Common sense suggests that networks are not random mazes of purposeless connections, but that these connections are organized so that networks can perform their functions well. One function common to many networks is targeted transport or navigation. Here, using game theory, we show that minimalistic networks designed to maximize the navigation efficiency at minimal cost share basic structural properties with real networks. These idealistic networks are Nash equilibria of a network construction game whose purpose is to find an optimal trade-off between the network cost and navigability. We show that these skeletons are present in the Internet, metabolic, English word, US airport, Hungarian road networks, and in a structural network of the human brain. The knowledge of these skeletons allows one to identify the minimal number of edges, by altering which one can efficiently improve or paralyse navigation in the network.
OpenFlow is an amazingly expressive dataplane programming language, but this expressiveness comes at a severe performance price as switches must do excessive packet classification in the fast path. The prevalent OpenFlow software switch architecture is therefore built on flow caching, but this imposes intricate limitations on the workloads that can be supported efficiently and may even open the door to malicious cache overflow attacks. In this paper we argue that instead of enforcing the same universal flow cache semantics to all OpenFlow applications and optimize for the common case, a switch should rather automatically specialize its dataplane piecemeal with respect to the configured workload. We introduce ESWITCH, a novel switch architecture that uses on-the-fly template-based code generation to compile any OpenFlow pipeline into efficient machine code, which can then be readily used as fast path. We present a proofof-concept prototype and we demonstrate on illustrative use cases that ESWITCH yields a simpler architecture, superior packet processing speed, improved latency and CPU scalability, and predictable performance. Our prototype can easily scale beyond 100 Gbps on a single Intel blade even with complex OpenFlow pipelines.
Lately, there has been an upsurge of interest in compressed data structures, aiming to pack ever larger quantities of information into constrained memory without sacrificing the efficiency of standard operations, like random access, search, or update. The main goal of this paper is to demonstrate how data compression can benefit the networking community by showing how to squeeze the IP Forwarding Information Base (FIB), the giant table consulted by IP routers to make forwarding decisions, into information-theoretical entropy bounds, with essentially zero cost on longest prefix match and FIB update. First, we adopt the state of the art in compressed data structures, yielding a static entropy-compressed FIB representation with asymptotically optimal lookup. Then, we redesign the venerable prefix tree, used commonly for IP lookup for at least 20 years in IP routers, to also admit entropy bounds and support lookup in optimal time and update in nearly optimal time. Evaluations on a Linux kernel prototype indicate that our compressors encode an FIB comprising more than 440 K prefixes to just about 100-400 kB of memory, with a threefold increase in lookup throughput and no penalty on FIB updates.Index Terms-Data compression, IP forwarding table lookup, prefix tree.
The ongoing network softwarization trend holds the promise to revolutionize network infrastructures by making them more flexible, reconfigurable, portable, and more adaptive than ever. Still, the migration from hard-coded/hardwired network functions towards their software-programmable counterparts comes along with the need for tailored optimizations and acceleration techniques, so as to avoid, or at least mitigate, the throughput/latency performance degradation with respect to fixed function network elements. The contribution of this article is twofold. First, we provide a comprehensive overview of the host-based Network Function Virtualization (NFV) ecosystem, covering a broad range of techniques, from low level hardware acceleration and bump-in-the-wire offloading approaches, to highlevel software acceleration solutions, including the virtualization technique itself. Second, we derive guidelines regarding the design, development, and operation of NFV-based deployments that meet the flexibility and scalability requirements of modern communication networks.
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