Load migration in distributed softwarized network controllers
Bibliographic record
Abstract
Abstract Distributed control solutions were introduced to address controller reliability and scalability issues in software‐defined networking (SDN). The dynamic nature of network traffic can lead to load imbalance among controller instances. A highly loaded controller instance can be slow in responding to datapath queries and can slow down the entire control platform, as state synchronization and consensus among controller instances are performed in a cooperative manner. In this paper, we present Efficient, Resilient, Consistent (ERC), a novel protocol for migrating the load of a given switch from a controller instance to a different instance. Our protocol has three distinguishing properties compared with prior works in this area: (1) It is resilient to failures during migration, (2) it maintains consistency among all controller instances, and nevertheless, (3) it is more efficient than existing load migration protocols. Compared with state‐of‐the‐art, ERC reduces the migration time by 23–50% depending on network load. The implicit assumed use case in the design of previous load migration algorithms (including ERC) has been the load balancing scenario. However, as this is not the only possible case, by maintaining the desirable properties of ERC, we introduce four variants of our protocol that can add to the versatility of the load migration handling. This is achieved by considering variations of role exchange between controller instances, which gives us an advantage over the fixed master–slave exchange that vanilla ERC or previous work support. We perform an extensive set of experiments to examine the impact of variable network parameters on the performance metrics of interest and to show the effectiveness of the ERC family of protocols in load migration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".