A comparative evaluation of mobile agent performance for network management
Bibliographic record
Abstract
Despite the strategic and software engineering benefits mobile agents (MAs) brings to network management, their performance is still a controversial issue. A number of quantitative analyses and experiments on mobile agent performance have been reported in the last few years. Among the claims in these studies, there are some obvious controversies. In an effort to determine the existence of contradiction and explore the cause for disagreement, this paper compares a number of evaluative studies of MA performance in the network management domain. Their experiments and analytical models are briefly described, and their findings and conclusions are highlighted for effective contrasting. With direct inference from this comparative survey, we suggest that many factors must be carefully taken into account when evaluating the MA network management paradigm. These key factors include the size of the network, the specific management tasks the MA is to perform, the initial MA size, data compression, the transfer mode, the specific platform adopted, etc. Our careful examination reveals that most of the disagreement is caused by difference in the above measurement factors. Ample evidence from the reviewed studies demonstrated that MAs are not actually efficient enough for real-time polling and collecting large amount of data, but we have enough proof that MAs demonstrate considerable robustness and performance in performing complex local computing, data filtering and updating. One contradiction in the body of literature is identified when the network has limited size. More carefully planed study is needed to solve this obvious contradiction.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".