The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
bh_fdr: struct<family_order: list<item: string>, p_values: list<item: double>, q: double, reject: list<item: (... 7 chars omitted)
child 0, family_order: list<item: string>
child 0, item: string
child 1, p_values: list<item: double>
child 0, item: double
child 2, q: double
child 3, reject: list<item: bool>
child 0, item: bool
descriptive_aggregate_anwg: struct<azure_llm_2024: struct<slai_anwg_mean: double, vllm_anwg_mean: double>, bailian_qwen: struct< (... 114 chars omitted)
child 0, azure_llm_2024: struct<slai_anwg_mean: double, vllm_anwg_mean: double>
child 0, slai_anwg_mean: double
child 1, vllm_anwg_mean: double
child 1, bailian_qwen: struct<slai_anwg_mean: double, vllm_anwg_mean: double>
child 0, slai_anwg_mean: double
child 1, vllm_anwg_mean: double
child 2, burstgpt: struct<slai_anwg_mean: double, vllm_anwg_mean: double>
child 0, slai_anwg_mean: double
child 1, vllm_anwg_mean: double
generated_at_utc: string
primary_effects: struct<azure_llm_2024: struct<ci_hi: double, ci_lo: double, condition_label: string, excludes_zero: (... 453 chars omitted)
child 0, azure_llm_2024: struct<ci_hi: double, ci_lo: double, condition_label: string, excludes_zero: bool, n_windows: int64, (... 67 chars omitted)
child 0, ci_hi: double
child 1, ci_lo: double
child 2, condition_label: string
child 3, excludes_zero: bool
child 4, n_windows: int64
child 5, p_value_two_sided: double
...
alibration_manifest_sha256: string
child 3, case_selection_manifest_sha256: string
child 4, ci_level: double
child 5, head_sha: string
child 6, input_cell_count: int64
child 7, rng_seed_reversal: int64
child 8, rng_seed_stable_control: int64
child 9, slurm_array_job_id: string
child 10, simulator_reversal_source: struct<sha256: string>
child 0, sha256: string
child 11, simulator_stable_control_source: struct<sha256: string>
child 0, sha256: string
child 12, validation_manifest_sha256: string
reversal_analysis: struct<agrees_with_simulator_selected_direction: bool, both_conditions_supported: bool, sign_flip_ob (... 87 chars omitted)
child 0, agrees_with_simulator_selected_direction: bool
child 1, both_conditions_supported: bool
child 2, sign_flip_observed: bool
child 3, simulator_selected_x_winner: string
child 4, simulator_selected_y_winner: string
stable_control_analysis: struct<same_sign_both_conditions: bool>
child 0, same_sign_both_conditions: bool
stamp: string
note_on_raw_per_cell_records: string
release: string
files: struct<LSSP_THIRD_PARTY_SOURCE_LICENSES.md: struct<bytes: int64, sha256: string>, rq6/RQ6_ANALYSIS_R (... 49 chars omitted)
child 0, LSSP_THIRD_PARTY_SOURCE_LICENSES.md: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 1, rq6/RQ6_ANALYSIS_RESULT.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
note: string
to
{'files': {'LSSP_THIRD_PARTY_SOURCE_LICENSES.md': {'bytes': Value('int64'), 'sha256': Value('string')}, 'rq6/RQ6_ANALYSIS_RESULT.json': {'bytes': Value('int64'), 'sha256': Value('string')}}, 'release': Value('string'), 'note': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
bh_fdr: struct<family_order: list<item: string>, p_values: list<item: double>, q: double, reject: list<item: (... 7 chars omitted)
child 0, family_order: list<item: string>
child 0, item: string
child 1, p_values: list<item: double>
child 0, item: double
child 2, q: double
child 3, reject: list<item: bool>
child 0, item: bool
descriptive_aggregate_anwg: struct<azure_llm_2024: struct<slai_anwg_mean: double, vllm_anwg_mean: double>, bailian_qwen: struct< (... 114 chars omitted)
child 0, azure_llm_2024: struct<slai_anwg_mean: double, vllm_anwg_mean: double>
child 0, slai_anwg_mean: double
child 1, vllm_anwg_mean: double
child 1, bailian_qwen: struct<slai_anwg_mean: double, vllm_anwg_mean: double>
child 0, slai_anwg_mean: double
child 1, vllm_anwg_mean: double
child 2, burstgpt: struct<slai_anwg_mean: double, vllm_anwg_mean: double>
child 0, slai_anwg_mean: double
child 1, vllm_anwg_mean: double
generated_at_utc: string
primary_effects: struct<azure_llm_2024: struct<ci_hi: double, ci_lo: double, condition_label: string, excludes_zero: (... 453 chars omitted)
child 0, azure_llm_2024: struct<ci_hi: double, ci_lo: double, condition_label: string, excludes_zero: bool, n_windows: int64, (... 67 chars omitted)
child 0, ci_hi: double
child 1, ci_lo: double
child 2, condition_label: string
child 3, excludes_zero: bool
child 4, n_windows: int64
child 5, p_value_two_sided: double
...
