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Hybrid numerical solution of the chemical master equation
Get PDFWe present a numerical approximation technique for the analysis of continuous-time Markov chains that describe net- works of biochemical reactions and play an important role in the stochastic modeling of biological systems. Our approach is based on the construction of a stochastic hybrid model in which certain discrete random variables of the original Markov chain are approximated by continuous deterministic variables. We compute the solution of the stochastic hybrid model using a numerical algorithm that discretizes time and in each step performs a mutual update of the transient prob- ability distribution of the discrete stochastic variables and the values of the continuous deterministic variables. We im- plemented the algorithm and we demonstrate its usefulness and efficiency on several case studies from systems biology
A marketplace for cloud resources
Get PDFCloud computing is an emerging paradigm aimed to offer users pay-per-use computing resources, while leaving the burden of managing the computing infrastructure to the cloud provider. We present a new programming and pricing model that gives the cloud user the flexibility of trading execution speed and price on a per-job basis. We discuss the scheduling and resource management challenges for the cloud provider that arise in the implementation of this model. We argue that techniques from real-time and embedded software can be useful in this context
Robustness in the presence of liveness
Get PDFSystems ought to behave reasonably even in circumstances that are not anticipated in their specifications. We propose a definition of robustness for liveness specifications which prescribes, for any number of environment assumptions that are violated, a minimal number of system guarantees that must still be fulfilled. This notion of robustness can be formulated and realized using a Generalized Reactivity formula. We present an algorithm for synthesizing robust systems from such formulas. For the important special case of Generalized Reactivity formulas of rank 1, our algorithm improves the complexity of [PPS06] for large specifications with a small number of assumptions and guarantees
Mean-payoff automaton expressions
Get PDFQuantitative languages are an extension of boolean languages that assign to each word a real number. Mean-payoff automata are finite automata with numerical weights on transitions that assign to each infinite path the long-run average of the transition weights. When the mode of branching of the automaton is deterministic, nondeterministic, or alternating, the corresponding class of quantitative languages is not robust as it is not closed under the pointwise operations of max, min, sum, and numerical complement. Nondeterministic and alternating mean-payoff automata are not decidable either, as the quantitative generalization of the problems of universality and language inclusion is undecidable.
We introduce a new class of quantitative languages, defined by mean-payoff automaton expressions, which is robust and decidable: it is closed under the four pointwise operations, and we show that all decision problems are decidable for this class. Mean-payoff automaton expressions subsume deterministic meanpayoff automata, and we show that they have expressive power incomparable to nondeterministic and alternating mean-payoff automata. We also present for the first time an algorithm to compute distance between two quantitative languages, and in our case the quantitative languages are given as mean-payoff automaton expressions
Aligators for arrays
Get PDFThis paper presents Aligators, a tool for the generation of universally quantified array invariants. Aligators leverages recurrence solving and algebraic techniques to carry out inductive reasoning over array content. The Aligators’ loop extraction module allows treatment of multi-path loops by exploiting their commutativity and serializability properties. Our experience in applying Aligators on a collection of loops from open source software projects indicates the applicability of recurrence and algebraic solving techniques for reasoning about arrays
Model checking transactional memories
Get PDFModel checking transactional memories (TMs) is difficult because of the unbounded number, length, and delay of concurrent transactions, as well as the unbounded size of the memory. We show that, under certain conditions satisfied by most TMs we know of, the model checking problem can be reduced to a finite-state problem, and we illustrate the use of the method by proving the correctness of several TMs, including two-phase locking, DSTM, and TL2. The safety properties we consider include strict serializability and opacity; the liveness properties include obstruction freedom, livelock freedom, and wait freedom. Our main contribution lies in the structure of the proofs, which are largely automated and not restricted to the TMs mentioned above. In a first step we show that every TM that enjoys certain structural properties either violates a requirement on some program with two threads and two shared variables, or satisfies the requirement on all programs. In the second step, we use a model checker to prove the requirement for the TM applied to a most general program with two threads and two variables. In the safety case, the model checker checks language inclusion between two finite-state transition systems, a nondeterministic transition system representing the given TM applied to a most general program, and a deterministic transition system representing a most liberal safe TM applied to the same program. The given TM transition system is nondeterministic because a TM can be used with different contention managers, which resolve conflicts differently. In the liveness case, the model checker analyzes fairness conditions on the given TM transition system
Expressiveness of streaming string transducers
Get PDFStreaming string transducers [1] define (partial) functions from input strings to output strings. A streaming string transducer makes a single pass through the input string and uses a finite set of variables that range over strings from the output alphabet. At every step, the transducer processes an input symbol, and updates all the variables in parallel using assignments whose right-hand-sides are concatenations of output symbols and variables with the restriction that a variable can be used at most once in a right-hand-side expression. It has been shown that streaming string transducers operating on strings over infinite data domains are of interest in algorithmic verification of list-processing programs, as they lead to PSPACE decision procedures for checking pre/post conditions and for checking semantic equivalence, for a well-defined class of heap-manipulating programs. In order to understand the theoretical expressiveness of streaming transducers, we focus on streaming transducers processing strings over finite alphabets, given the existence of a robust and well-studied class of "regular" transductions for this case. Such regular transductions can be defined either by two-way deterministic finite-state transducers, or using a logical MSO-based characterization. Our main result is that the expressiveness of streaming string transducers coincides exactly with this class of regular transductions
GIST: A solver for probabilistic games
Get PDFGIST is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides the first and efficient implementations of several reduction-based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications
Strategy construction for parity games with imperfect information
Get PDFWe consider two-player parity games with imperfect information in which strategies rely on observations that provide imperfect information about the history of a play. To solve such games, i.e., to determine the winning regions of players and corresponding winning strategies, one can use the subset construction to build an equivalent perfect-information game. Recently, an algorithm that avoids the inefficient subset construction has been proposed. The algorithm performs a fixed-point computation in a lattice of antichains, thus maintaining a succinct representation of state sets. However, this representation does not allow to recover winning strategies. In this paper, we build on the antichain approach to develop an algorithm for constructing the winning strategies in parity games of imperfect information. One major obstacle in adapting the classical procedure is that the complementation of attractor sets would break the invariant of downward-closedness on which the antichain representation relies. We overcome this difficulty by decomposing problem instances recursively into games with a combination of reachability, safety, and simpler parity conditions. We also report on an experimental implementation of our algorithm: to our knowledge, this is the first implementation of a procedure for solving imperfect-information parity games on graphs. (C) 2010 Elsevier Inc. All rights reserved