1577905
SDSAT: Tight Integration of Small Domain Encoding and Lazy Approaches in a Separation Logic Solver
Existing Separation Logic (a.k.a Difference Logic, DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel Separation Logic Solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in separation predicates. This phase is similar to previous small domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and smaller ranges for variables. Furthermore, the Separation Logic formula is not transformed into an equi-satisfiable Boolean formula in one step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it can not be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state-of-the art solvers.
11387042
Predicate learning and selective theory deduction for a difference logic solver
Design and verification of systems at the register-transfer (RT) or behavioral level require the ability to reason at higher levels of abstraction. Difference logic consists of an arbitrary Boolean combination of propositional variables and difference predicates and therefore provides an appropriate abstraction. In this paper, we present several new optimization techniques for efficiently deciding difference logic formulas. We use the lazy approach by combining a DPLL Boolean SAT procedure with a dedicated graph-based theory solver, which adds transitivity constraints among difference predicates on a "need-to" basis. Our new optimization techniques include flexible theory constraint propagation, selective theory deduction, and dynamic predicate learning. We have implemented these techniques in our lazy solver. We demonstrate the effectiveness of the proposed techniques on public benchmarks through a set of controlled experiments
8665183
Dynamic detection and removal of inactive clauses in SAT with application in image computation
In this paper, we present a new technique for the efficient dynamic detection and removal of inactive clauses, i.e. clauses that do not affect the solutions of interest of a Boolean satisfiability (SAT) problem. The algorithm is based on the extraction of gate connectivity information during generation of the Boolean formula from the circuit, and its use in the inner loop of a branch-and-bound SAT algorithm. The motivation for this optimization is to exploit the circuit structure information, which can be used to find unobservable gates at circuit outputs under dynamic conditions. It has the potential to speed up all applications of SAT in which the SAT formula is derived from a logic circuit. In particular, we find that it has considerable impact on an image computation algorithm based on SAT. We present practical results for benchmark circuits which show that the use of this optimization consistently improves the performance for reachability analysis, in some cases enabling the prototype tool to reach more states than otherwise possible.
15353920
Beyond safety: customized SAT-based model checking
Model checking of safety properties has taken a significant lead over nonsafety properties in recent years. To bridge the gap, we propose dedicated SAT-based model checking algorithms for properties beyond safety. Previous bounded model checking (BMC) approaches have relied on either converting such properties to safety checking, or finding proofs by deriving termination criteria using loop-free path analysis. Instead, our approach uses a customized SAT-based formulation for bounded model checking of nonsafety properties, and determines the completeness bounds for liveness using unbounded SAT-based analysis. Our main contributions are: 1) Customized property translations for LTL formulas for BMC, with novel features that utilize partitioning, learning, and incremental formulation. Customized translations not only improve the BMC performance significantly in comparison to standard monolithic LTL translations, but also allow efficient derivation and use of completeness bounds. Though we discuss the translation schemas for liveness, they can be easily extended to handle other LTL properties as well. 2) Customized formulations for determining completeness bounds for liveness using SAT-based unbounded model checking (UMC) rather than using loop-free path analysis. These formulations comprise greatest fixed-point and least fixed-point computations to efficiently handle nested properties using SAT-based quantification approaches. We show the effectiveness of our overall approach for checking liveness on public benchmarks and several industry designs.
10395010
Abstraction and BDDs Complement SAT-Based BMC in DiVer
Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity for finding bugs in large designs. However, due to its incompleteness, there is a need to perform deeper searches for counterexamples, or a proof by induction where possible. The DiVer verification platform uses abstraction and BDDs to complement BMC in the quest for completeness. We demonstrate the effectiveness of our approach in practice on industrial designs.
600527
Learning from BDDs in SAT-based bounded model checking
Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity as an alternative to BDD-based model checking techniques for finding bugs in large designs. In this paper, we explore the use of learning from BDDs, where learned clauses generated by BDD-based analysis are added to the SAT solver, to supplement its other learning mechanisms. We propose several heuristics for guiding this process, aimed at increasing the usefulness of the learned clauses, while reducing the overheads. We demonstrate the effectiveness of our approach on several industrial designs, where BMC performance is improved and the design can be searched up to a greater depth by use of BDD-based learning.
