@inproceedings{3356,
abstract = {There is recently a significant effort to add quantitative objectives to formal verification and synthesis. We introduce and investigate the extension of temporal logics with quantitative atomic assertions, aiming for a general and flexible framework for quantitative-oriented specifications. In the heart of quantitative objectives lies the accumulation of values along a computation. It is either the accumulated summation, as with the energy objectives, or the accumulated average, as with the mean-payoff objectives. We investigate the extension of temporal logics with the prefix-accumulation assertions Sum(v) ≥ c and Avg(v) ≥ c, where v is a numeric variable of the system, c is a constant rational number, and Sum(v) and Avg(v) denote the accumulated sum and average of the values of v from the beginning of the computation up to the current point of time. We also allow the path-accumulation assertions LimInfAvg(v) ≥ c and LimSupAvg(v) ≥ c, referring to the average value along an entire computation. We study the border of decidability for extensions of various temporal logics. In particular, we show that extending the fragment of CTL that has only the EX, EF, AX, and AG temporal modalities by prefix-accumulation assertions and extending LTL with path-accumulation assertions, result in temporal logics whose model-checking problem is decidable. The extended logics allow to significantly extend the currently known energy and mean-payoff objectives. Moreover, the prefix-accumulation assertions may be refined with "controlled-accumulation", allowing, for example, to specify constraints on the average waiting time between a request and a grant. On the negative side, we show that the fragment we point to is, in a sense, the maximal logic whose extension with prefix-accumulation assertions permits a decidable model-checking procedure. Extending a temporal logic that has the EG or EU modalities, and in particular CTL and LTL, makes the problem undecidable.},
author = {Boker, Udi and Chatterjee, Krishnendu and Henzinger, Thomas A and Kupferman, Orna},
location = {Toronto, Canada},
publisher = {IEEE},
title = {{Temporal specifications with accumulative values}},
doi = {10.1109/LICS.2011.33},
year = {2011},
}
@inproceedings{3357,
abstract = {We consider two-player graph games whose objectives are request-response condition, i.e conjunctions of conditions of the form "if a state with property Rq is visited, then later a state with property Rp is visited". The winner of such games can be decided in EXPTIME and the problem is known to be NP-hard. In this paper, we close this gap by showing that this problem is, in fact, EXPTIME-complete. We show that the problem becomes PSPACE-complete if we only consider games played on DAGs, and NP-complete or PTIME-complete if there is only one player (depending on whether he wants to enforce or spoil the request-response condition). We also present near-optimal bounds on the memory needed to design winning strategies for each player, in each case.},
author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Horn, Florian},
editor = {Dediu, Adrian-Horia and Inenaga, Shunsuke and Martín-Vide, Carlos},
location = {Tarragona, Spain},
pages = {227 -- 237},
publisher = {Springer},
title = {{The complexity of request-response games}},
doi = {10.1007/978-3-642-21254-3_17},
volume = {6638},
year = {2011},
}
@inproceedings{3358,
abstract = {The static scheduling problem often arises as a fundamental problem in real-time systems and grid computing. We consider the problem of statically scheduling a large job expressed as a task graph on a large number of computing nodes, such as a data center. This paper solves the large-scale static scheduling problem using abstraction refinement, a technique commonly used in formal verification to efficiently solve computationally hard problems. A scheduler based on abstraction refinement first attempts to solve the scheduling problem with abstract representations of the job and the computing resources. As abstract representations are generally small, the scheduling can be done reasonably fast. If the obtained schedule does not meet specified quality conditions (like data center utilization or schedule makespan) then the scheduler refines the job and data center abstractions and, again solves the scheduling problem. We develop different schedulers based on abstraction refinement. We implemented these schedulers and used them to schedule task graphs from various computing domains on simulated data centers with realistic topologies. We compared the speed of scheduling and the quality of the produced schedules with our abstraction refinement schedulers against a baseline scheduler that does not use any abstraction. We conclude that abstraction refinement techniques give a significant speed-up compared to traditional static scheduling heuristics, at a reasonable cost in the quality of the produced schedules. We further used our static schedulers in an actual system that we deployed on Amazon EC2 and compared it against the Hadoop dynamic scheduler for large MapReduce jobs. Our experiments indicate that there is great potential for static scheduling techniques.},
author = {Henzinger, Thomas A and Singh, Vasu and Wies, Thomas and Zufferey, Damien},
location = {Salzburg, Austria},
pages = {329 -- 342},
publisher = {ACM},
title = {{Scheduling large jobs by abstraction refinement}},
doi = {10.1145/1966445.1966476},
year = {2011},
}
@inproceedings{3359,
abstract = {Motivated by improvements in constraint-solving technology and by the increase of routinely available computational power, partial-program synthesis is emerging as an effective approach for increasing programmer productivity. The goal of the approach is to allow the programmer to specify a part of her intent imperatively (that is, give a partial program) and a part of her intent declaratively, by specifying which conditions need to be achieved or maintained. The task of the synthesizer is to construct a program that satisfies the specification. As an example, consider a partial program where threads access shared data without using any synchronization mechanism, and a declarative specification that excludes data races and deadlocks. The task of the synthesizer is then to place locks into the program code in order for the program to meet the specification.
