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Record W2538147401 · doi:10.1109/sdn4fns.2013.6702548

PolicyCop: An Autonomic QoS Policy Enforcement Framework for Software Defined Networks

2013· article· en· W2538147401 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceQuality of serviceSoftware-defined networkingOpenFlowComputer networkNetwork managementScalabilityService levelForwarding planeThroughputDistributed computingOperating system

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.272
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations142
Published2013
Admission routes1
Has abstractyes

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