Introducing reliability and load balancing in mobile IPv6‐based networks
Bibliographic record
Abstract
Abstract Mobile IPv6 is an enabling platform for creating IP mobility in the evolution path towards next generation service offerings. However, Mobile IPv6 does not provide reliability and load balancing in the network. In this paper, we introduce ‘Virtual HA Reliability Protocol.’ It is an extension to Mobile IPv6 that introduces reliability and load balancing in the Mobile IPv6‐based networks. It also provides solutions to the problems caused due to Home Agent failures in Mobile IPv6. These problems are: delayed failure detection, service interruption in the upper layer applications, increased workload on the Mobile Node, message overhead over the air interface, and IPsec Security Associations re‐establishment. We also present the results of several experiments to assess the performance of our solution. The results show that our solution provides transparent Home Agent failure detection and recovery mechanisms. As a result, there is a significant reduction in message exchange over the air interface. Also, our solution provides high service availability in the upper layer applications. Moreover, there is reduced workload on the Mobile Node. Finally, the load balancing mechanism of our solution provides efficient, dynamic, and transparent load balancing among the multiple Home Agents. Thus our solution improves the overall Mobile IPv6 and upper layer applications performance. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".