13088866
Specification-based anomaly detection: a new approach for detecting network intrusions
Unlike signature or misuse based intrusion detection techniques, anomaly detection is capable of detecting novel attacks. However, the use of anomaly detection in practice is hampered by a high rate of false alarms. Specification-based techniques have been shown to produce a low rate of false alarms, but are not as effective as anomaly detection in detecting novel attacks, especially when it comes to network probing and denial-of-service attacks. This paper presents a new approach that combines specification-based and anomaly-based intrusion detection, mitigating the weaknesses of the two approaches while magnifying their strengths. Our approach begins with state-machine specifications of network protocols, and augments these state machines with information about statistics that need to be maintained to detect anomalies. We present a specification language in which all of this information can be captured in a succinct manner. We demonstrate the effectiveness of the approach on the 1999 Lincoln Labs intrusion detection evaluation data, where we are able to detect all of the probing and denial-of-service attacks with a low rate of false alarms (less than 10 per day). Whereas feature selection was a crucial step that required a great deal of expertise and insight in the case of previous anomaly detection approaches, we show that the use of protocol specifications in our approach simplifies this problem. Moreover, the machine learning component of our approach is robust enough to operate without human supervision, and fast enough that no sampling techniques need to be employed. As further evidence of effectiveness, we present results of applying our approach to detect stealthy email viruses in an intranet environment.
13299166
An Interpolated Volume Model for Databases
In this paper we present a volume data model amenable to querying volumes in databases. Unlike most existing volume models, which are directed towards specific applications (notably, volume displaying) and are therefore directed towards executing with the greatest possible efficiency a small set of operations, we are interested in defining a volume algebra through which generic query conditions can be composed.
43248661
Online Transcoding of Web Pages for Mobile Devices
Accessing Internet from mobile devices such as PDAs and cell phones is becoming widespread. Since these devices do not have the same rendering capabilities as desktop computers, it is necessary for web contents to be transcoded for proper presentation on these types of devices. In this paper we propose an architecture for online transcoding of web documents. In the proposed system, a proxy fetches web pages on the client’s behalf, categorizes the contents into predefined domain-specific categories, and dynamically generates a hierarchical summary view of the relevant topics of the page based on domain specific knowledge. This hierarchical summary of information is then transmitted to the client. The architecture uses domain specific knowledge, but allows easy adaptation to different domains by plugging in rule sets specific to the domain. The system does not expect any adjustment in the existing WWW contents. Results for one test domain, News, are shown. The results indicate that the system can produce high precision transcoded pages online using domain specific knowledge with very little performance penalty.
10440176
Task assignment with imprecise information for real-time operation in a supply chain
Abstract Real-time operation is often required in a supply chain, where it is desirable to re-optimize the schedules each time a new order is submitted. An organization in a supply chain may not be able to perform all the tasks it has received completely due to lack of resources. It has to delegate those subtasks which it cannot perform to its suppliers in the supply chain. In a practical situation, the suppliers may be parts of more than one supply chain. Hence the organization does not have a complete control over the schedules of its suppliers. There should be coordination within and across organizations for producing the schedules. Usually, multiple objectives (or criteria) like quality, cost, and duration are associated with a task. Preference ratings, assigned by the end-user or the organization itself, may be associated with these criteria. The preference ratings may be specified in an imprecise manner by using ordinal values or linguistic terms. The assignment of the tasks to the suppliers, which is a multiobjective decision making problem, has to be done quickly for real-time operation. In this paper, a supply chain is first modeled using a multiagent framework and an architecture for the agents located at every organization in the chain is presented. We then propose a real-time scheduler that can schedule new orders with soft real-time deadlines. An algorithm for the task assignment using the fuzzy set approach for the real-time operation in a supply chain is then proposed. Comparison of the algorithm versus other commonly used heuristic methods for task assignment is done in a simulated environment. The experimental results show significant performance gains for a large range of task arrival rates. However, the performance deteriorates for very high arrival rates. In order to overcome this limitation, we present a hybrid algorithm that augments the fuzzy technique and does not suffer from performance degradation even for high arrival rates.
11586348
Cost-Sharing Mechanisms for Network Design
We consider a single source network design problem from a game-theoretic perspective. Gupta, Kumar and Roughgarden (Proc. 35th Annual ACM STOC, pages 365-372, 2003) developed a simple method for single source rent-or-buy problem that also yields the best-known approximation ratio for the problem. We show how to use a variant of this method to develop an approximately budget-balanced and group strategyproof cost-sharing method for the problem. The novelty of our approach stems from our obtaining the cost-sharing methods for the rent-or-buy problem by carefully combining cost-shares for the simpler problem Steiner tree problem; we feel that this idea may have wider implications. Our algorithm is conceptually simpler than the previous such cost-sharing method due to Pal and Tardos (Proc. 44th Annual FOCS, pages 584-593, 2003), and has a much improved approximation factor of 4.6 (over the previously known factor of 15).