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Journal Articles Elsevier Computer Networks Year : 2017

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

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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.
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Dates and versions

hal-01613509 , version 1 (09-10-2017)

Identifiers

  • HAL Id : hal-01613509 , version 1

Cite

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⟩
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