Efficient mobility and traffic management for delay tolerant cloud data in 5G networks
Bibliographic record
Abstract
The explosive growth of the demand for higher data rates in mobile networks have been mainly driven by the increasing use of cloud based applications by smartphones. This has led the industry to investigate new radio access technologies to be deployed as part of 5G networks, while providing mechanisms to manage user mobility and traffic in a more efficient manner. In this paper, we consider a mobility and traffic management mechanism that proposes a close interaction between the cloud data servers and the radio access network to enable efficient network operation. Such a management mechanism is enabled by utilizing the application-dependent delay tolerance properties of the cloud data, with the delay values conveyed to the radio access network and UE to manage the service requests for the cloud data. The mechanism was evaluated using LTE-Advanced heterogeneous network scenario and 5G dense-urban information society scenario from EU FP7 METIS project, and relative gains in terms of packet delays and throughput values are presented. The results indicate significant gains using the proposed management mechanism as compared to the reference case where no such enhancements are used.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".