Toward Cloud-Based Distributed Interactive Applications: Measurement, Modeling, and Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the prevalence of broadband network and wireless mobile network accesses, distributed interactive applications (DIAs) such as online gaming have attracted a vast number of users over the Internet. The deployment of these systems, however, comes with peculiar hardware/software requirements on the user consoles. Recently, such industrial pioneers as Gaikai, Onlive, and Ciinow have offered a new generation of cloud-based DIAs (CDIAs), which shifts the necessary computing loads to cloud platforms and largely relieves the pressure on individual user's consoles. In this paper, we aim to understand the existing CDIA framework and highlight its design challenges. Our measurement reveals the inside structures as well as the operations of real CDIA systems and identifies the critical role of cloud proxies. While its design makes effective use of cloud resources to mitigate client's workloads, it may also significantly increase the interaction latency among clients if not carefully handled. Besides the extra network latency caused by the cloud proxy involvement, we find that computation-intensive tasks (e.g., game video encoding) and bandwidth-intensive tasks (e.g., streaming the game screens to clients) together create a severe bottleneck in CDIA. Our experiment indicates that when the cloud proxies are virtual machines (VMs) in the cloud, the computation-intensive and bandwidth-intensive tasks may seriously interfere with each other. We accordingly capture this feature in our model and present an interference-aware solution. This solution not only smartly allocates workloads but also dynamically assigns capacities across VMs based on their arrival/departure patterns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 it