Resource optimization of TCAM-based SDN measurements via diminishing-points autodetection
Bibliographic record
Abstract
Network measurement is an important tool for network managers and operators since it provides the information needed to carry out different management tasks. However, because of the rapid increase in data link speeds and the volume of traffic carried by modern networks, the availability of system resources dedicated to network measurement has always been the main limiting factor in developing modern measurement solutions. The emergence of Software Defined Networks in recent years has inspired the development of new measurement solutions that takes advantage of the capabilities offered by this new paradigm such as programmability and central management [9]. Current SDN enabled measurement solutions are able to orchestrate the execution and resources allocation of network-wide measurement tasks but still falls short in their ability to recognize efficient operating points (amount of allocated resources) for running tasks and often results in inefficient resources utilization. In this paper we provide a novel resource allocation method that continuously estimates the resources-accuracy relationships for running tasks, infers their individual points of diminishing-returns and uses the resulting value of each task as it's target point of operation in order to achieve a more efficient resource utilization. In contrast to existing work, our proposed method maximizes the return on used system resources and results in lower drop rates of running tasks as shown by our simulations.
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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 | 0.000 | 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 | 0.000 | 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 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".