The brewing storm in cloud gaming: A measurement study on cloud to end-user latency
Bibliographic record
Abstract
Cloud computing has been a revolutionary force in changing the way organizations deploy web applications and services. However, many of cloud computing's core design tenets, such as consolidating resources into a small number of datacenters and fine-grain partitioning of general purpose computing resources, conflict with an emerging class of multimedia applications that is highly latency sensitive and requires specialized hardware, such as graphic processing units (GPUs) and fast memory. In this paper, we look closely at one such application, namely, on-demand gaming (also known as cloud gaming), that has the potential to radically change the multi-billion dollar video game industry. We demonstrate through a large-scale measurement study that the current cloud computing infrastructure is unable to meet the strict latency requirements necessary for acceptable game play for many end-users, thus limiting the number of potential users for an on-demand gaming service. We further investigate the impact of augmenting the current cloud infrastructure with servers located near the end-users, such as those found in content distribution networks, and show that the user coverage significantly increases even with the addition of only a small number of servers.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".