Dynamic Aspect-Oriented Load Balancing in Java RMI.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Load balancing is the process of distributing client requests over a set of servers, and is a key element of obtaining good performance in a distributed application. Java RMI extends Java with distributed objects whose methods can be called from remote clients. In some Java RMI programs, there may be multiple replicas of a given object that can be the receiver of a remote method invocation. Effectively distributing these requests across these replicas requires either an extra balancer process or additional code on the client for this distribution. In this paper, we demonstrate the use of dynamic aspects in JAC to solve this problem. The client proxy is modified with an aspect to forward requests to a specific server, but the server is also able to shed load by altering or removing this aspect. The overhead of this approach is evaluated using a set of microbenchmarks.
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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 it