alibration_manifest_sha256: string
child 3, case_selection_manifest_sha256: string
child 4, ci_level: double
child 5, head_sha: string
child 6, input_cell_count: int64
child 7, rng_seed_reversal: int64
child 8, rng_seed_stable_control: int64
child 9, slurm_array_job_id: string
child 10, simulator_reversal_source: struct<sha256: string>
child 0, sha256: string
child 11, simulator_stable_control_source: struct<sha256: string>
child 0, sha256: string
child 12, validation_manifest_sha256: string
reversal_analysis: struct<agrees_with_simulator_selected_direction: bool, both_conditions_supported: bool, sign_flip_ob (... 87 chars omitted)
child 0, agrees_with_simulator_selected_direction: bool
child 1, both_conditions_supported: bool
child 2, sign_flip_observed: bool
child 3, simulator_selected_x_winner: string
child 4, simulator_selected_y_winner: string
stable_control_analysis: struct<same_sign_both_conditions: bool>
child 0, same_sign_both_conditions: bool
stamp: string
note_on_raw_per_cell_records: string
release: string
files: struct<LSSP_THIRD_PARTY_SOURCE_LICENSES.md: struct<bytes: int64, sha256: string>, rq6/RQ6_ANALYSIS_R (... 49 chars omitted)
child 0, LSSP_THIRD_PARTY_SOURCE_LICENSES.md: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 1, rq6/RQ6_ANALYSIS_RESULT.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
note: string
to
{'files': {'LSSP_THIRD_PARTY_SOURCE_LICENSES.md': {'bytes': Value('int64'), 'sha256': Value('string')}, 'rq6/RQ6_ANALYSIS_RESULT.json': {'bytes': Value('int64'), 'sha256': Value('string')}}, 'release': Value('string'), 'note': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
LLM-Serving Scheduler Portability (LSSP) — v1.0.0
LSSP measures how portable comparative LLM-serving scheduler rankings are across workload sources, load regions, evaluation metrics, and SLO definitions: does the best-performing scheduling policy stay best when you change the traffic source or the load level, or do rankings reverse? This dataset is the derived analysis behind that question, paired with a selected-case physical validation (RQ6).
The underlying simulator campaign spans 9,360 unique policy–window–region configurations, each executed in two deterministic verification passes (18,720 total executions); the workload window, not the verification repetition, is the inferential unit.
This is an initial (v1.0.0), intentionally partial release — see "What's in this release" below for exactly what is and is not included, and why. Everything included is either byte-identical to its source (noted per file) or an explicitly-labeled, content-complete-on-the-claims- that-matter reduction.
Relation to the paper
Companion dataset to "How Portable Are LLM-Serving Scheduler Rankings
Across Workloads, Operating Regions, and Metrics?" (Soroush Vahidi;
GitHub: SoroushVahidi/llm-serving-scheduler-robustness-benchmark).
This dataset revision corresponds to GitHub release
v1.0.0
(commit 18128a8cf4d449c333c6db4d31788dd5eae180bd).
