Siphon: expediting inter-datacenter coflows in wide-area data analytics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is increasingly common that large volumes of production data originate from geographically distributed datacenters. Processing such datasets with existing data parallel frameworks may suffer from significant slowdowns due to the much lower availability of inter-datacenter bandwidth. Thus, it is critical to optimize the delivery of inter-datacenter traffic, especially coflows that imply application-level semantics, to improve the performance of such geo-distributed applications. In this paper, we present Siphon, a building block integrated in existing data parallel frameworks (e.g., Apache Spark) to expedite their generated inter-datacenter coflows at runtime. Specifically, Siphon serves as a transport service that accelerates and schedules the inter-datacenter traffic with the awareness of workload-level dependencies and performance, while being completely transparent to analytics applications. Novel intra-coflow and inter-coflow scheduling and routing strategies have been designed and implemented in Siphon, based on a software-defined networking architecture. On our cloud-based testbeds, we have extensively evaluated Siphon's performance in accelerating coflows generated by a broad range of workloads. With a variety of Spark jobs, Siphon can reduce the completion time of a single coflow by up to 76%. With respect to the average coflow completion time, Siphon outperforms the state-of-the-art scheme by 10%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it