Flow-Based Management For Energy Efficient Campus Networks
Bibliographic record
Abstract
Recent studies have shown that the energy consumption of wireless access networks is a threat to the sustainability of mobile cloud services. Consequently, energy efficient solutions are becoming crucial for both local and wireless access networks. In this paper, we propose a flow-based management framework to achieve energy efficiency in campus networks. We address the problem from the dynamic perspective, where users come and leave the system in an unpredictable way. Specifically, we propose an online flow-based routing approach that allows dynamic reconfiguration of existing flows as well as dynamic link rate adaptation, while taking into account users' demands and mobility. Our approach is compliant with the emerging software defined networking (SDN) paradigm since it can be integrated as an application on top of an SDN controller. To achieve this, we first formulate the flow-based routing problem as an integer linear program (ILP). As this problem is known to be NP-hard, we then propose a simple yet efficient ant colony-based approach to solve the formulated ILP. Through extensive simulations, we show that our proposed approach is able to achieve significant gains in terms of energy consumption, compared to heuristic solutions and conventional routing solutions such as the shortest path (SP) routing, the minimum link residual capacity routing metric (MRC), and the load balancing (LB) scheme. In particular, we show that the energy consumption can be reduced by up to 7%, 35%, 44%, and 49% compared to Greedy-OFER, MRC, SP, and LB, respectively, while ensuring the required quality of service (QoS).
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".