A pro-active mobility management scheme for pub/sub systems using neighborhood graph
Bibliographic record
Abstract
This paper presents a novel and efficient mobility management scheme based on a pro-active caching approach (i.e., state transfer/caching occurs prior to the subscriber's movement) to extend existing pub/sub systems to the mobile, wireless domain. This approach depends largely on a mechanism that intelligently pre-loads subscriber contexts one hop ahead of its current broker. To achieve this in an automated fashion, we introduce the notion of a neighbor graph, which is built automatically, to capture user mobility patterns. We have investigated the effectiveness of our proposed approach through testbed experiments, comparing it to the current state-of-the-art solutions, durable subscription-based and reactive, proposed in the literature. The experimental results across a broad-range of workload parameters show that our proactive approach outperforms earlier approaches by wide margins. Our approach supports fast handoff with low cost in terms of state transfer overhead. It also prevents message duplication and considerably reduces message loss.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".