Software-defined networking-based resource management: data offloading with load balancing in 5G HetNet
Bibliographic record
Abstract
Abstract The explosive growth of mobile data traffic and the shortage of the available spectral resources have created new challenges for future cellular networks. In particular, resource management in heterogeneous network environment has become a critical issue. In this paper, we propose software-defined networking (SDN)-based resource management algorithms for future cellular network. Specifically, in this work, we have a threefold objective: i) alleviate spectrum shortage concerns by efficiently offloading traffic over the Wi-Fi network, ii) address network congestion by optimally balancing loads across multiple cells and iii) achieve the aforementioned objectives while taking network conditions and the end user quality-of-service (QoS) requirements into consideration. To this end, we present SDN-based partial data offloading and load balancing algorithms. The proposed algorithms exploit an SDN controller’s global view of the network and take optimized resource allocation decisions. We analyze the performance of the proposed algorithms under realistic network model. Moreover, we also present an analytical framework to quantify the delay incurred due to the SDN-based data processing and forwarding. Our analysis and system-level simulations show that the proposed load balancing algorithm significantly improves the equilibrium extent and network stability as compared to the baseline algorithms. On the other hand, the proposed partial data offloading algorithm is shown to satisfy end user’s quality-of-service while saving a significant amount of cellular resources.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".