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Paper Citation Record · LEDGER

Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2205.11913.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2205.11913 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:19:19.272330Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T18:35:11.317944Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8d2faf1c-7b5b-410e-a0d9-2757d24dd339 · inbound

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling cites this paper.

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:35:11.331805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T18:35:10.985537Z digest=sha256:70c255554801155def667056cf84f52c1cb2609cc15f0da71da27db7a4f37573

Observation 2bb8d257-8f46-4579-abe1-33f20f8ee644 · inbound

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks cites this paper.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-15T17:19:19.272330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T17:19:19.272330Z digest=sha256:d347038052aeca996838c96d86beb80422d4956fb0ecbeb8961bc0c565edca6e