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NajatAlsa
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NajatAlsa
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mistralai/Mistral-7B-Instruct-v0.3
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This repository contains four QLoRA fine-tuned adapters for Mistral 7B Instruct, one for each QoS weight scheme (A, B, C, D), trained as part of the LLM-VNSE framework for QoS-aware volunteer node selection in fog computing environments. The adapters are released as reproducibility artefacts accompanying the paper:
"Calibrated and Adaptive Listwise Ranking with Large Language Models for Quality-of-Service-Aware Volunteer Node Selection in Fog Computing"
| Adapter | Scheme | Application Context | Top-1 (%) | MAE | Spearman | NDCG |
|---|---|---|---|---|---|---|
| scheme-a | A — Equal weights | Ambient sensing | 92.03 | 0.3211 | 0.9734 | 0.9958 |
| scheme-b | B — Latency-dominant | Latency-sensitive interactive | 91.24 | 0.3570 | 0.9703 | 0.9955 |
| scheme-c | C — Availability-dominant | Critical monitoring | 83.27 | 0.4040 | 0.9625 | 0.9924 |
| scheme-d | D — Throughput-dominant | Bulk data transfer | 91.24 | 0.3203 | 0.9728 | 0.9966 |
The adapters were fine-tuned on the QWS Dataset 2.0 (2,507 real web services across five QoS attributes: response time, throughput, availability, reliability, and latency). The dataset was partitioned into 2,005 training lists, 251 validation lists, and 251 test lists following an 80/10/10 ratio. Ground-truth rankings were generated using a weighted-sum scoring function under each scheme's weight configuration as defined in Table 1 of the paper.
These adapters are intended for QoS-aware listwise ranking of volunteer nodes in fog computing environments. Given a candidate pool of volunteer nodes with their QoS profiles, the fine-tuned model produces a complete ranked ordering from best to worst under the active SLA priority configuration.
Dataset distribution: The model was trained on QWS Dataset 2.0 collected in 2008, which describes stable server-hosted web services. Transferability to real volunteer fog node deployments may be limited due to distributional differences in response time and throughput.
ICL instability: When used as a base model under in-context learning without fine-tuning, Mistral 7B exhibits severe confidence compression (scores clustering around 0.5) and sensitivity to prompt serialisation format, with accuracy drops of 17–23% under Mathematical Vector/Tuple Notation compared to the original key-value format. Fine-tuning with these adapters overcomes these limitations.
Scheme-specific: Each adapter is optimised for its corresponding weight configuration. For cross-scheme generalisation without retraining, use the weight-conditioned Protocol II approach described in the paper.
bibtex
@article{alsaiari-llmvnse, title={Calibrated and Adaptive Listwise Ranking with Large Language Models for Quality-of-Service-Aware Volunteer Node Selection in Fog Computing}, author={Alsaiari, Najat and Hussain, Farookh}, year={2026}}Code and reproducibility artefacts: github.com/NajatAlsaiari/LLM-VNSE
@article{alsaiari-llmvnse, title={Calibrated and Adaptive Listwise Ranking with Large Language Models for Quality-of-Service-Aware Volunteer Node Selection in Fog Computing}, author={Alsaiari, Najat and Hussain, Farookh}, year={2026}}