230 research outputs found
Hybrid system identification using switching density networks
Behaviour cloning is a commonly used strategy for imitation learning and can
be extremely effective in constrained domains. However, in cases where the
dynamics of an environment may be state dependent and varying, behaviour
cloning places a burden on model capacity and the number of demonstrations
required. This paper introduces switching density networks, which rely on a
categorical reparametrisation for hybrid system identification. This results in
a network comprising a classification layer that is followed by a regression
layer. We use switching density networks to predict the parameters of hybrid
control laws, which are toggled by a switching layer to produce different
controller outputs, when conditioned on an input state. This work shows how
switching density networks can be used for hybrid system identification in a
variety of tasks, successfully identifying the key joint angle goals that make
up manipulation tasks, while simultaneously learning image-based goal
classifiers and regression networks that predict joint angles from images. We
also show that they can cluster the phase space of an inverted pendulum,
identifying the balance, spin and pump controllers required to solve this task.
Switching density networks can be difficult to train, but we introduce a cross
entropy regularisation loss that stabilises training
A Fast Compiler for NetKAT
High-level programming languages play a key role in a growing number of
networking platforms, streamlining application development and enabling precise
formal reasoning about network behavior. Unfortunately, current compilers only
handle "local" programs that specify behavior in terms of hop-by-hop forwarding
behavior, or modest extensions such as simple paths. To encode richer "global"
behaviors, programmers must add extra state -- something that is tricky to get
right and makes programs harder to write and maintain. Making matters worse,
existing compilers can take tens of minutes to generate the forwarding state
for the network, even on relatively small inputs. This forces programmers to
waste time working around performance issues or even revert to using
hardware-level APIs.
This paper presents a new compiler for the NetKAT language that handles rich
features including regular paths and virtual networks, and yet is several
orders of magnitude faster than previous compilers. The compiler uses symbolic
automata to calculate the extra state needed to implement "global" programs,
and an intermediate representation based on binary decision diagrams to
dramatically improve performance. We describe the design and implementation of
three essential compiler stages: from virtual programs (which specify behavior
in terms of virtual topologies) to global programs (which specify network-wide
behavior in terms of physical topologies), from global programs to local
programs (which specify behavior in terms of single-switch behavior), and from
local programs to hardware-level forwarding tables. We present results from
experiments on real-world benchmarks that quantify performance in terms of
compilation time and forwarding table size
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
Verificare: a platform for composable verification with application to SDN-Enabled systems
Software-Defined Networking (SDN) has become increasing prevalent
in both the academic and industrial communities. A new class of system built on
SDNs, which we refer to as SDN-Enabled, provide programmatic interfaces between
the SDN controller and the larger distributed system. Existing tools for SDN
verification and analysis are insufficiently expressive to capture
this composition of a network and a larger distributed system. Generic
verification systems are an infeasible solution, due to their monolithic
approach to modeling and rapid state-space explosion.
In this thesis we present a new compositional approach to system modeling and
verification that is particularly appropriate for SDN-Enabled systems.
Compositional models may have sub-components (such as switches and
end-hosts) modified, added, or removed with only minimal, isolated changes.
Furthermore, invariants may be defined over the composed system that restrict
its behavior, allowing assumptions to be added or removed and for components to
be abstracted away into the service guarantee that they provide (such as
guaranteed packet arrival). Finally, compositional modeling can minimize the
size of the state space to be verified by taking advantage of known model
structure.
We also present the Verificare platform, a tool chain for building
compositional models in our modeling language and automatically compiling them
to multiple off-the-shelf verification tools. The compiler outputs a minimal,
calculus-oblivious formalism, which is accessed by plugins via a translation
API. This enables a wide variety of requirements to be
verified. As new tools become available, the translator can easily be extended
with plugins to support them
Abstract Interpretation of Stateful Networks
Modern networks achieve robustness and scalability by maintaining states on
their nodes. These nodes are referred to as middleboxes and are essential for
network functionality. However, the presence of middleboxes drastically
complicates the task of network verification. Previous work showed that the
problem is undecidable in general and EXPSPACE-complete when abstracting away
the order of packet arrival.
We describe a new algorithm for conservatively checking isolation properties
of stateful networks. The asymptotic complexity of the algorithm is polynomial
in the size of the network, albeit being exponential in the maximal number of
queries of the local state that a middlebox can do, which is often small.
Our algorithm is sound, i.e., it can never miss a violation of safety but may
fail to verify some properties. The algorithm performs on-the fly abstract
interpretation by (1) abstracting away the order of packet processing and the
number of times each packet arrives, (2) abstracting away correlations between
states of different middleboxes and channel contents, and (3) representing
middlebox states by their effect on each packet separately, rather than taking
into account the entire state space. We show that the abstractions do not lose
precision when middleboxes may reset in any state. This is encouraging since
many real middleboxes reset, e.g., after some session timeout is reached or due
to hardware failure
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