Linking handover delay to load balancing in SDN-based heterogeneous networks
Bibliographic record
Abstract
Software-Defined Networking (SDN) paradigm provides the ability to handle mobility more efficiently due to its programmability and fine granularity. However, in this emerging setting, the handover procedure still suffers delay due to exchanging and processing handover signaling messages. In this paper, we study the relevancy between an SDN controller’s load and handover delay. We show that an over-loading state can prolong handover delay, so as a countermeasure, reaching that state is mitigated by applying a load balancing mechanism. Our primary metric is the controller’s response time, as it directly affects the completion of any mobility-related procedure. We propose a load balancing management framework that deploys two concepts: network heterogeneity and context-aware vertical mobility. Our proposal is composed of three main aspects. First, we identify candidate users based on their context information. Second, we reduce the frequency of load dissemination between multiple controllers, and hence, reducing processing and communication overhead. Third, after the candidate users are determined, we optimize the decision problem on the selection among heterogeneous candidate networks. Through simulation, our framework has shown as much drop as a 28% drop in response time compared to previous proposals.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".