A quality of service management testbed
Bibliographic record
Abstract
Today's computing environments are becoming more and more distributed in nature. At the same time, the applications used in these environments are becoming more complicated and are being used in more mission critical roles in the enterprise. Consequently, users demands for performance, reliability, and availability are increasing rapidly. To meet these needs, a high level of quality of service must be delivered to the user. Doing so, however, is not an easy task. Because of considerable research effort into this area, we are making great strides towards acceptable quality of service solutions. To facilitate continuing work in this area, we must have access to a tightly controllable, highly portable, and flexible environment suitable for quality of service experimentation. Not only is such an experimental testbed useful for gaining valuable insight into developing better quality of service solutions, but it is also required for evaluating the merit of these solutions. To address this problem, we have developed a testbed for carrying out experimentation into quality of service management. We present our initial requirements for the testbed, its design, and a prototype implementation. We describe experimentation to date and evaluate the testbed and its effectiveness based on these preliminary results. Finally, we conclude with a summary of our work and outline our plans to evolve the testbed to accommodate further experimentation and work in the future.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".