Six Navers Lab / Einsia benchmarks and the work they test
Compare the tasks, scoring and evidence behind six Einsia benchmarks, with complete Radar detail pages and original references.
Claire Radar
CloudCons
CloudCons is an end-to-end benchmark for evaluating forecasting models in cloud resource consolidation. It encompasses datasets from Huawei Cloud, Microsoft Azure, and Google Borg with diverse workload patterns, and evaluates statistical, deep learning, and time series foundation models. The evaluation includes resource efficiency and service reliability metrics under a forecast-then-optimize paradigm.
Evidence available for this benchmark
Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.
Start with these resources
Cited documents and dates (1)
Catalog collected on 2026-10-14; resources and fields reflect that saved source.
Evaluation context and evidence
- Source
- Claire Radar
- Publisher
- Not recorded
- Benchmark release
- 2026-06-11
- Collected
- 2026-10-14
- Modality
- Not recorded
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Reported results 0
This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.