Chronos: Meeting coflow deadlines in data center networks
Bibliographic record
Abstract
Guaranteed performance for data-parallel applications is important for both service providers and cloud data centers that host such services. A job of data-parallel applications involves communication among multiple machines to transmit intermediate results. Such communication comprises a collection of parallel flows, which is abstracted as a coflow in recent proposals. In this paper, we study the problem of meeting deadlines for coflows in data center networks. Existing flow-level scheduling schemes are insufficient to guarantee the coflow-level performance, since a coflow can meet its deadline only when all its constituent flows finish on time. Due to the scarce bandwidth on the network bottleneck, it is vital to coordinate concurrent coflows to meet as many deadlines as possible. We present Chronos, a scheduling framework that captures the correlation of flows belonging to a coflow, and handles the resource allocation among multiple concurrent coflows. Chronos is work-conserving and starvation-free without integrating complicated admission control mechanisms. We show via extensive simulations on ns3 that Chronos can make 1.6× more coflows meet their deadlines compared to flow-level schemes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".