15,506 research outputs found
Efficient Synthesis of Network Updates
Software-defined networking (SDN) is revolutionizing the networking industry,
but current SDN programming platforms do not provide automated mechanisms for
updating global configurations on the fly. Implementing updates by hand is
challenging for SDN programmers because networks are distributed systems with
hundreds or thousands of interacting nodes. Even if initial and final
configurations are correct, naively updating individual nodes can lead to
incorrect transient behaviors, including loops, black holes, and access control
violations. This paper presents an approach for automatically synthesizing
updates that are guaranteed to preserve specified properties. We formalize
network updates as a distributed programming problem and develop a synthesis
algorithm based on counterexample-guided search and incremental model checking.
We describe a prototype implementation, and present results from experiments on
real-world topologies and properties demonstrating that our tool scales to
updates involving over one-thousand nodes
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both
academic and industry research laboratories. Graph Neural Networks (GNN) have
been widely used in relational and symbolic domains, with widespread
application of GNNs in combinatorial optimization, constraint satisfaction,
relational reasoning and other scientific domains. The need for improved
explainability, interpretability and trust of AI systems in general demands
principled methodologies, as suggested by neural-symbolic computing. In this
paper, we review the state-of-the-art on the use of GNNs as a model of
neural-symbolic computing. This includes the application of GNNs in several
domains as well as its relationship to current developments in neural-symbolic
computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape
Using SPIN to Analyse the Tree Identification Phase of the IEEE 1394 High-Performance Serial Bus(FireWire)Protocol
We describe how the tree identification phase of the IEEE 1394 high-performance serial bus (FireWire) protocol is modelled in Promela and verified using SPIN. The verification of arbitrary system configurations is discussed
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
On Compact Routing for the Internet
While there exist compact routing schemes designed for grids, trees, and
Internet-like topologies that offer routing tables of sizes that scale
logarithmically with the network size, we demonstrate in this paper that in
view of recent results in compact routing research, such logarithmic scaling on
Internet-like topologies is fundamentally impossible in the presence of
topology dynamics or topology-independent (flat) addressing. We use analytic
arguments to show that the number of routing control messages per topology
change cannot scale better than linearly on Internet-like topologies. We also
employ simulations to confirm that logarithmic routing table size scaling gets
broken by topology-independent addressing, a cornerstone of popular
locator-identifier split proposals aiming at improving routing scaling in the
presence of network topology dynamics or host mobility. These pessimistic
findings lead us to the conclusion that a fundamental re-examination of
assumptions behind routing models and abstractions is needed in order to find a
routing architecture that would be able to scale ``indefinitely.''Comment: This is a significantly revised, journal version of cs/050802
Statistical thermodynamics for choice models on graphs
Formalism based on equilibrium statistical thermodynamics is applied to
communication networks of decision making individuals. It is shown that in
statistical ensembles for choice models, properly defined disutility can play
the same role as energy in statistical mechanics. We demonstrate additivity and
extensivity of disutility and build three types of equilibrium statistical
ensembles: the canonical, the grand canonical and the super-canonical. Using
Boltzmann-like probability measure one reproduce the logit choice model. We
also propose using q-distributions for temperature evolution of moments of
stochastic variables. The formalism is applied to three network topologies of
different degrees of symmetry, for which in many cases analytic results are
obtained and numerical simulations are performed for all of them. Possible
applications of the model to airline networks and its usefulness for practical
support of economic decisions is pointed out.Comment: 17 pages, 13 figure
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