With current efforts to design Future Internet Architectures (FIAs), the evaluation and comparison of different proposals is an interesting research challenge. Previously, metrics such as bandwidth or latency have commonly been used to compare FIAs to IP networks. We suggest the use of power consumption as a metric to compare FIAs. While low power consumption is an important goal in its own right (as lower energy use translates to smaller environmental impact as well as lower operating costs), power consumption can also serve as a proxy for other metrics such as bandwidth and processor load. Lacking power consumption statistics about either commodity FIA routers or widely deployed FIA testbeds, we propose models for power consumption of FIA routers. Based on our models, we simulate scenarios for measuring power consumption of content delivery in different FIAs. Specifically, we address two questions: 1) which of the proposed FIA candidates achieves the lowest energy footprint; and 2) which set of design choices yields a power-efficient network architecture? Although the lack of real-world data makes numerous assumptions necessary for our analysis, we explore the uncertainty of our calculations through sensitivity analysis of input parameters.
Content-Centric Networking (CCN) is a novel networking paradigm centered around content distribution rather than host-to-host connectivity. This change from host-centric to content-centric has several attractive advantages, such as network load reduction, low dissemination latency, and energy efficiency. However, it is unclear whether today's technology is ready for the CCN (r)evolution. The major contribution of this paper is a systematic evaluation of the suitability of existing software and hardware components in today's routers for the support of CCN. Our main conclusion is that a CCN deployment is feasible at a Content Distribution Network (CDN) and ISP scale, whereas today's technology is not yet ready to support an Internet scale deployment. Information-Centric Networking (ICN) has seen a significant resurgence in recent years. ICN promises benefits to users and service providers along several dimensions (e.g., performance, security, and mobility). These benefits, however, come at a non-trivial cost as many ICN proposals envision adding significant complexity to the network by having routers serve as content caches and support nearest-replica routing. This paper is driven by the simple question of whether this additional complexity is justified and if we can achieve these benefits in an incrementally deployable fashion. To this end, we use trace-driven simulations to analyze the quantitative benefits attributed to ICN (e.g., lower latency and congestion). Somewhat surprisingly, we find that pervasive caching and nearest-replica routing are not fundamentally necessary---most of the performance benefits can be achieved with simpler caching architectures. We also discuss how the qualitative benefits of ICN (e.g., security, mobility) can be achieved without any changes to the network. Building on these insights, we present a proof-of-concept design of an incrementally deployable ICN architecture.  Internet usage has dramatically evolved towards content dissemination and retrieval, whilst the underlying infrastructure remains tied up to hosts interconnection. Information centric networking (ICN) proposals have recently emerged to rethink Internet foundations and design a natively content-centric network environment. Important features of such networks are the availability of built-in network storage and of receiver-driven chunk-level transport, whose interaction significantly impacts overall system and user performance. In the paper, we provide an analytical characterization of statistical bandwidth and storage sharing, under fairly general assumption on total demand, topology, content popularity and limited network resources. A closed-form expression for average content delivery time is derived and its accuracy confirmed by event-driven simulations. Finally, we present some applications of our model, leveraging on explicit formulae for the optimal dimensioning and localization of storage resources.