PolicyCop: An Autonomic QoS Policy Enforcement Framework for Software Defined Networks
Bibliographic record
Abstract
Network management is becoming increasingly challenging with the relentless growth in network size, traffic volume, and the diversity in QoS requirements. Traditionally, the concept of predefined Service Level Agreements (SLAs) has been utilized to establish QoS parameters. However, most state-of-the-art technologies in this area are both proprietary and inflexible. To this end, Software Defined Networking (SDN) has the potential to make network management tasks flexible, scalable, and an open platform to encourage innovation. In this paper, we present the design of PolicyCop, an open, flexible, and vendor agnostic QoS policy management framework targeted towards OpenFlow based SDN. PolicyCop provides an interface for specifying QoS SLAs and then exploits the control plane's API to enforce them. PolicyCop also monitors the network and autonomically readjusts network parameters to meet customer SLAs. We present experimental results to demonstrate PolicyCop's effectiveness in ensuring throughput, latency, and reliability guarantees.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".