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Title: Prediction of the Domain Name System (DNS) Quality Attributes
Authors: Radwan, Marwan
Heckel, Reiko
First Published: 3-Apr-2017
Presented at: The 32nd ACM Symposium on Applied Computing Marrakech, Morocco
Start Date: 3-Apr-2017
End Date: 7-Apr-2017
Publisher: Association for Computing Machinery (ACM)
Citation: SAC '17 Proceedings of the Symposium on Applied Computing, 2017, pp. 578-585
Abstract: The Domain Name System (DNS) has a direct impact on the performance and dependability of nearly all aspects of interactions on the Internet. DNS relies on a delegation-based architecture, where resolution of a name to its IP address requires resolving the names of the servers responsible for that name. The graphs of the inter-dependencies that exist between name servers associated with each zone are called Dependency Graphs. We constructed a DNS Dependency Model as a unified representation of these Dependency Graphs. We utilize a set of Structural Metrics defined over this model as indicators of external quality attributes of the domain name system. We explore the inter-metric and inter-quality relations further in order to quantify the indicative power of each metric. We apply some machine learning algorithms in order to construct Prediction Models of the perceived quality attributes of the operational system out of the structural metrics of the model. Assessing these quality attributes at an early stage of the design/deployment enables us to avoid the implications of defective and low-quality designs and deployment choices and identify configuration changes that might improve the availability, security, stability and resiliency postures of the DNS.
DOI Link: 10.1145/3019612.3019728
ISBN: 9781450344869
Version: Post-print
Type: Conference Paper
Rights: Copyright © ACM 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the Symposium on Applied Computing,
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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