Leveraging Graph Neural Networks for SLA Violation Prediction in Cloud Computing
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 this paper we examine different approaches for the prediction of Service Level Agreements (SLAs) violations that occur during the service provisioning between cloud customers and providers. Despite the fact that there are many network metrics that involve the server - client interaction, it is an open research question how these available metrics can be used by a SLA prediction mechanism. We study three different data representation models for the network characteristics, a time series, a content and a context representation. We see that a context approach using graph representations captures efficiently the associativity of clients and improves the performance of traditional SLA violation prediction models when it is combined with them. The prediction of the SLA violations takes place using neural networks, making us propose a composite SLA prediction model that leverages Graph Neural Networks (GNNs). In our research, we put special emphasis and try different variations on how we construct the graphs. We perform an extensive performance evaluation of 23 different SLA prediction models that can be grouped into the three representations categories, namely the vector models that are based on network features, sequential models that leverage the temporal evolution of QoS metrics and Graph models that take into consideration the associativity of the clients. The experimental results show that our proposed GNN-based model can significantly improve the accuracy of SLA violation prediction, making it a useful tool for Cloud and Service providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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