Exploring Microservices as the Architecture of Choice for Network Function Virtualization Platforms
Bibliographic record
Abstract
NFV is an emerging key technology that overcomes many challenges facing network service providers, such as reducing the capital and the operating expenses and satisfying the growing demand for mobile services. Integrating NFV with MEC and cloud environments requires an architecture that enables efficient implementations and deployments of NFV entities. Microservices architecture is a promising implementation of service-oriented architecture with recognized advantages in terms of modularity and continuous delivery. This article envisions microservices architecture as the solution of choice for building NFV platforms that are hosted in a dynamic environment ranging from MEC to cloud environments. This article addresses the major challenges and requirements of the microservices architecture to fully-exploit the potential of its adoption in NFV. It also proposes potential solutions that alleviate these issues. The article also discusses the need for agile and modular NFV entities along with MEC to realize various applications. To this end, the article discusses explicitly a novel NFV microservices entities scheduler optimization model. The proposed scheduler aims at minimizing network delays while taking into consideration various functional and non-functional constraints. The evaluation of the simulation results demonstrates that the proposed model minimizes the computational paths' latencies and improves the performance and availability of the NFV service chains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".