Experiences with client-based speculative remote display
Bibliographic record
Abstract
We propose an approach to remote display systems in which the client predicts the screen update events that the server will send and applies them to the screen immediately, thus eliminating the network round-trip time and making the system more responsive in a wide-area or high loss environment. Incorrectly predicted events are undone when the actual events arrive from the server. The approach requires no server or protocol changes, and thus can work with existing systems. Since it is core to the feasibility of such a speculative remote display system, we study the predictability of the events that occur under typical workloads in two extant systems, Windows Remote Desktop and VNC. We find that simple, state-limited Markov models are often able to correctly predict the next event. Based on these results, we design, implement, and evaluate a speculative remote display extension to the VNC client. In our implementation, the end user can trade off between the responsiveness of the display and the level of temporarily displayed incorrect predictions. We evaluate VNC/SRD with two user studies. We conclude by describing design alternatives.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".