Skip to Main content Skip to Navigation
Journal articles

A Hybrid Methodology for the Performance Evaluation of Internet-scale Cache Networks

Abstract : Two concurrent factors challenge the evaluation of large-scale cache networks: complex algorithmic interactions, which are hardly represented by analytical models, and catalog/network size, which limits the scalability of event-driven simulations. To solve these limitations, we propose a new hybrid technique, that we colloquially refer to as ModelGraft, which combines elements of stochastic analysis within a simulative Monte-Carlo approach. In ModelGraft, large scenarios are mapped to a downscaled counterpart built upon Time-To-Live (TTL) caches, to achieve CPU and memory scalability. Additionally, a feedback loop ensures convergence to a consistent state, whose performance accurately represent those of the original system. Finally, the technique also retains simulation simplicity and flexibility, as it can be seamlessly applied to numerous forwarding, meta-caching, and replacement algorithms. We implement and make ModelGraft available as an alternative simulation engine of ccnSim. Performance evaluation shows that, with respect to classic event-driven simulation, ModelGraft gains over two orders of magnitude in both CPU time and memory complexity, while limiting accuracy loss below 2%. Ultimately, ModelGraft pushes the boundaries of the performance evaluation well beyond the limits achieved in the current state of the art, enabling the study of Internet-scale scenarios with content catalogs comprising hundreds billions objects.
Document type :
Journal articles
Complete list of metadata
Contributor : Admin Télécom Paristech Connect in order to contact the contributor
Submitted on : Monday, October 9, 2017 - 4:42:29 PM
Last modification on : Tuesday, November 30, 2021 - 2:32:02 PM


  • HAL Id : hal-01613509, version 1


E. Leonardi, Dario Rossi, Michele Tortelli. A Hybrid Methodology for the Performance Evaluation of Internet-scale Cache Networks. Elsevier Computer Networks, 2017, 125, pp.146-159. ⟨hal-01613509⟩



Record views