Game-Aware and SDN-Assisted Bandwidth Allocation for Data Center Networks
Bibliographic record
Abstract
Cloud computing has recently emerged as a promising paradigm for end-users and service providers. The application of the cloud-computing model to different applications offers many attractive advantages, such as scalability, ubiquity, reliability, and cost reduction to users and providers. By applying this model, the major computational parts of underlying applications are performed in data centers. Hence, effectively assigning the resources (e.g. memory, bandwidth) to applications plays a key role in providing a high Quality of Experience (QoE) to end-users. In the case of delay sensitive applications like video streaming and online gaming, the efficient resource allocation becomes more crucial. In this paper, we propose a game traffic friendly bandwidth utilization scheme using the Software Defined Networking (SDN) paradigm to solve the bandwidth allocation problem in cloud computing data center networks. Our proposed method makes use of machine learning techniques to classify the incoming traffic flows in real-time while ensuring game flows are prioritized over others. Our simulation results for a realistic network topology indicate good performance in terms of network traffic classification accuracy, and improvements of at least 9% in average utility (QoE), up to 30% increase in fairness (according to the Jain’s fairness index), and on average an 8% reduction in delay experienced by users compared to a representative conventional method: Equal Cost Multi-path (ECMP).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".