Combined Degree-Based with Independent Dominating Set Approach for Controller Placement Problem in Software Defined Networks
Bibliographic record
Abstract
In Software Defined Networks (SDN), controllers should ideally be optimally placed to preserve the minimal response time between the controllers and the forwarding nodes in the data plane. It is well understood that the placement of controllers can greatly impact network performance in terms of controller responsiveness. An Independent Dominating Set (IDS) offers an optimized way of achieving the goal of minimal response time. However, finding an independent dominating set of minimal size is NP-hard, which means that there likely no polynomial-time algorithm can guarantee an optimal solution. In this paper, we propose a new node degree-based algorithm named High Degree with Independent Dominating Set (HDIDS) for the controller placement problem for the SDN networks. Our algorithm is composed of two phases to deal with controller placement: (1) determining candidate controller instances by selecting those nodes with highest node degree; and (2) partitioning the network into multiple domains, one controller per domain, exploiting the independent dominating set approach to ensure a distribution of controllers with lowest response times. Experiments results show that our algorithm, for a fixed number of selected controllers, has better performance in terms of minimizing the average response time between each controller and its forwarding nodes, and the response time of node with largest delay from the controller, as compared to a previously published algorithm based on optimized K-means. Further, we show that for an upper limit threshold for the required response time for an SDN, our algorithm nearly always requires fewer controllers to satisfy that threshold.
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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) | 0.002 | 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 | 0.001 |
| 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".