Profile-based mobile MPLS protocol
Bibliographic record
Abstract
Mobile MPLS is a new scheme that integrates mobile IP and MPLS protocols, in order to enable the MPLS protocol to support mobility. In mobile MPLS, when a correspondent node (CN) wants to communicate with a mobile user, the CN will first send its packets to the mobile user's home domain. Then, the home agent (HA) of the mobile user will forward the packets to the mobile user through the label switched path (LSP) from the HA to the current location of the mobile user. This forwarding process leads to a "triangle routing" problem. Even if route optimization is applied, several of the packets transmitted at the beginning of the connection will still be forwarded through the remote HA of the mobile user, since there is no address binding for the mobile user in the cache of the CN at the very beginning of the transmission. However, if the mobile user's behavior, e.g. the user's mobility pattern, travel schedule, are predictable or well-known before the transmission of the packets, this kind of "triangle routing" problem can be avoided. We propose a profile-based mobile MPLS protocol. The profiles maintain the regular behaviors of the mobile users. If the CN is able to obtain the profile of the mobile user, it will know (to some extend of accuracy) the current location of the mobile user and forward its traffic to the area the user is expected to be, thus eliminating the "triangle routing". Two schemes are proposed to maintain the profiles of the mobile users. One is based on a distributed, the second on a centralized approach. In the distributed scheme, the profiles are obtained directly from the mobile users, while in the centralized scheme a profile server is applied to maintain the profiles of all the mobile users in a domain. With the profile-based mobile MPLS approach, the delay for the traffic to the mobile user is reduced and the network performance is enhanced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".