Viable system theory

Résumé

Viable system theory (VST) concerns cybernetic processes in relation to the development/evolution of dynamical systems. They are considered to be living systems in the sense that they are complex and adaptive, can learn, and are capable of maintaining an autonomous existence, at least within the confines of their constraints. These attributes involve the maintenance of internal stability through adaptation to changing environments. One can distinguish between two strands such theory: formal systems and principally non-formal system. Formal viable system theory is normally referred to as viability theory, and provides a mathematical approach to explore the dynamics of complex systems set within the context of control theory. In contrast, principally non-formal viable system theory is concerned with descriptive approaches to the study of viability through the processes of control and communication, though these theories may have mathematical descriptions associated with them. Histo

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https://en.wikipedia.org/wiki/Viable_system_theory

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The student will acquire the basis for the analysis of static structures and deformation of simple structural elements. The focus is given to problem-solving skills in the context of engineering design.

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Throughput Analysis of Large Networks

Serj Haddad

While wired infrastructure constitutes the backbone of most wireless networks, wireless systems appeal the most to the dynamic and rapidly evolving requirements of today's communication systems because of their ease of deployment and mobility, not to mention the high cost of building a wired infrastructure. This led to an increased interest in the so called wireless ad hoc networks formed of a group of users, known as nodes, capable of communicating with each other through a shared wireless channel. Needless to say, these nodes are asked to use the shared wireless medium in the most efficient fashion, which is not an easy task given the absence of wired backbone. This requires a profound understanding of the wireless medium to establish a decentralized cooperation scheme, if needed, that best utilizes the resources available in the wireless channel. A significant part of this thesis focuses on the properties of the shared wireless channel, whereby we are interested in studying the spatial diversity and the beamforming capabilities in large wireless networks which are crucial in analyzing the throughput of ad hoc networks. In this thesis, we mainly focus on the problem of broadcasting information in the most efficient manner in a large two-dimensional ad hoc wireless network at low SNR and under line-of-sight propagation. A new communication scheme, which we call multi-stage back-and-forth beamforming, is proposed, where source nodes first broadcast their data to the entire network, despite the lack of sufficient available power. The signal's power is then reinforced via successive back-and-forth beamforming transmissions between different groups of nodes in the network, so that all nodes are able to decode the transmitted information at the end. This scheme is shown to achieve asymptotically the broadcast capacity of the network, which is expressed in terms of the largest singular value of the matrix of fading coefficients between the nodes in the network. A detailed mathematical analysis is then presented to evaluate the asymptotic behavior of this largest singular value. We further characterize the maximum achievable broadcast rate under different sparsity regimes. Our result shows that this rate depends negatively on the sparsity of the network. This is to be put in contrast with the number of degrees of freedom available in the network, which have been shown previously to increase with the sparsity of the network. In this context, we further characterize the degrees of freedom versus beamforming gain tradeoff, which reveals that high beamforming gains can only be obtained at the expense of reduced spatial degrees of freedom. Another important factor that impacts the throughput in wireless networks is the transmit/receive capability of the transceiver at the nodes. Traditionally, wireless radios are half-duplex. However, building on self-interference cancellation techniques, full-duplex radios have emerged as a viable paradigm over the recent years. In the last part of this thesis, we ask the fundamental question: how much can full-duplex help? Intuitively, one may expect that full-duplex radios can at most double the capacity of wireless networks, since they enable nodes to transmit and receive at the same time. However, we show that the capacity gain can indeed be larger than a factor of 2; in particular, we construct a specific instance of a wireless network where the the full-duplex capacity is triple the half-duplex capacity.

EPFL2017

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Microsecond Consensus for Microsecond Applications

Naama Ben David, Rachid Guerraoui, Athanasios Xygkis, Mihail Igor Zablotchi

We consider the problem of making apps fault-tolerant through replication, when apps operate at the microsecond scale, as in finance, embedded computing, and microservices apps. These apps need a replication scheme that also operates at the microsecond scale, otherwise replication becomes a burden. We propose Mu, a system that takes less than 1.3 microseconds to replicate a (small) request in memory, and less than a millisecond to fail-over the system-this cuts the replication and fail-over latencies of the prior systems by at least 61% and 90%. Mu implements bona fide state machine replication/consensus (SMR) with strong consistency for a generic app, but it really shines on microsecond apps, where even the smallest overhead is significant. To provide this performance, Mu introduces a new SMR protocol that carefully leverages RDMA. Roughly, in Mu a leader replicates a request by simply writing it directly to the log of other replicas using RDMA, without any additional communication. Doing so, however, introduces the challenge of handling concurrent leaders, changing leaders, garbage collecting the logs, and more-challenges that we address in this paper through a judicious combination of RDMA permissions and distributed algorithmic design. We implemented Mu and used it to replicate several systems: a financial exchange app called Liquibook, Redis, Memcached, and HERD [33]. Our evaluation shows that Mu incurs a small replication latency, in some cases being the only viable replication system that incurs an acceptable overhead.

USENIX ASSOC2020

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Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints. Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.

Public Library of Science2014

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Throughput Analysis of Large Networks

Serj Haddad

EPFL2017