What's in this release
| Path | Contents | Fidelity |
|---|---|---|
rq6/RQ6_ANALYSIS_RESULT.json |
The complete reduced RQ6 result: per-source point estimates, 95% bootstrap CIs, bootstrap settings, and all upstream provenance hashes (execution SHA, three manifest hashes, simulator-source hashes) for Slurm job 1222413's 240/240-cell campaign. Sufficient to fully and independently verify every RQ6 claim in the manuscript. |
Complete on all reported numbers |
table_data/rq1_rq2_portability.json |
RQ1/RQ2 cross-source ranking-portability table (all 18 source-pair×region rows, all per-metric summaries). | Complete content, minified (whitespace-only change) |
table_data/rq4_sample_complexity.json |
RQ4/RQ5 sample-complexity recovery curves (all 3 sources × 5 sample sizes, concentrated-vs-spread comparison). | Complete on headline numbers; omits the exhaustive per-metric secondary_metric_thresholds breakdown, minified |
LSSP_THIRD_PARTY_SOURCE_LICENSES.md |
Per-source license/redistribution/attribution detail for BurstGPT, Azure-2024, Bailian/Qwen. | Byte-identical to GitHub source |
checksums.json |
SHA-256 for every byte-identical file above. | — |
README.md |
This dataset card. | — |
Not included in this v1.0.0 revision, and why:
table_data/rq3_reversals.json,table_data/rq5_temporal_robustness.jsonand the six intermediate canonicalanalysis_canonical/*.jsonoutputs (pairwise reversals, ranking correlations, sample complexity, telemetry explanation, temporal robustness, top-k overlap): deferred to a follow-up dataset revision. The manuscript's headline RQ1/RQ2/RQ4/RQ5 numbers are already fully present above; the deferred files back RQ3's pilot-scale result and finer per-metric/per-condition detail beyond the headline tables. Because these six files are absent,paper/scripts/generate_phase12_tables_figures.py(which consumes them as input) cannot currently be run against this dataset download — the releasedtable_data/*.jsonfiles are that script's output, already provided directly.manifests/(frozen campaign manifests, including the 18,720-execution campaign freeze): not duplicated here — every one of these files is already publicly tracked in the GitHub repository underartifacts/manifests/in the taggedv1.0.0release.- The full 240 raw per-cell RQ6 execution records and the exploded per-cell raw/enriched shards for RQ1–RQ5 (tens of megabytes, not yet packaged for release): the included reduced/canonical files are sufficient to verify every reported claim; the raw per-cell shards exist only for independent verification of the analysis step itself and are candidates for a follow-up revision.
- Raw third-party workload traces (BurstGPT, Azure LLM Inference Trace 2024, Bailian/Qwen): not redistributed by this project under any revision; obtain from their original sources subject to their respective terms — see
LSSP_THIRD_PARTY_SOURCE_LICENSES.md.
RQ3 status
RQ3 (synthetic-to-real ranking transfer) is an engineering-validation pilot only. It does not support a synthetic-to-real transfer conclusion, positive or negative; the transfer statistic is undefined in most pilot conditions by design. See the manuscript's Results section for detail.
RQ6 status
RQ6 (Slurm job 1222413, execution SHA 703a752762348bd911c9d93f17731fa5244b38f9)
is complete: 240/240 cells COMPLETED, independently validated, and
analyzed with the frozen robustbench.real_llm.rq6_validation_analysis
implementation (2,000-resample window-level paired bootstrap, 95% CI).
Result: all three real-system slai_faithful-vs-vllm_faithful ANWG
effects are statistically supported and favor vllm_faithful; the
simulator-predicted Azure/BurstGPT reversal did not reproduce on
physical hardware, while the Azure/Bailian-Qwen stable control did
retain its ordering. This is a selected-case validation (2 of 13 panel
policies, 3 sources, one operating region) — it is evidence about
simulator-to-hardware fidelity in this selected case, not a general
estimate of hardware reversal prevalence. See the manuscript's
Limitations section for exact scope.
License / redistribution
Code (GitHub repository) is MIT-licensed. This dataset — all
LSSP-derived files above — is released under the same MIT terms. No
raw third-party workload trace files are included. BurstGPT
(CC-BY-4.0), Azure LLM Inference Trace 2024 (CC-BY), and Bailian/Qwen
(Apache-2.0) are referenced by canonical source, release tag/commit, and
independently-verified SHA-256 only; this project has consistently
treated all three as read-only-referenced rather than duplicated. See
LSSP_THIRD_PARTY_SOURCE_LICENSES.md in this dataset for full
per-source detail, redistribution status, and acquisition instructions.
Citation
See CITATION.cff in the GitHub repository.
The archived-artifact citation is available now via the Zenodo DOI,
10.5281/zenodo.22306798; the
manuscript's own journal citation will be added once one exists (this
dataset makes no claim of journal publication or acceptance).
Limitations
- RQ6 evaluated 2 of 13 panel policies, 3 sources, one operating region, one hardware/software environment, one physical execution per (policy, source, window) cell.
- See "Not included in this revision" above for the specific deferred files and the reason for each.
METRIC_DEFINITION_SENSITIVITYandSLO_DEFINITION_SENSITIVITYrobustness families have no implementing artifact; disclosed as a gap, not populated with an invented result.- Bailian/Qwen license confidence is MEDIUM (single-source confirmation).
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