Attila Kőrösi

Navigable networks as Nash equilibria of navigation games

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.

Dataplane Specialization for High-performance OpenFlow Software Switching

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 proof-of-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.

Geometric explanation of the rich-club phenomenon in complex networks

The rich club organization (the presence of highly connected hub core in a network) influences many structural and functional characteristics of networks including topology, the efficiency of paths and distribution of load. Despite its major role, the literature contains only a very limited set of models capable of generating networks with realistic rich club structure. One possible reason is that the rich club organization is a divisive property among complex networks which exhibit great diversity, in contrast to other metrics (e.g. diameter, clustering or degree distribution) which seem to behave very similarly across many networks. Here we propose a simple yet powerful geometry-based growing model which can generate realistic complex networks with high rich club diversity by controlling a single geometric parameter. The growing model is validated against the Internet, protein-protein interaction, airport and power grid networks.

Compressing IP forwarding tables: towards entropy bounds and beyond

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.

Routes Obey Hierarchy in Complex Networks

The last two decades of network science have discovered stunning similarities in the topological characteristics of real life networks (many biological, social, transportation and organizational networks) on a strong empirical basis. However our knowledge about the operational paths used in these networks is very limited, which prohibits the proper understanding of the principles of their functioning. Today, the most widely adopted hypothesis about the structure of the operational paths is the shortest path assumption. Here we present a striking result that the paths in various networks are significantly stretched compared to their shortest counterparts. Stretch distributions are also found to be extremely similar. This phenomenon is empirically confirmed on four networks from diverse areas of life. We also identify the high-level path selection rules nature seems to use when picking its paths.

Diversity Coding-Based Survivable Routing with QoS and Differential Delay Bounds

Abstract Survivable routing with instantaneous recovery gained much attention in the last decade, as in optical backbone networks even the shortest disruption of a connection may cause tremendous loss of data. Recently, strict delay requirements emerge with the growing volume of multimedia and video streaming applications, which have to be ensured both before and after a failure. Diversity coding provides a nice trade-off between the simplicity of dedicated protection and bandwidth-efficiency of network coding to ensure instantaneous recovery for the connections. Hence, in this paper we thoroughly investigate the optimal structure of diversity coding-based survivable routing, which has a well-defined acyclic structure of subsequent paths and disjoint path-pairs between the communication end-points. We define the delay of these directed acyclic graphs, and investigate the effect of Quality-of-Service and differential delay bounds on the solution cost. Complexity analysis and integer linear programs are provided to solve these delay aware survivable routing problems. We discuss their approximability and provide some heuristic algorithms, too. Thorough experiments are conducted to demonstrate the benefits of diversity coding on randomly generated and real-world optical topologies.

On the Computational Complexity of Policy Routing

With the advent of new network architectures, like Software Defined Networks, the rules governing the way traffic is routed through the network are becoming increasingly complex. In this paper, we revisit the theoretic underpinnings of policy routing in the light of the new requirements. We show that certain simple but plausible algebraic properties already induce intractable path selection instances, and we extend the algebraic description of policies for which the related path selection problem is guaranteed to be tractable with a new class, called polynomial finite algebras, which captures many real-life application domains.

Modeling the Content Popularity Evolution in Video-on-Demand Systems

The simulation and testing of Video-on-Demand (VoD) services require the generation of realistic content request patterns to emulate a virtual user base. The efficiency of these services depend on the popularity distribution of the video library, thus the traffic generators have to mimic the statistical properties of real life video requests. In this paper the connection among the content popularity descriptors of a generic VoD service is investigated. We provide an analytical model for the relationships among the most important popularity descriptors, such as the ordered long term popularity of the whole video library, the popularity evolutions and the initial popularity of the individual contents. Beyond the theoretical interest, our method provides a simple way of generating realistic request patterns for simulating or testing media servers.

A dataset on human navigation strategies in foreign networked systems

Humans are involved in various real-life networked systems. The most obvious examples are social and collaboration networks but the language and the related mental lexicon they use, or the physical map of their territory can also be interpreted as networks. How do they find paths between endpoints in these networks? How do they obtain information about a foreign networked world they find themselves in, how they build mental model for it and how well they succeed in using it? Large, open datasets allowing the exploration of such questions are hard to find. Here we report a dataset collected by a smartphone application, in which players navigate between fixed length source and destination English words step-by-step by changing only one letter at a time. The paths reflect how the players master their navigation skills in such a foreign networked world. The dataset can be used in the study of human mental models for the world around us, or in a broader scope to investigate the navigation strategies in complex networked systems.

Optimal resource pooling over legacy equal-split load balancing schemes

Abstract Splitting traffic flows to different data paths is crucial in current and future networks. Traffic division serves as the basis for load balancing between application servers, optimal Traffic Engineering, using multiple paths in data centers, and several other places of an end-to-end connection. Unfortunately, by allowing only equal division amongst the parallel resources, existing technologies often cannot realize the optimal traffic splitting, which can have serious negative consequences on the network performance. In this paper we present a flexible and effective traffic splitting method that is incrementally deployable and fully compatible with practically all existing protocols and data planes. Our proposal, called Virtual Resource Allocation (VRA), is based on setting up virtual resources alongside existing ones, thereby tricking the legacy equal traffic splitting technology into realizing the required non-equal traffic division over the physical media. We propose several VRA schemes, give theoretical bounds on their performance, and also show that the full-fledged VRA problem is NP-complete in general. Accordingly, we provide solution algorithms, including an optimal, but necessarily slow method and several quick heuristics. Our simulations show that VRA has huge practical potential as it allows approaching an ideal traffic split using only a very limited set of virtual resources. Based on the results, we also give detailed suggestions on which algorithm to apply in different scenarios.