ICN software tools: Survey and cross-comparison

Tortelli, Michele; Rossi, Dario; Boggia, Gennaro; Grieco, Alfredo

doi:10.1016/j.simpat.2016.01.015

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…To evaluate the accuracy of ModelGraft, we observe that simulators considered in [43] (ccnSim, NDNsim and Icarus, to mention a few) all yield consistent results: it follows that comparing ModelGraft against one of these simulators is sufficient to confirm ModelGraft validity. We thus select ccnSim -which was already shown to be among the most scalable ICN software tools [43]-and consider the largest scenario we can investigate via its event-driven engine.…”

Section: Modelgraft Validation: Very Large-scale Scenariomentioning

confidence: 92%

“…Before delving into ModelGraft's details, we analyze it in the context of the full framework made available as open-source through the latest version of ccnSim [2] (although the methodology is portable to other simulators [43]), and we illustrate each of its building blocks at high level. As illustrated in Fig.…”

Section: Modelgraft Components and Workflowmentioning

confidence: 99%

“…As far as cache networks are concerned, however, further approximations are required as an alternative approach to the computationally and algorithmically challenging characterization of the miss streams at any node in the network [29,32]. Approximations that often lead to a significant degradation of the accuracy with respect to simulation [43], especially in arbitrary networks with shortest path [39] or more complex routing policies [42], where estimation errors can potentially cascade. In addition, analytical approaches often assume stationary conditions, thus lacking in characterizing transient periods, although a model has been recently proposed only for a single cache [19].…”

Section: Introductionmentioning

confidence: 99%

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A hybrid methodology for the performance evaluation of Internet-scale cache networks

2017

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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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Section: Modelgraft Validation: Very Large-scale Scenariomentioning

confidence: 92%

Section: Modelgraft Components and Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid methodology for the performance evaluation of Internet-scale cache networks

2017

Self Cite

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“…As far as network of caches are concerned, however, further approximations are required as an alternative approach to the computationally and algorithmically challenging characterization of the miss stream at any node in the network [22,25]. In arbitrary networks with shortest path [32] or more complex routing policies [33], it has been shown that inaccuracies can potentially cascade, with significant degradation of the accuracy with respect to simulation [34]. Finally, analytical approaches often assume stationary conditions, thus lacking in characterizing transient periods, although a model has been recently proposed only for a single cache [11].…”

Section: A Backgroundmentioning

confidence: 99%

“…To assess ModelGraft accuracy, we consider the largest scenario we can investigate via event-driven simulation gathered via ccnSim, already shown to be among the most scalable ICN software tools [34]. To stretch the boundaries reachable by event-driven simulation, we integrate the rejection inversion sampling -to the best of our knowledge, this represents the first performance evaluation of ICN networks with content catalogs in the order of billions.…”

Section: A Very-large Scale Scenariomentioning

confidence: 99%

ModelGraft: Accurate, Scalable, and Flexible Performance Evaluation of General Cache Networks

Tortelli¹,

Rossi²,

Leonardi³

2016

2016 28th International Teletraffic Congress (ITC 28)

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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 in details all the interactions of complex replacement, replication, and routing policies on arbitrary topologies. On the other hand, the sheer size of networks 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 Internetscale scenarios with content catalogs comprising hundreds of billions objects.

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Ant colony optimization based Information‐Centric networking delivery strategy via flow analysis and scheduling

Chen

2020

Internet Technology Letters

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Information‐Centric Networking (ICN) has been deeply investigated in recent years since it shows the rich and wide application value in some emerging fields, such as Internet of Things (IoT) and mobile communication. Because of the feature of in‐network caching, there exists several content providers in ICN network. Under the situation, it is obvious that there are multiple paths from an interest requester to these content providers; that is to say, the routing of ICN aims to select a path from these paths, which guaranteeing the selected path is optimal according the requirements of users and network status. Especially during the phase of content delivery, the elephant flows usually cause the serious load unbalance and network congestion due to the unreasonable scheduling even though the proportion of elephant flows is very low. In this paper, the content delivery strategy of ICN is proposed with the load balance consideration, in which Ant Colony Optimization (ACO) is used to schedule the optimal path based on the dynamically analyzed network status, called ACOF. The simulation results show that ACOF efficiently improves delivery latency, bandwidth utilization and load balance over the simulated network topology.

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ICN software tools: Survey and cross-comparison

Cited by 17 publications

References 18 publications

A hybrid methodology for the performance evaluation of Internet-scale cache networks

A hybrid methodology for the performance evaluation of Internet-scale cache networks

ModelGraft: Accurate, Scalable, and Flexible Performance Evaluation of General Cache Networks

Ant colony optimization based Information‐Centric networking delivery strategy via flow analysis and scheduling

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