Service and Resource Aware Flow Management Scheme for an SDN-Based Smart Digital Campus Environment
Bibliographic record
Abstract
Recently, campuses have been embracing smart digital technologies in order to boost the efficiency of education and creativity. Thus, massive heterogeneous flows are generated as a result of multitude simultaneous access from several heterogeneous devices. This is putting pressure on campuses to make better management of their constrained resources and to ensure the required Quality of Service (QoS). In this paper, we propose a multi-flow management scheme over a software-defined smart digital campus network, named Service and Resource Aware Flow Management (SRAFM). Our approach offers a unified fully-programmable architecture, a distributed end-host-based flow characterization plane, and a centralized software-defined optimization model to efficiently manage heterogeneous flows. Network functionalities, including QoS aware routing and resource allocation optimization, are formulated as a mixed-integer linear programming problem. Due to its NP-hard complexity, we propose an approximation algorithm in a decomposed fashion based on Lagrangian Dual Decomposition (LDD) and subgradient methods to find an optimal solution for flow management. We evaluate our scheme from different aspects, including the number of simultaneous heterogeneous flows, QoS provisioning, characterization impacts, and network scalability. Compared to the well-known benchmarks in QoS aware routing and optimization problem, SWAY and LARAC, our simulation results conducted with a large number of flows over a small-scale network show promising performance. The proposed scheme significantly improves the cost reduction by 51% as compared to LARAC, the end-to-end delay by 21% and 34%, the bandwidth availability by 27% and 36%, and the QoS violation by 11% and 29% as compared to SWAY and LARAC, respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".