In this paper, we argue that quantitative objectives are needed in partial-program synthesis in order to produce higher-quality programs, while enabling simpler specifications. Returning to the example, the synthesizer could construct a naive solution that uses one global lock for shared data. This can be prevented either by constraining the solution space further (which is error-prone and partly defeats the point of synthesis), or by optimizing a quantitative objective that models performance. Other quantitative notions useful in synthesis include fault tolerance, robustness, resource (memory, power) consumption, and information flow.},
author = {Cerny, Pavol and Henzinger, Thomas A},
location = {Taipei; Taiwan},
pages = {149 -- 154},
publisher = {ACM},
title = {{From boolean to quantitative synthesis}},
doi = {10.1145/2038642.2038666},
year = {2011},
}
@inproceedings{3360,
abstract = {A discounted-sum automaton (NDA) is a nondeterministic finite automaton with edge weights, which values a run by the discounted sum of visited edge weights. More precisely, the weight in the i-th position of the run is divided by lambda^i, where the discount factor lambda is a fixed rational number greater than 1. Discounted summation is a common and useful measuring scheme, especially for infinite sequences, which reflects the assumption that earlier weights are more important than later weights. Determinizing automata is often essential, for example, in formal verification, where there are polynomial algorithms for comparing two deterministic NDAs, while the equivalence problem for NDAs is not known to be decidable. Unfortunately, however, discounted-sum automata are, in general, not determinizable: it is currently known that for every rational discount factor 1 < lambda < 2, there is an NDA with lambda (denoted lambda-NDA) that cannot be determinized. We provide positive news, showing that every NDA with an integral factor is determinizable. We also complete the picture by proving that the integers characterize exactly the discount factors that guarantee determinizability: we show that for every non-integral rational factor lambda, there is a nondeterminizable lambda-NDA. Finally, we prove that the class of NDAs with integral discount factors enjoys closure under the algebraic operations min, max, addition, and subtraction, which is not the case for general NDAs nor for deterministic NDAs. This shows that for integral discount factors, the class of NDAs forms an attractive specification formalism in quantitative formal verification. All our results hold equally for automata over finite words and for automata over infinite words. },
author = {Boker, Udi and Henzinger, Thomas A},
location = {Bergen, Norway},
pages = {82 -- 96},
publisher = {Springer},
title = {{Determinizing discounted-sum automata}},
doi = {10.4230/LIPIcs.CSL.2011.82},
volume = {12},
year = {2011},
}
@inproceedings{3361,
abstract = {In this paper, we investigate the computational complexity of quantitative information flow (QIF) problems. Information-theoretic quantitative relaxations of noninterference (based on Shannon entropy)have been introduced to enable more fine-grained reasoning about programs in situations where limited information flow is acceptable. The QIF bounding problem asks whether the information flow in a given program is bounded by a constant $d$. Our first result is that the QIF bounding problem is PSPACE-complete. The QIF memoryless synthesis problem asks whether it is possible to resolve nondeterministic choices in a given partial program in such a way that in the resulting deterministic program, the quantitative information flow is bounded by a given constant $d$. Our second result is that the QIF memoryless synthesis problem is also EXPTIME-complete. The QIF memoryless synthesis problem generalizes to QIF general synthesis problem which does not impose the memoryless requirement (that is, by allowing the synthesized program to have more variables then the original partial program). Our third result is that the QIF general synthesis problem is EXPTIME-hard.},
author = {Cerny, Pavol and Chatterjee, Krishnendu and Henzinger, Thomas A},
location = {Cernay-la-Ville, France},
pages = {205 -- 217},
publisher = {IEEE},
