Analyzing the Performance of SD-WAN Enabled Service Function Chains Across the Globe with AWS
Bibliographic record
Abstract
Cloud Computing has revolutionized the information technology world and the application offering over the last two decades. At the same time recent trends in Network Function Virtualization (NFV) and Software-Defined Wide Area Networks (SD-WAN) and the combination of those with the Cloud paradigm has allowed an unprecedented shift of enterprise networking services towards the Public Cloud. Even though this network evolutionary approach brings many benefits, it still presents many drawbacks as well. The performance stability and service continuity over a black box Public Cloud infrastructure can hinder the formal service guarantees that many new emerging applications may have. To this end, in this paper, we aim to shed light on the overall performance achieved when deploying coast-to-coast and intercontinental Service Function Chains (SFCs) that interconnect geographically distributed enterprise branches over the Amazon Web Services (AWS) infrastructure. In particular, we investigate the impact of region, Virtual Machine (VM) instance, time of the day and day of the week in the overall throughput and delay attained. The obtained results show the strengths and weaknesses of entirely relying on the AWS infrastructure to offer networking services by investigating possible hidden performance bottlenecks.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".