Fault Tolerance in Service Function Chains: A Taxonomy, Survey and Future Directions
Bibliographic record
Abstract
Abstract The rise of Network Function Virtualization (NFV) has revolutionized the network services, by enabling the delivery of flexible and scalable solutions through Service Function Chains (SFCs). However, the virtualized deployment of SFCs increases their susceptibility to various types of failures in comparison to their physical deployment. Hence, a thorough understanding of the failure types, their reasons of occurrence and management methods is important. This will help us to achieve high fault tolerance in virtualized SFCs equivalent or perhaps higher to their physical counter parts while reaping the benefits of virtualization. This article attempts to present a systematic review of fault tolerance practices for SFCs. As an outcome, we propose a comprehensive taxonomy that categorizes the state-of-the-art in fault tolerance in SFCs based on failure types, and their pattern of occurrence, failure management approaches, and their evaluation metrics. A critical analysis of the state-of-the-art is performed to identify the key trends and research gaps. A conceptual framework to achieve high fault tolerance in SFCs is presented along with a discussion on future research directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".