title = {{The complexity of quantitative information flow problems}},
doi = {10.1109/CSF.2011.21},
year = {2011},
}
@inproceedings{3362,
abstract = {State-transition systems communicating by shared variables have been the underlying model of choice for applications of model checking. Such formalisms, however, have difficulty with modeling process creation or death and communication reconfigurability. Here, we introduce “dynamic reactive modules” (DRM), a state-transition modeling formalism that supports dynamic reconfiguration and creation/death of processes. The resulting formalism supports two types of variables, data variables and reference variables. Reference variables enable changing the connectivity between processes and referring to instances of processes. We show how this new formalism supports parallel composition and refinement through trace containment. DRM provide a natural language for modeling (and ultimately reasoning about) biological systems and multiple threads communicating through shared variables.},
author = {Fisher, Jasmin and Henzinger, Thomas A and Nickovic, Dejan and Piterman, Nir and Singh, Anmol and Vardi, Moshe},
location = {Aachen, Germany},
pages = {404 -- 418},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Dynamic reactive modules}},
doi = {10.1007/978-3-642-23217-6_27},
volume = {6901},
year = {2011},
}
@unpublished{3363,
abstract = {We consider probabilistic automata on infinite words with acceptance defined by safety, reachability, Büchi, coBüchi, and limit-average conditions. We consider quantitative and qualitative decision problems. We present extensions and adaptations of proofs for probabilistic finite automata and present a complete characterization of the decidability and undecidability frontier of the quantitative and qualitative decision problems for probabilistic automata on infinite words.},
author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Tracol, Mathieu},
pages = {19},
publisher = {ArXiv},
title = {{The decidability frontier for probabilistic automata on infinite words}},
year = {2011},
}
@article{3364,
abstract = {Molecular noise, which arises from the randomness of the discrete events in the cell, significantly influences fundamental biological processes. Discrete-state continuous-time stochastic models (CTMC) can be used to describe such effects, but the calculation of the probabilities of certain events is computationally expensive. We present a comparison of two analysis approaches for CTMC. On one hand, we estimate the probabilities of interest using repeated Gillespie simulation and determine the statistical accuracy that we obtain. On the other hand, we apply a numerical reachability analysis that approximates the probability distributions of the system at several time instances. We use examples of cellular processes to demonstrate the superiority of the reachability analysis if accurate results are required.},
author = {Didier, Frédéric and Henzinger, Thomas A and Mateescu, Maria and Wolf, Verena},
journal = {Theoretical Computer Science},
number = {21},
pages = {2128 -- 2141},
publisher = {Elsevier},
title = {{Approximation of event probabilities in noisy cellular processes}},
doi = {10.1016/j.tcs.2010.10.022},
volume = {412},
year = {2011},
}
@inproceedings{3365,
abstract = {We present the tool Quasy, a quantitative synthesis tool. Quasy takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. The user can choose between a system that satisfies and optimizes the specifications (a) under all possible environment behaviors or (b) under the most-likely environment behaviors given as a probability distribution on the possible input sequences. Quasy solves these two quantitative synthesis problems by reduction to instances of 2-player games and Markov Decision Processes (MDPs) with quantitative winning objectives. Quasy can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with mean-payoff objectives, and ergodic MDPs with mean-payoff parity objectives.},
author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Jobstmann, Barbara and Singh, Rohit},
location = {Saarbrucken, Germany},
pages = {267 -- 271},
publisher = {Springer},
title = {{QUASY: quantitative synthesis tool}},
doi = {10.1007/978-3-642-19835-9_24},
volume = {6605},
year = {2011},
}