Delayed Installation and Expedited Eviction: An Alternative Approach to Reduce Flow Table Occupancy in SDN Switches
Bibliographic record
Abstract
Limited flow table size in switches is a major concern for SDN applications. The common approach to overcome this problem is to identify elephant flows and solely focus on them. However, there is no gold standard to assess the effectiveness of such greedy solutions. In this paper, we formally define this problem by choosing a cost function (hit ratio) and an objective function to optimize the (average table occupancy) and present the optimum solution (i.e., theoretical gold standard) for it. We model the problem as a knapsack problem, analyze how its solution minimizes the table occupancy, and the similarities to and differences from the default idle timeout mechanism used in OpenFlow. We also present a new approach to minimize flow table occupancy based on the insight gained from the knapsack model analysis. Our solution expedites rule evictions by forecasting the TCP flow termination from RST/FIN packets and delays rule installation by incubating non-TCP flows. It reduces average flow table occupancy between 16%-62% in various networks with less than 1.5% reduction in hit ratio. Using three real-world packet traces, we compare the performance of our solution with the theoretically optimum solution, the static idle timeout approach used in current OpenFlow systems, and heavy hitter detection approaches that are commonly used to solve this problem. We provide in-depth analysis of when and where our approach outperforms other solutions, while discussing why it might be better to use rate-based heavy hitter detection in some scenarios.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".