CTL-Based Adaptive Service Composition in Edge Networks
Why this work is in the frame
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Bibliographic record
Abstract
With the recent adoption of edge computing, <i>I</i> nternet of <i>T</i> hings ( <i>IoT</i> ) devices collaborate at the network edge to facilitate edge-native applications. In this setting, <i>IoT</i> devices are typically encapsulated as <i>IoT</i> services to encode their functionalities, and their collaboration is achieved through <i>IoT</i> service composition. Due to the continuous resource occupancy, release, and consumption of <i>IoT</i> devices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certain <i>IoT</i> services may significantly downgrade in their <i>Q</i> uality-of- <i>S</i> ervices ( <i>QoS</i> ). To guarantee the compatibility of compositions with <i>QoS</i> variations, this article proposes an adaptive composition mechanism leveraging <i>C</i> omputation <i>T</i> ree <i>L</i> ogic ( <i>CTL</i> ) specifications. Specifically, we formalize the composition as a temporal task, and convert it to <i>CTL</i> formulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted by <i>CTL</i> semantics during the execution of compositions. Besides, we construct a <i>QoS</i> <i>D</i> ependency <i>G</i> raph ( <i>QoSDG</i> ) to capture <i>QoS</i> variations, and achieve adaptive composition with dynamic <i>QoS</i> satisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higher <i>QoS</i> dependencies ranging from 0.3 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> to 27.8 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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