Datacenter Traffic Shaping for Delay Reduction in Cloud Gaming
Bibliographic record
Abstract
Cloud Gaming enables users to play games using a thin-client, regardless of their location or what platform they use (PCs, laptops, tablets, smartphones). Since the major computational parts of game processing are performed in datacenters, effectively assigning the resources (e.g. memory, bandwidth) to gaming sessions plays a key role in providing a high quality gaming experience to end-users. In this paper, we propose a traffic policing and shaping method using the Software Defined Networking (SDN) paradigm to solve the bandwidth allocation problem in cloud gaming datacenter networks. Our proposed method considers the current status of the datacenter paths in terms of remaining bandwidth and delay to achieve fair bandwidth allocation. The proposed scheme optimizes bandwidth utilization while ensuring compliance with the threshold of tolerable delay in cloud gaming systems. Our experimental results show that the proposed method improves bandwidth utilization and reduces end-to-end delay and delay variation (jitter) by almost 12% and 9%, respectively, without engendering additional packet loss compared to a representative conventional method: Equal Cost Multi-path (ECMP). These reductions lead to improvements in players' gaming experience.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".