Re-Architecting NFV Ecosystem with Microservices: State of the Art and Research Challenges
Bibliographic record
Abstract
Network Function Virtualization (NFV), considered a key enabler of network "softwarization", promises to reduce capital and operational expenditures for network operators by moving packet processing from purpose-built hardware to software running on commodity servers. However, the state-of-the-art in NFV is merely replacing monolithic hardware with monolithic VNFs, the software that realizes different network functions (e.g., firewalls, WAN optimizers, and so on). Although this is a first step toward deploying NFV, common functionality is repeatedly implemented in monolithic VNFs. Repeated execution of such redundant functionality introduces processing overhead when VNFs are chained to realize Service Function Chains and leads to sub-optimal usage of infrastructure resources. This stresses the need for re-architecting the NFV ecosystem, from VNFs to their orchestration, through modular VNF design and flexible service composition. In that perspective, we make the case for using the microservice software architecture, proven to be effective for building large-scale cloud applications from reusable and independently deployable components, to re-architect the NFV ecosystem. We also discuss the state-of-the-art in realizing modular VNFs from both industry and academia. Finally, we outline a set of research challenges for microservice-based NFV platforms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".