5G Service Function Chain Provisioning: A Deep Reinforcement Learning-Based Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We study the dynamic joint service function chain (SFC) embedding problem in a network function virtualization (NFV)-enabled edge cloud network. Our design goal is to optimize the network throughput by maximizing the average number of SFCs successfully embedded into the network, i.e., the Grade of Service (GoS), while guaranteeing their individual stringent end-to-end delay and resource constraints over a time horizon. To this end, we proposed a deep reinforcement learning (DRL)-based framework for jointly performing VNF embedding and routing tasks for the arrival SFCs in the considered NFV-enabled network. We implemented two versions of the proposed framework, one with the Deep Q-learning (DQL) method and one with the Advantage Actor-Critic (A2C) as the core algorithms, respectively. Moreover, for training these DRL algorithms and demonstrating the performance of the proposed framework, we implement a network environment based on the real-world network topology and a service request generator for generating SFCs traffic. Numerical results show that the DQL and A2C versions of the proposed framework achieve over 95% of the average GoS and over 95% of the network throughput ratio compared to the upper bound. This performance level is comparable to that of the near-optimal optimization-based approach while having ten times shorter execution times.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it