21,446 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
PKind: A parallel k-induction based model checker
PKind is a novel parallel k-induction-based model checker of invariant
properties for finite- or infinite-state Lustre programs. Its architecture,
which is strictly message-based, is designed to minimize synchronization delays
and easily accommodate the incorporation of incremental invariant generators to
enhance basic k-induction. We describe PKind's functionality and main features,
and present experimental evidence that PKind significantly speeds up the
verification of safety properties and, due to incremental invariant generation,
also considerably increases the number of provable ones.Comment: In Proceedings PDMC 2011, arXiv:1111.006
Towards Realizability Checking of Contracts using Theories
Virtual integration techniques focus on building architectural models of
systems that can be analyzed early in the design cycle to try to lower cost,
reduce risk, and improve quality of complex embedded systems. Given appropriate
architectural descriptions and compositional reasoning rules, these techniques
can be used to prove important safety properties about the architecture prior
to system construction. Such proofs build from "leaf-level" assume/guarantee
component contracts through architectural layers towards top-level safety
properties. The proofs are built upon the premise that each leaf-level
component contract is realizable; i.e., it is possible to construct a component
such that for any input allowed by the contract assumptions, there is some
output value that the component can produce that satisfies the contract
guarantees. Without engineering support it is all too easy to write leaf-level
components that can't be realized. Realizability checking for propositional
contracts has been well-studied for many years, both for component synthesis
and checking correctness of temporal logic requirements. However, checking
realizability for contracts involving infinite theories is still an open
problem. In this paper, we describe a new approach for checking realizability
of contracts involving theories and demonstrate its usefulness on several
examples.Comment: 15 pages, to appear in NASA Formal Methods (NFM) 201
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Theory-driven learning : using intra-example relationships to constrain learning
We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains
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