Topics: Information and content centric network; Named data networking
Authors: Michele Tortelli and Dario Rossi (Telecom ParisTech, France); Emilio Leonardi (Politecnico di Torino, Italy)
Presenter bio:
Emilio is an Associate Professor at the Dipartimento di Elettronica
of Politecnico di Torino.
His research interests are in the field of:
performance evaluation of computer networks and distributed systems and queueing theory.
Abstract: Large scale deployments of general cache networks, such as Content
Delivery Networks or Information Centric Networking architectures, arise
new challenges regarding their performance evaluation for network
planning. On the one hand, analytical models can hardly represent all
the detailed interaction of complex replacement, replication, and
routing policies on arbitrary topologies. On the other hand, the sheer
size of network and content catalogs makes event-driven simulation
techniques inherently non-scalable. We propose a new technique for the
performance evaluation of large scale caching systems that intelligently
integrates elements of stochastic analysis within a MonteCarlo
simulative approach, that we colloquially refer to as ModelGraft. Our
approach (i) leverages the intuition that complex scenarios can be
mapped to a simpler equivalent scenario that builds upon Time-To-Live
(TTL) caches; it (ii) significantly downscales the scenario to lower
computation and memory complexity, while, at the same time, preserving
its properties to limit accuracy loss; finally, it (iii) is simple to
use and robust, as it autonomously converges to a consistent state
through a feedback-loop control system, regardless of the initial state.
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%. In
addition, we show that ModelGraft extends performance evaluation well
beyond the boundaries of classic approaches, by enabling study of
Internet scale scenarios with content catalogs comprising hundreds of
billions objects.
