Applying adaptation spaces to support quality of service and survivability
Bibliographic record
Abstract
Adaptation is a key technique in constructing survivable information systems. Allowing a system to continue running, albeit with reduced functionality or performance, in the face of reduced resources, attacks, or broken components is often preferable to either complete shutdown or continued normal operation in compromised mode. However, unpredictable adaptation can sometimes be worse than the problem it seeks to cope with. In this paper we introduce adaptation spaces, which precisely and predictably specify the adaptation of a software component. We then present two survivable systems that have been specified and implemented using adaptation spaces. The first example uses user preferences regarding quality in an audio application to guide the adaptation when available bandwidth decreases. The second trades off performance overhead with intrusion resistance for "stack-smashing" attacks. We formally define an adaptation space and show briefly how it enables certain kinds of reasoning about adaptive applications. We conclude with related work and future plans.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".