A Bi-Objective Policy for Resilient and Sustainable SFC Management in Telco-Cloud Environments
Bibliographic record
Abstract
Service Function Chains (SFCs), built using Virtual Network Functions (VNFs), offer a flexible method for delivering modern network services but pose challenges in availability and sustainability. This paper addresses this challenge by proposing an availability-and sustainability-aware resource provisioning and SFC embedding bi-objective policy for telco-cloud computing systems combining edge and cloud resources. The policy leverages a Tradeoff Aware Embedding (TAE) algorithm using greedy approach for resource provisioning, alongside a PSO-based Redundancy Optimizer (PRO) that dynamically adjusts VNF redundancy to meet service availability requirements without increasing the carbon footprint. To further enhance the fault tolerance, the policy integrates a Gradient-Boosting (GB) enabled failure prediction and remediation mechanism with checkpointing called Redundancy with Checkpointing (RPC), enabling proactive failure detection and rapid recovery. Simulation results using real-world failure traces demonstrated that the proposed policy with RPC achieved up to 99.987% availability while reducing the carbon footprint of the service by over 87% and service failures by more than 98% compared to the worst-case without redundancy and checkpointing mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".