Moving the California distributed CMS XCache from bare metal into containers using Kubernetes

Fajardo, Edgar; Tadel, M.; Balcas, Justas; Tadel, Alja Mrak; Wuerthwein, Frank; Davila, Diego; Guiang, J.; Sfiligoi, I.

doi:10.1051/epjconf/202024504042

Cited by 6 publications

(5 citation statements)

References 8 publications

(10 reference statements)

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“…By 2028, the community expects the data volume to increase by thirty fold [1]. We observe that a significant portion of the popular datasets are shared among users in the same geographical region [1], which suggests that regional data storage caches could reduce data access latency by holding popular datasets closer to user analyses [2][3][4][5][6][7][8][9]. In-network cache or regional data caching mechanism [6][7][8][9] has been deployed in Southern California for the US CMS, one of the LHC experiment.…”

Section: Introductionmentioning

confidence: 93%

“…We observe that a significant portion of the popular datasets are shared among users in the same geographical region [1], which suggests that regional data storage caches could reduce data access latency by holding popular datasets closer to user analyses [2][3][4][5][6][7][8][9]. In-network cache or regional data caching mechanism [6][7][8][9] has been deployed in Southern California for the US CMS, one of the LHC experiment. The caching approach improves overall application performance by decreasing data access latency and increasing data access throughput.…”

Section: Introductionmentioning

confidence: 96%

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Predicting Resource Utilization Trends with Southern California Petabyte Scale Cache

Sim,

Wu,

Sim

et al. 2024

EPJ Web of Conf.

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Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 96%

Predicting Resource Utilization Trends with Southern California Petabyte Scale Cache

Sim,

Wu,

Sim

et al. 2024

EPJ Web of Conf.

Self Cite

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“…• RedirCache -Reading data over SoCal Cache [20] Redirector distributed between 2 Sites: Caltech and UCSD. Tests (RedirCache1) were repeated to highlight identified issues below.…”

Section: Testing Environmentmentioning

confidence: 99%

Job CPU Performance comparison based on MINIAOD reading options: Local versus remote

Balcas,

Newman,

Bhat

et al. 2024

EPJ Web of Conf.

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A critical challenge of performing data transfers or remote reads is to be as fast and efficient as possible while, at the same time, keeping the usage of system resources as low as possible. Ideally, the software that manages these data transfers should be able to organize them so that one can have them run up to the hardware limits. Significant portions of LHC analysis use the same datasets, running over each file or dataset multiple times. By utilizing "ondemand" based regional caches, we can improve CPU Efficiency and reduce the wide area network usage. Speeding up user analysis and reducing network usage (and hiding latency from jobs by caching most essential files on demand) are significant challenges for HL-LHC, where the data volume increases to an exabyte level. In this paper, we will describe our journey and tests with the CMS XCache project (SoCal Cache), which will compare job performance and CPU efficiency using different storage solutions (Hadoop, Ceph, Local Disk, Named Data Networking). It will also provide insights into our tests over a wide area network and possible storage and network usage savings.

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“…When a user's computing job needs a file from SoCal Repo, the system first looks up the location of the file using the "Trivial File Catalogue" (TFC) [8,9]. Following the established convention for the tiered storage system, the data files are grouped into the namespace for the local cache nodes and the TFC points to a "local redirector" in XRootD where the "local redirector" knows all regional caches.…”

Section: Introductionmentioning

confidence: 99%

Access Trends of In-network Cache for Scientific Data

Han,

Sim,

et al. 2022

Preprint

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Scientific collaborations are increasingly relying on large volumes of data for their work and many of them employ tiered systems to replicate the data to their worldwide user communities. Each user in the community often selects a different subset of data for their analysis tasks; however, members of a research group often are working on related research topics that require similar data objects. Thus, there is a significant amount of data sharing possible. In this work, we study the access traces of a federated storage cache known as the Southern California Petabyte Scale Cache. By studying the access patterns and potential for network traffic reduction by this caching system, we aim to explore the predictability of the cache uses and the potential for a more general in-network data caching. Our study shows that this distributed storage cache is able to reduce the network traffic volume by a factor of 2.35 during a part of the study period. We further show that machine learning models could predict cache utilization with an accuracy of 0.88. This demonstrates that such cache usage is predictable, which could be useful for managing complex networking resources such as in-network caching. CCS CONCEPTS• Networks → Network performance analysis; • Computing methodologies → Distributed computing methodologies.

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Moving the California distributed CMS XCache from bare metal into containers using Kubernetes

Cited by 6 publications

References 8 publications

Predicting Resource Utilization Trends with Southern California Petabyte Scale Cache

Predicting Resource Utilization Trends with Southern California Petabyte Scale Cache

Job CPU Performance comparison based on MINIAOD reading options: Local versus remote

Access Trends of In-network Cache for Scientific Data

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