Load balancing model for mobile peer-to-peer networks-based 3D streaming
Bibliographic record
Abstract
Recently a great deal of attention has been dedicated to the design of virtual environment based class of applications. So far, a significant body of work has been devoted to the challenges of 3D streaming with a desktop scenario; whereas a little research work has been committed to 3D streaming on thin mobile devices that mainly focused to address challenges such as streaming performance, bandwidth limitation, and supplying partners strategies. To deal with the latter issue, i.e. supplying partner strategies, several approaches proposed to use a nearby peer. However, since mobile devices have limited capabilities and resources, there is a high probability that a number of peers will be always serving the peers in need, which makes them overloaded and which consumes their resources. Therefore, a load balancing mechanism has to be added in order to balance the overhead among peers and avoid having an overloaded peer, with low energy, and reduced resources and bandwidth. In this paper, we propose a load balancing mechanism for P2P mobile 3D streaming supplying partners techniques. Our proposed scheme tries, using a set of parameters and cost factors, (1) to identify the highly active peers, i.e. those that are frequently selected to stream data to peers in need, (2) to unburden the loaded sources, and (3) to make a balanced distribution of content among the remaining supplying partners. By applying the load balancing mechanism in conjunction with the supplying partner strategies, our protocol ensures to make the system costs shared among peers. Moreover, it unburdens the loaded peers in order to improve their streaming performance. After an extensive set of simulations and performance evaluations, we show that our proposed load balancing technique notably unloads the busy nodes, which ameliorates the 3D streaming in mobile environments in terms of fast processing and good resources utilization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".