OpenFlow rule placement in carrier networks for augmented reality applications
Bibliographic record
Abstract
Today, mobile consumers increasingly use Augmented Reality (AR) devices to stream personal video through carrier networks. Thanks to its flexibility, Software-Defined Networking (SDN) is deployed in many carrier networks to support end-to-end network-slicing, which is substantial for these AR applications. In an OpenFlow-enabled SDN network, a controller must decide the rules to be placed into the switches in the network, subject to multiple constraints such as memory capacity, link bandwidth limitation, and flow continuity. Due to legacy switch models, prior work focuses only on unicast flows which cannot efficiently support AR applications with streaming traffic from one a source device (or server) to many destination devices across the network. In this paper, we optimize rule placement in resource-constrained Openflow networks for both unicast and multicast flows. Our approach is to leverage the use of Group Tables, which is recently introduced in the Open-Flow 1.1 specification, to support multicast flows and, at the same time, save switch memory. Traffic to multiple destinations can be aggregated to match a single flow table entry per switch. Therefore, significant link resources can be saved. The experimental results on three different topologies show our solution can support a higher number of flows than the state-of-the-art solutions by reducing both the link usage by up to 30% and the number of flow entries needed to deliver the traffic to destinations by 22%.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".