FlowTrace: measuring round-trip time and tracing path in software-defined networking with low communication overhead
Bibliographic record
Abstract
In today’s networks, load balancing and priority queues in switches are used to support various quality-of-service (QoS) features and provide preferential treatment to certain types of traffic. Traditionally, network operators use ‘traceroute’ and ‘ping’ to troubleshoot load balancing and QoS problems. However, these tools are not supported by the common OpenFlow-based switches in software-defined networking (SDN). In addition, traceroute and ping have potential problems. Because load balancing mechanisms balance flows to different paths, it is impossible for these tools to send a single type of probe packet to find the forwarding paths of flows and measure latencies. Therefore, tracing flows’ real forwarding paths is needed before measuring their latencies, and path tracing and latency measurement should be jointly considered. To this end, FlowTrace is proposed to find arbitrary flow paths and measure flow latencies in OpenFlow networks. FlowTrace collects all flow entries and calculates flow paths according to the collected flow entries. However, polling flow entries from switches will induce high overhead in the control plane of SDN. Therefore, a passive flow table collecting method with zero control plane overhead is proposed to address this problem. After finding flows’ real forwarding paths, FlowTrace uses a new measurement method to measure the latencies of different flows. Results of experiments conducted in Mininet indicate that FlowTrace can correctly find flow paths and accurately measure the latencies of flows in different priority classes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".