Michele Tortelli

COBRA: Lean intra-domain routing in NDN

Named Data Networking (NDN) is an emerging Information Centric Networking architecture based on hierarchical content names, in-network caching mechanisms, receiver-driven operations, and content-level security schema. NDN networking primitives and routing are based on content names and therefore efficient content discovery of permanent as well as temporarily available cached copies is a key problem to address. This paper examines current NDN approaches and proposes a fully distributed, content-driven, bloom filter-based intra-domain routing algorithm (COBRA), which outperforms previous solutions in this area. COBRA creates routes based on paths used previously for content retrieval, and maintains routing information up-to-date without the need for extensive signaling between nodes. We evaluate COBRA using simulation and compare its performance with other established routing strategies over the European research network GEANT topology as an example of a ndnSIM core network. Our results illustrate that COBRA can significantly reduce overhead with respect to flood-based routing while ensuring hit distances of the same order as when using Dijkstras algorithm.

A fairness analysis of content centric networks

In recent years the new vision of data-centric networks has emerged as a natural way to satisfy user needs in the Future Internet. It is based on novel architectures oriented towards data sharing and delivering as opposite to classic host-to-host communications. Among several proposals, Content Centric Networking has been conceived as a very promising solution by the Palo Alto Research Center team. It can be gradually deployed over current IP networks and solves different problems, as NAT Traversal, depletion of IP addresses, security, mobility, and multicast communications. In this work an analytical framework for investigating properties of Content Centric Network is proposed, with a particular focus on fairness in cache usage. It captures the distribution of content replicas among nodes of the network by taking into account contents availability, data popularity, topology information, and cache size. Preliminary results, validated using numerical simulations, shed some light on the underlying fairness of a Content Centric Network, thus helping future developments and upgrades on the current architecture.

ICN software tools: survey and cross-comparison

Research interest on Information Centric Networking (ICN) has been sharply growing. Although new architectures, algorithms, and analytical models have been proposed, their evaluation remains often isolated and not rigorously verified by the research community. This paper initially portrays the composition of open source software tools available for ICN, certifying the predominance of Content Centric Networking (CCN)/Named Data Networking (NDN) simulators. Then, inspired by similar works related to the P2P field, it surveys related research papers to qualify the ICN literature produced so far, finding that a large fraction of contributions either uses custom, proprietary, and unavailable software, or even plainly fails to mention any information in this regard. By adopting a rigorous methodology, in the second part of the paper four simulators, namely ndnSIM, ccnSim, CCNPL-Sim, and Icarus, are cross-compared under several key aspects. Our findings confirm both their accuracy with respect to reference theoretical models in simple settings, and their consistency in more complex scenario. Additionally, our analysis can both assist researchers in the choice of the tool that best fits their needs, and provide guidelines to avoid common pitfalls in the ICN performance evaluation.

CCN forwarding engine based on Bloom filters

The lack of scalable routing algorithms is one of the main obstacles that slow down a large deployment of Content Centric Networking on an Internet-scale. From one side, content based networking promises to solve the current problems of the Internet. On the other hand, instead, it requires routers to account for a very huge amount of content names. Bloom Filters are widely recognized as a possible solution to this limitation. At the same time, their adoption requires careful tuning rules and novel design methodologies. In this perspective, the present contribution proposes a Bloom Filter-based routing scheme for Content Centric Networking (CCN) and shows several preliminary observations about Bloom Filters size and signaling overhead.

Pedestrian crossing: The long and winding road toward fair cross-comparison of ICN quality

While numerous Information Centric Networking (ICN) architectures have been proposed over the last years, the community has so far only timidly attempted at a quantitative assessment of the relative quality of service level that users are expected to enjoy in each of them. This paper starts a journey toward the cross comparison of ICN alternatives, making several contributions along this road. Specifically, a census of 20 ICN software tools reveals that about 10 are dedicated to a specific architecture, about half of which are simulators. Second, we survey ICN research papers using simulation to gather information concerning the used simulator, finding that a large fraction either uses custom proprietary and unavailable software, or even plainly fails to mention any information on this regard, which is deceiving. Third, we cross-compare some of the available simulators, finding that they achieve consistent results, which is instead encouraging. Fourth, we propose a methodology to increase and promote cross-comparison, which is within reach but requires community-wide agreement, promotion and enforcement.

Dynamic Adaptive Video Streaming: Towards a Systematic Comparison of ICN and TCP/IP

Streaming of video content over the Internet is experiencing an unprecedented growth. While video permeates every application, it also puts tremendous pressure in the network—to support users having heterogeneous accesses and expecting a high quality of experience, in a furthermore cost-effective manner. In this context, future internet paradigms, such as information centric networking (ICN), are particularly well suited to not only enhance video delivery at the client (as in the dynamic adaptive streaming over HTTP (DASH) approach), but to also naturally and seamlessly extend video support deeper in the network functions. In this paper, we contrast ICN and transmission control protocol/internet protocol (TCP/IP) with an experimental approach, where we employ several state-of-the-art DASH controllers (PANDA, AdapTech, and BOLA) on an ICN versus TCP/IP network stack. Our campaign, based on tools that we developed and made available as open-source software, includes multiple clients (homogeneous vesrus heterogeneous mixture and synchronous vesrus asynchronous arrivals), videos (up to 4k resolution), channels (e.g., DASH profiles, emulated WiFi and LTE, and real 3G/4G traces), and levels of integration with an ICN network (i.e., vanilla named data networking (NDN), wireless loss detection and recovery at the access point, and load balancing). Our results clearly illustrate, as well as quantitatively assess, the benefits of ICN-based streaming, warning about potential pitfalls that are however easy to avoid.

CCN simulators: analysis and cross-comparison

This demo focuses on the cross-comparison of CCN simulators available as open source software. The aim is to start a quantitative evaluation of the accuracy, coherence, as well as scalability of software tools available for CCN, in order to understand their boundaries and check if they achieve consistent results. The demo process consists of showing results produced by the tracing systems of each simulator using an interactive parallel coordinate graph, which allows different metrics to be shown at the same time. Both the consistency of simulation results and the differences between several combinations of forwarding strategies, cache replacements policies, and network settings can be verified by users that can interact by proposing and reproducing their own scenario in more than one simulator.

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

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 Internet-scale scenarios with content catalogs comprising hundreds of billions objects.

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 MonteCarlo 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 represents 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 of billions objects.

A simple yet effective network-assisted signal for enhanced DASH quality of experience

We propose and evaluate simple signals coming from in-network telemetry that are effective to enhance the quality of DASH streaming. Specifically, in-network caching is known to positively affect DASH streaming quality but at the same time negatively affect the controller stability, increasing the quality switch ratio. Our contributions are to first (i) consider the broad spectrum of interaction between the network and the application, and then (ii) to devise how to effectively exploit in a DASH controller a very simple signal (i.e., per-quality hit ratio) that can be exported by framework such as Server and Network Assisted DASH (SAND) at fairly low rate (i.e., a timescale of 10s of seconds). Our thorough experimental campaign confirms the soundness of the approach (that significantly ameliorate performance with respect to network-blind DASH), as well as its robustness (i.e., tuning is not critical) and practical appeal (i.e., due to its simplicity and compatibility with SAND).