CTL-Based Adaptive Service Composition in Edge Networks
Bibliographic record
Abstract
With the recent adoption of edge computing,Internet ofThings (IoT) devices collaborate at the network edge to facilitate edge-native applications. In this setting,IoTdevices are typically encapsulated asIoTservices to encode their functionalities, and their collaboration is achieved throughIoTservice composition. Due to the continuous resource occupancy, release, and consumption ofIoTdevices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certainIoTservices may significantly downgrade in theirQuality-of-Services (QoS). To guarantee the compatibility of compositions withQoSvariations, this article proposes an adaptive composition mechanism leveragingComputationTreeLogic (CTL) specifications. Specifically, we formalize the composition as a temporal task, and convert it toCTLformulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted byCTLsemantics during the execution of compositions. Besides, we construct aQoSDependencyGraph (QoSDG) to captureQoSvariations, and achieve adaptive composition with dynamicQoSsatisfactions. 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 higherQoSdependencies ranging from 0.3$\%$to 27.8$\%$.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".