A Bi-Objective Policy for Resilient and Sustainable SFC Management in Telco-Cloud Environments
Bibliographic record
Abstract
Achieving high availability in Service Function Chains (SFCs) typically requires deploying redundant Virtual Network Function (VNF) instances. However, this redundancy increases resource usage and energy consumption, raising the carbon footprint of the service. Reducing redundancy lowers the energy consumption but impacts the service availability during failures, creating a critical trade-off between service availability and sustainability. To address this, a novel bi-objective policy is introduced that leverages a Tradeoff Aware Embedding (TAE) algorithm using greedy approach for resource provisioning, and 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 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".