On the Structuring of Reliable Multicast Protocols for Distributed Mobile Systems
Bibliographic record
Abstract
We consider reliable multicast in distributed systems including mobile hosts (MHs) that communicate with a wired infrastructure by means of wireless links. Nearly all existing proposals are based on hand-off, i.e. whenever a MH switches cell, state information about this host travels across the wired network from the support station of the old cell to that of the new cell. However, we are not aware of any detailed performance analysis for hand-off based reliable multicast protocols: previous research in this area has focused mainly on correctness rather than on performance. We analyze in detail, by simulation, the performance of a proposal by Acharya and Badrinath that is based on hand-off and has been highly influential in the design of later protocols. Then, we compare this proposal with one by us that is based on an entirely different philosophy and is the only existing proposal not based on hand-off. Surprisingly, we found that our proposal outperforms the one by Acharya and Badrinath in all the aspects considered: latency, scalability, bandwidth usage efficiency and quickness in managing cell switches of MHs. Moreover, we found that this performance improvement is not obtained at the expense of increased resource requirements on MHs such as energy or memory. We believe that this performance and cost analysis allows us to gain insights into the design of reliable multicast protocols for distributed mobile systems.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".