"Running" ModelGraft to evaluate internet-scale ICN

<p>The analysis of Internet-scale Information-centric networks, and of cache networks in general, poses scalability issues like CPU and memory requirements, which can not be easily targeted by neither state-of-the-art analytical models nor well designed event-driven simulators. This demo focuses on showcasing performance of our new hybrid methodology, named ModelGraft, which we release as a simulation engine of the open-source ccnSim simulator: being able to seamlessly use a classic event-driven or the novel hybrid engine dramatically improves the flexibility and scalability of current simulative and analytical tools. In particular, ModelGraft combines elements and intuitions of stochastic analysis into a MonteCarlo simulative approach, offering a reduction of over two orders of magnitude in both CPU time and memory occupancy, with respect to the purely event-driven version of ccnSim, notably one of the most scalable simulators for Information-centric networks. This demo consists in gamifying the aforementioned comparison: we represent ModelGraft vs event-driven simulation as two athletes running a 100-meter competition using sprite-based animations. Differences between the two approaches in terms of CPU time, memory occupancy, and results accuracy, are highlighted in the score-board.</p

Parallel Simulation of Very Large-Scale General Cache Networks

In this paper we propose a methodology for the study of general cache networks, which is intrinsically scalable and amenable to parallel execution. We contrast two techniques: one that slices the network, and another that slices the content catalog. In the former, each core simulates requests for the whole catalog on a subgraph of the original topology, whereas in the latter each core simulates requests for a portion of the original catalog on a replica of the whole network. Interestingly, we find out that when the number of cores increases (and so the split ratio of the network topology), the overhead of message passing required to keeping consistency among nodes actually offsets any benefit from the parallelization: this is strictly due to the correlation among neighboring caches, meaning that requests arriving at one cache allocated on one core may depend on the status of one or more caches allocated on different cores. Even more interestingly, we find out that the newly proposed catalog slicing, on the contrary, achieves an ideal speedup in the number of cores. Overall, our system, which we make available as open source software, enables performance assessment of large scale general cache networks, i.e., comprising hundreds of nodes, trillions contents, and complex routing and caching algorithms, in minutes of CPU time and with exiguous amounts of memory

Running ModelGraft to Evaluate Internet-scale ICN

The analysis of Internet-scale Information-centric networks, and of cache networks in general, poses scalability issues like CPU and memory requirements, which can not be easily targeted by neither state-of-the-art analytical models nor well designed event-driven simulators. This demo focuses on showcasing performance of our new hybrid methodology, named ModelGraft, which we release as a simulation engine of the open-source ccnSim simulator: being able to seamlessly use a classic event-driven or the novel hybrid engine dramatically improves the flexibility and scalability of current simulative and analytical tools. In particular, ModelGraft combines elements and intuitions of stochastic analysis into a MonteCarlo simulative approach, offering a reduction of over two orders of magnitude in both CPU time and memory occupancy, with respect to the purely event-driven version of ccnSim, notably one of the most scalable simulators for Information-centric networks. This demo consists in gamifying the aforementioned comparison: we represent ModelGraft vs event-driven simulation as two athletes running a 100-meter competition using sprite-based animations. Differences between the two approaches in terms of CPU time, memory occupancy, and results accuracy, are highlighted in the score-board