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PADLL: Taming Metadata-intensive HPC Jobs Through Dynamic, Application-agnostic QoS Control

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Ricardo Macedo, Mariana Miranda, Yusuke Tanimura, Jason Haga, Amit Ruhela, Stephen Lien Harrell, Richard Todd Evans, José Pereira
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João Paulo

Distributed, Parallel, and Cluster Computing · Submitted 13 Feb 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.

how it is stated · asserted 1.00 read the record →

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arXiv ID2302.06418 (version 2)
Pith IDKU3RVM6V, Pith's permanent record of this paper and its review
CitationAmit Ruhela, Jason Haga, Jo\~ao Paulo, Jos\'e Pereira, Mariana Miranda, Ricardo Macedo, Richard Todd Evans, Stephen Lien Harrell, Yusuke Tanimura. "PADLL: Taming Metadata-intensive HPC Jobs Through Dynamic, Application-agnostic QoS Control." arXiv:2302.06418 (2023). https://pith.science/paper/KU3RVM6V
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,954 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.08 · human participants 0.02 · controlled comparison 0.13 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.12 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple concurrent applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. background 1.00 0.07 0.02 asserted 0.97
s1 However, having multiple concurrent applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. background 0.98 0.07 0.03 asserted 0.55
s2 We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. contribution 1.00 0.90 0.13 asserted 1.00
s3 It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. method 0.97 0.74 0.05 asserted 1.00
s4 We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. method 0.81 0.96 0.07 asserted 1.00
s5 Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization. result 0.94 0.96 0.86 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.99.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple concurrent applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.

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