dark energy, dark matter andgravity. Due to its unprecedented accuracy similar to that of the Hubble space tele-scope, Euclid data will be useful for most astronomical studies. The Euclid consortium(https://www.euclid-ec.org) has organised the scientific and technical commu-nity into a number of working groups for efficient workload distribution. The resultingdata volume comprising several tens of PB (Dubath et al. 2017) will be distributed overseveral SDCs (in Europe, USA) responsible for data storage and processing.One of the main outputs at each of these SDCs will be high-quality astronomicalsurvey images (including from related ground based telescopes and simulations) andsource catalogues. The traditional approach involving local copies at user PCs or largecompute clusters, or even at one physical location may be difficult due to large datavolumes and costs involved. Thus distributed, real-time interactive visualisation of thesurvey is critical to gain needed insight to maximise scientific astronomical research.201202 Singh et al.2. Distributed Visualisation PipelineThe two ends of any visualisation pipeline framework are (i) The Demand (User) Side :– the visualisation software at the users end (client) and (ii) The Supply Side : – a co-hesive data storage, management and transport framework able to provide the requireddata/information in real time in an efficient and transparent manner.For Euclid, Aladin sky atlas (https://aladin.u-strasbg.fr) will be the mainvisualisation analysis tool on the user (client) side (e.g. an astronomer). The defaultformat for Euclid surveys will be based on Hierarchical Progressive Surveys (HiPS).HiPS makes use of the hierarchical features of HEALPix (Górski et al. 2005) to organiseastronomical data into HEALPix maps of different resolutions (Fernique et al. 2015).We followed a two-pronged approach, firstly to develop a visualisation frameworkfor distributed data to address the challenges at the supply side of the pipeline. Secondlywe worked on ways to minimise the size of survey data itself. In this paper we presentonly the work related to the visualisation framework for distributed data.3. Architectural DesignThe http compliant nature of HiPS (http://www.ivoa.net/documents/HiPS) al-lows it to be accessed via any http compliant server. We make use of this useful featureto arrive at a client-server based architectural design which binds the distributed re-sources across the SDCs into the visualisation framework. This flexible architecturealso empowers us to utilise the http protocols and associated tools. As an example,while the HiPS does prescribe the storage of the HiPS image (survey) in a simple hi-erarchy of directory and files, the http compliance allows the actual storage behind thehttp server to be implemented in any desired manner, provided the critical requirementof supplying the requested information to the client (e.g., Aladin) is correctly met.Our framework is based on the generic software LAMP stack model founded onfour open source software components, namely Linux, Apache, MySQL (and alter-natives) and PHP (and alternatives). Figure 1(a) shows the three main constituents inthe framework, namely : (i) Astronomers using Aladin at their PCs to remotely visu-alise Euclid surveys. (ii) A FEN acts as the main interface for Aladin requests andhosts the mandatory and recommended (optional) files necessary to initiate survey vi-sualisation (e.g., properties, index.html, moc.fits etc.). It has a Linux operating sys-tem (e.g., Ubuntu) with a listening Apache http server, a MySQL server/database andPHP/Python scripts. The database contains the necessary entries to locate each surveyfile constituting the entire survey (across all SDCs). The PHP/Python scripts dynami-cally respond with appropriate action to the incoming http requests. (iii) The SDCs alsohave a listening Apache http server and collectively host the actual survey images.The visualisation framework can be set up in two modes namely (i) A fully dis-tributed visualisation mode and (ii) A reverse proxy mode. Figure 1(b) shows a sim-plified flowchart for the fully distributed mode setup. An astronomer initiates a surveyvisualisation using Aladin from the PC. Aladin makes a http request for the desiredinformation to the http server on the FEN. There could be (mainly) three possible sce-narios : (a) The request is for one of the basic mandatory (or recommended) files (hostedat FEN) (b) The requested file is unique and present at one or more SDCs and (c) Therequested file is non-unique and present at one or several SDCs. Here the term ‘unique’refers to files which have only identical copies at one or more SDCs. The term ‘non-Distributed Visualisation of Big Data 203Figure 1. (a) An overall schematic of visualisation framework for distributed data.Using Aladin, astronomers visualise their regions of interest (Euclid surveys) at theirindividual PCs. The FEN is the main interface with overall control. (b) A simplifiedflowchart depicting how Aladin’s http requests are handled in the fully distributedvisualisation mode setup (c) An example of two same tile images but with differentpartial sky coverage which are dynamically combined during visualisation.204 Singh et al.unique’ refers to a file present at multiple SDCs some of which contain non-identicalinformation (e.g., the same tile image but with different partial coverage).In case (a) the http server at the FEN responds with the requested file. In case (b)it responds with a redirect message to Aladin with the details of the most appropriateSDC to which the request must be re-addressed. Aladin re-requests to the appropriatehttp server which responds with the desired file. In case (c) a PHP script is instantiatedwhich requests and receives all the non-unique instances of the requested file from theSDCs, combines them locally and responds with the resulting combined file to Aladinas shown in Figure 1(c). The fully distributed mode setup is network efficient as mostof the data transfer to the clients happens directly from the SDCs hosting it. The reverseproxy mode setup is convenient for overall monitoring and system security as clientscommunicate only with the FEN and the SDCs are shielded from direct access.4. Conclusions and Future workWe have successfully developed a distributed visualisation framework with the aim ofenabling users to visualise all sky HiPS surveys as if they were stored locally on theirindividual PCs. It places no constraints on the inherent capabilities of Aladin and onthe SDCs including their heterogeneity in terms of their size, system design, operatingsystem, file system and intra-SDC bandwidth. An user on his PC screen can visualiseeither a small sky area at high angular resolution or a large sky area at low angularresolution given the limited number of screen pixels. Thus the visualisation frame-work has to transfer only a limited amount of data which can be visualised at a giventime. Every http request from all the users is handled simultaneously (in parallel), andindependently (including the data transfer) due to stateless nature of the http protocol.Our framework can be used for visualisation of distributed data in general andwill be also applied for Euclid mission. The present work has been carried out usingVirtual Machines (https://www.oracle.com/virtualization) as SDCs and thePC as the FEN. We are now implementing the visualisation framework on OmegaCENinfrastructure (http://www.astro.rug.nl/~omegacen) for the 4th data release ofKilo-Degree Survey (Kuijken et al. 2019). The documentation including the installationinstructions and software code will be available for open access.We expect to make further improvements by exploring options which include butare not limited to: NoSQL distributed databases like Apache Cassandra, NGINX httpserver on SDCs, key-value stores, replacing PHP scripts by daemon processes, caching,ELK stack (https://www.elastic.co) with Kibana as Web-UI for integrated perfor-mance monitoring. The International Virtual Observatory Alliance will play a criticalrole for future big international projects where single entities cannot efficiently tacklethe big data volumes. In that context, we expect our work to play a useful role.ReferencesDubath, P., Apostolakos, N., Bonchi, A., Belikov, A., et al. 2017, in Astroinformatics, editedby M. Brescia, S. G. Djorgovski, E. D. Feigelson, G. Longo, & S. Cavuoti, vol. 325 ofIAU Symposium, 73. 1701.08158Fernique, P., Allen, M. G., et al. 2015, A&A, 578, A114. 1505.02291Górski, K. M., Hivon, E., et al. 2005, ApJ, 622, 759. astro-ph/0409513Kuijken, K., Heymans, C., et al. 2019, A&A, 625, A2. 1902.11265

Scientific Visualisation of Extremely Large Distributed Astronomical Surveys

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Scientific Visualisation of Extremely Large Distributed Astronomical Surveys

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