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.

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The rapid growth of server virtualization has ignited a wide adoption of software-based virtual switches, with significant interest in speeding up their performance. In a similar trend, software-defined networking (SDN), with its strong reliance on rule-based flow classification, has also created renewed interest in multi-dimensional packet classification. However, despite these recent advances, the performance of current software-based packet classifiers is still limited, mostly by the low parallelism of general-purpose CPUs. In this paper, we explore how to accelerate packet classification using the high parallelism and latency-hiding capabilities of graphic processing units (GPUs). We implement GPU-accelerated versions for both linear and tuple search, currently deployed in virtual switches, and also introduce a novel algorithm called Bloom search. These algorithms are integrated with high-speed packet I/O to build GSwitch, a GPU-accelerated software switch. Our experimental evaluation shows that GSwitch is at least 7x faster than an equally-priced CPU classifier and is able to reach 10 Gbps with minimum-sized packets and a rule set containing 128K OpenFlow entries with 512 different wildcard patterns.