Non-Intrusive and Workflow-Aware Virtual Network Function Scheduling in User-Space
Bibliographic record
Abstract
The simple programming model and very low-overhead I/O capabilities of emerging packet processing techniques leveraging kernel-bypass I/O and poll-mode processing is gaining significant popularity for building high performance softwaremiddleboxes(akaVirtual Network Functions (VNFs)). However, existing OS schedulers fall short in rightsizing CPU allocation to poll-mode VNFs due to the schedulers’ shortcoming in capturing the actual processing cost of these VNFs. This issue is further exacerbated by their inability to consider VNF processing order when VNFs are chained to form Service Function Chains (SFCs). The state-of-the-art VNF schedulers proposed as an alternative to OS schedulers areintrusive, requiring the VNFs to be built with scheduler specific libraries or having carefully selected scheduling checkpoints. This highly restricts the VNFs that can properly work with these schedulers. In this article, we presentUNiS, aUser-spaceNon-intrusive work-flow aware VNFScheduler. Unlike existing approaches, UNiS is non-intrusive, i.e., does not require VNF modifications and treats poll-mode VNFs as black boxes. UNiS is also workflow-aware, i.e., takes SFC processing order into account while scheduling VNFs. Testbed experiments show thatUNiSis able to achieve a throughput within 90 and 98 percent of that achievable using an intrusive co-operative scheduler for synthetic and real data center traffic, respectively.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".