Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches

Bellavita, Julian; Sim, Alex; Wu, Kesheng; Monga, Inder; Guok, Chin; Würthwein, F.; Davila, Diego

doi:10.1145/3526064.3534111

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Although this caching system is common enough in HEP computing environments, not often can it be found used directly by the analysis framework, whereas usually it is activated at the level of the grid site. Many efforts from literature are focused on analysing access patterns of certain datasets and evaluating different strategies to improve network and I/O usage [141,142,143].…”

Section: State Of the Artmentioning

confidence: 99%

Distributed Computing Solutions for High Energy Physics Interactive Data Analysis

Padulano¹

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The scientific research in High Energy Physics (HEP) is characterised by complex computational challenges, which over the decades had to be addressed by researching computing techniques in parallel to the advances in understanding physics. One of the main actors in the field, CERN, hosts both the Large Hadron Collider (LHC) and thousands of researchers yearly who are devoted to collecting and processing the huge amounts of data generated by the particle accelerator. This has historically provided a fertile ground for distributed computing techniques, which led to the creation of the Worldwide LHC Computing Grid (WLCG), a global network providing large computing power for all the experiments revolving around the LHC and the HEP field. Data generated by the LHC so far has already posed challenges for computing and storage. vii de recerca sobre aquesta proposta. Des del punt de vista de l'usuari, això es detalla en forma duna nova interfície que es pot executar en un ordinador portàtil o en milers de nodes informàtics, sense canvis en l'aplicació de l'usuari. Aquest desenvolupament obre la porta a l'explotació de recursos distribuïts a través de motors d'execució estàndard de la indústria que poden escalar a múltiples nodes en clústers HPC o HTC, o fins i tot en ofertes serverless de núvols comercials. Atès que sovint l'anàlisi de dades en aquest camp està limitada per E/S, cal comprendre quins són els possibles mecanismes d'emmagatzematge en memòria cau. En aquest sentit, es va investigar un nou sistema d'emmagatzematge basat en la tecnologia d'emmagatzematge d'objectes com a objectiu per a la memòria cau.En conclusió, el futur de l'anàlisi de dades a HEP presenta reptes des de diverses perspectives, des de l'explotació de recursos informàtics i d'emmagatzematge distribuïts fins al disseny d'interfícies d'usuari ergonòmiques. Els marcs de programari han d'apuntar a l'eficiència i la facilitat d'ús, desvinculant la definició dels càlculs físics dels detalls d'implementació de la seva execució. Aquesta tesi s'emmarca en l'esforç col•lectiu de la comunitat HEP cap a aquests objectius, definint problemes i possibles solucions que poden ser adoptades per futurs investigadors. viii

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Section: State Of the Artmentioning

confidence: 99%

Distributed Computing Solutions for High Energy Physics Interactive Data Analysis

Padulano¹

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Optimization of ecological sports innovation teaching path based on multiple intelligence evaluation model

Hao,

Li,

Wang

2023

Applied Mathematics and Nonlinear Sciences

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Rural sports tourism is an emerging industry that integrates rural tourism and sports tourism and plays an important role in broadening the field of sports and cultural consumption and driving rural economic development. Under the background of big data, how to better integrate rural sports culture and sports tourism becomes an important issue to improve the quality of rural tourism. This paper analyzes the big data of rural sports tourism on the Internet review platform based on big data processing technology, combined with the investigation and research on the current situation of rural sports tourism resources in a town. The big data analysis shows that rural sports tourists are only 7% more female than male, and the overall gender ratio is relatively balanced. In terms of age composition, the largest number of tourists were between 36 and 45 years old, accounting for 42.6%. In addition, rural sports tourists aged 26 to 60 accounted for 82.6% of all, indicating that rural sports tourism is dominated by middle-aged and elderly people. In terms of travel time, the largest number of rural sports tourists chose 2 to 3 days, accounting for 33.8%, and only 2.3% traveled for more than 7 days. In the choice of travel mode, 37.6% of tourists are organized by travel agencies, accounting for the largest number of travelers. From the big data emotional analysis of the evaluation words of sports tourists, the most frequent occurrence is characteristic, reaching 435 times, and the least frequent is danger, but also 89 times. Meanwhile, convenience and health also appear more frequently. Comprehensive analysis of the above, this paper believes that rural sports tourism should promote more local characteristics of sports culture and highlight differentiation while strengthening personal safety protection measures for tourists.

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Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches

Cited by 2 publications

References 12 publications

Distributed Computing Solutions for High Energy Physics Interactive Data Analysis

Distributed Computing Solutions for High Energy Physics Interactive Data Analysis

Optimization of ecological sports innovation teaching path based on multiple intelligence evaluation model

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