A hierarchical Quality of Service control architecture for configurable multimedia applications
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
In order to achieve the best application‐level Quality‐of‐Service (QoS), multimedia applications need to be dynamically tuned and reconfigured to adapt to fluctuating computing and communication environments. QoS‐sensitive adaptations are critical when applications run in general‐purpose systems, with no mechanisms provided for supporting resource reservations and real‐time guarantees. Such adaptations are triggered by resource availability variations caused by best‐effort resource allocations in unpredictable open environments. In this paper, we argue that adaptations are most effective to achieve a better QoS when performed within applications, where they may be optimized towards the best performance tradeoffs across various application parameters with different semantics. However, we believe that decisions about when and how adaptations should occur need to be coordinated, and formalized as a generic algorithm to be applied to a wide range of applications. For this purpose, we first identify an application model to focus on a set of application‐specific tuning ‘knobs’ and critical parameters, then propose a polynomial‐complexity QoS probing algorithm to quantitatively capture the run‐time relationships between the two sets of parameters. Finally, we present a hierarchical adaptive QoS control architecture to bridge the gap between original ‘triggers’ of adaptation and actual tuning ‘knobs’ to be invoked. To prove the validity of our architecture and algorithms, we present Agilos, a middleware implementation of our hierarchical architecture. Under its control, we show that a configurable multimedia tracking application is able to deliver optimal performance even when operating in unpredictable open environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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