Pith. sign in

REVIEW 1 cited by

Introducing JIRIAF: A Virtual Kubelet Integration for Optimizing HPC Resource Provisioning

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.18596 v1 pith:B3KZL2UQ submitted 2025-02-25 cs.DC

classification cs.DC
keywords jiriafsystemacrossresourceapplicationscomputingcontrolenvironments
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The JIRIAF (JLab Integrated Research Infrastructure Across Facilities) framework is designed to streamline resource management and optimize high-performance computing (HPC) workloads across heterogeneous environments. Central to JIRIAF is the JIRIAF Resource Manager (JRM), which effectively leverages Kubernetes and Virtual Kubelet to manage resources dynamically, even in environments with restricted user privileges. By operating in userspace, JRM facilitates the execution of user applications as containers across diverse computing sites, ensuring unified control and monitoring. The framework's effectiveness is demonstrated through a case study involving the deployment of data-stream processing pipelines on the Perlmutter system at NERSC, showcasing its capability to manage large-scale HPC applications efficiently. Additionally, we discuss the integration of a digital twin model for a simulated queue system related to a streaming system, using a Dynamic Bayesian Network (DBN) to enhance real-time monitoring and control, providing valuable insights into system performance and optimization strategies.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking

    cs.IR 2026-02 conditional novelty 4.0 of 10

    A transformer-based sequential recommender, Feed SR, improved LinkedIn Feed time spent by 2.10% in an online A/B test and now serves the majority of Feed traffic.

Pith tools