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What is Probe

Emerging Automation Techniques for the Future Internet
The source of synthetic test traffic in an active measurement.
Published in Chapter:
A Perspective on the Standardization of Autonomic Detection of Service Level Agreement Violations
Jéferson Campos Nobre (University of Vale do Rio dos Sinos, Brazil) and Lisandro Zambenedetti Granville (Federal University of Rio Grande do Sul, Brazil)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/978-1-5225-7146-9.ch011
Abstract
Service level agreements (SLAs) allow networked services established between providers and their customers to operate according to the conditions defined in the SLA. Measurement mechanisms can be used to support SLA monitoring. However, these mechanisms are expensive in terms of resource consumption. In addition, if the number of SLA violations at any given time is greater than the available measurement sessions, some violations will likely be missed. The current best practice is to observe just a subset of network destinations based upon the expertise of a few human administrators. Such observation mode is error prone, reactive, and scales poorly. Such practice can lead to SLA violations being missed, which hampers the reliability of the SLA monitoring process. In this context, the use of autonomic network features can improve such processes, especially when these features are deployed in a decentralized manner. The use of these autonomic features is described in RFC 8316. The authors expect that such a document can lead to better SLA monitoring tools and methods.
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More Results
The Affymetrix GeneChip® Microarray Platform
A probe is a fragment of DNA of 25 nucleotides/basepairs length, which is used to detect in RNA samples the presence of nucleotide sequences (the DNA target) that are complementary to the sequence in the probe. The probe thereby hybridizes to single-stranded nucleic acid (DNA or RNA) the base sequence of which allows probe-target base pairing due to complementarity between the probe and target.
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EM-Source Localization in Indoor Environments by Using an Artificial Neural Network Performance Assessment and Optimization
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