Informed Network Routing for SD-WAN enabled SaaS Optimization: An M365 Proof of Concept
Bibliographic record
Abstract
The majority of applications nowadays reside in the cloud, allowing users to utilize remote resources and services providing flexibility and low cost access to a large range of applications. An inherent part of cloud operations is monitoring, which allows to survey the performance of the consumed applications and take corrective actions whenever a performance drift is noticed. Nonetheless, cloud telemetry tools are only concentrating in monitoring the data center infrastructure that hosts the cloud native applications, failing to assure the performance in a user to cloud continuum fashion. Hence, in this paper, we propose a two-way monitoring approach, leveraging Software Defined Wide Area Network (SD-WAN) capabilities. Specifically, by installing monitoring probes in both the WAN and cloud infrastructures, cloud vendors can monitor user traffic and provide application performance view on network links, in order to take Informed Network Routing (INR) decisions. Our results, using Microsoft 365 (M365) as a proof of concept, clearly show the benefit of the two-way monitoring approach in various traffic congestion scenarios. Additionally, since this is a first-of-its-kind solution, we also propose the foundations of a new communication protocol that can leverage the proposed two-way monitoring approach in larger scale and multi-vendor Software as a Service (SaaS) environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".