Barrier-Aware Max-Min Fair Bandwidth Sharing and Path Selection in Datacenter Networks
Bibliographic record
Abstract
In production datacenters operated by Web service providers such as Google, multiple data parallel applications, such as MapReduce, are employed to facilitate data processing at a large scale, with a strong demand for intra-datacenter bandwidth in their communication stages. A noteworthy phenomenon in these applications is the presence of barriers, which implies that a job will not finish until the last task completes. Existing flow-level sharing in datacenter networks is not designed and optimized to meet such application-level needs. In this paper, we promote the awareness of application barriers in the design of both bandwidth allocation and path selection strategies. In particular, we propose the notion of application-level fairness when bandwidth is allocated, with favorable properties of performance-centric max-min fairness and Pareto efficiency. Further, we show that both application-level performance and resource utilization can be further improved by considering path selection as well. With our implementation in the Mininet emulation testbed and large-scale simulations, we demonstrate that our new barrier-aware strategy for fair bandwidth sharing and path selection significantly outperforms barrier-agnostic strategy when application performance is concerned.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".