Abstract
Content-Centric Networking (CCN) research addresses the mismatch between the modern usage of the Internet and its outdated architecture. Importantly, CCN routers use various caching strategies to locally cache content frequently requested by end users. However, it is unclear which content shall be stored and when it should be replaced. In this work, we employ novel techniques towards intelligent administration of CCN routers. Our approach allows for autonomous switching between existing strategies in response to changing content request patterns using rule-based stream reasoning framework LARS which extends Answer Set Programming for streams. The obtained possibility for flexible router configuration at runtime allows for faster experimentation and may result in significant performance gains, as shown in our evaluation.
This work was partly funded by the Austrian Science Fund (FWF) under the CHIST-ERA project CONCERT (A Context-Adaptive Content Ecosystem Under Uncertainty), project number I1402, as well as projects P26471 and W1255-N23.
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Beck, H., Bierbaumer, B., Dao-Tran, M., Eiter, T., Hellwagner, H., Schekotihin, K. (2016). Rule-based Stream Reasoning for Intelligent Administration of Content-Centric Networks. In: Michael, L., Kakas, A. (eds) Logics in Artificial Intelligence. JELIA 2016. Lecture Notes in Computer Science(), vol 10021. Springer, Cham. https://doi.org/10.1007/978-3-319-48758-8_34
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