6607709
Itinerary-Based Access Control for Mobile Tasks in Scientific Workflows
Current scientific workflow models require datasets to be transferred from their source to the hosts where they can be processed. This seriously impedes data-intensive applications. In order to address this limitation, we propose a notion of mobile tasks that can move from their home hosts towards datasets and perform computation on the dataset side. Since a mobile task might migrate across various administrative domains and get executed at multiple hosts, it is critically important to ensure the security of a mobile- task-based workflow system. In this paper, we propose an itinerary-based access control model to ensure the secure migration of mobile tasks.
25613636
Efficient distributed SAT and SAT-based distributed Bounded Model Checking
SAT-based Bounded Model Checking (BMC), though a robust and scalable verification approach, still is computationally intensive, requiring large memory and time. Even with the recent development of improved SAT solvers, the memory limitation of a single server rather than time can become a bottleneck for doing deeper BMC search for large designs. Distributing computing requirements of BMC over a network of workstations can overcome the memory limitation of a single server, albeit at increased communication cost. In this paper, we present (a) a method for distributed SAT over a network of workstations using a Master/Client model where each Client workstation has an exclusive partition of the SAT problem and uses knowledge of partition topology to communicate with other Clients, (b) a method for distributing SAT-based BMC using the distributed SAT. For the sake of scalability, at no point in the BMC computation does a single workstation have all the information. We experimented on a network of heterogeneous workstations interconnected with a standard Ethernet LAN. To illustrate, on an industrial design with ∼13 K FFs and ∼0.5 million gates, the non-distributed BMC on a single workstation (with 4 GB memory) ran out of memory after reaching a depth of 120; on the other hand, our SAT-based distributed BMC over 5 similar workstations was able to go up to 323 steps with a communication overhead of only 30%.
13092959
Whodunit? Causal Analysis for Counterexamples
Although the counterexample returned by a model checker can help in reproducing the symptom related to a defect, a significant amount of effort is often required for the programmer to interpret it in order to locate the cause. In this paper, we provide an automated procedure to zoom in to potential software defects by analyzing a single concrete counterexample. Our analysis relies on extracting from the counterexample a syntactic-level proof of infeasibility, i.e., a minimal set of word-level predicates that contradict with each other. The procedure uses an efficient weakest pre-condition algorithm carried out on a single concrete execution path, which is significantly more scalable than other model checking based approaches. Unlike most of the existing methods, we do not need additional execution traces other than the buggy one. We use public-domain examples to demonstrate the effectiveness of our new algorithm.
361158
Efficient Distributed SAT and SAT-Based Distributed Bounded Model Checking
SAT-based Bounded Model Checking (BMC), though a robust and scalable verification approach, still is computationally intensive, requiring large memory and time. Interestingly, with the recent development of improved SAT solvers, it is frequently the memory limitation of a single server rather than time that becomes a bottleneck for doing deeper BMC search. Distributing computing requirements of BMC over a network of workstations can overcome the memory limitation of a single server, albeit at increased communication cost. In this paper, we present: a) a method for distributed-SAT over a network of workstations using a Master/Client model where each Client worsktation has an exclusive partition of the SAT problem and uses knowledge of partition topology to communicate with other Clients, b) a method for distributing SAT-based BMC using the distributed-SAT. For the sake of scalability, at no point in the BMC computation does a single workstation have all the information. We experimented on a network of heterogenous workstations interconnected with a standard Ethernet LAN. To illustrate, on an industrial design with 13K FFs and 0.5M gates, the non-disributed BMC on a single workstation (with 4 Gb memory) ran out of memroy after reaching a depth of 120; on the otherhand, our SAT-based distributed BMC over 5 similar workstations was able to go upto 323 steps with a communication overhead of only 30%.
6254649
Disjunctive Image Computation for Embedded Software Verification
Finite state models generated from software programs have unique characteristics that are not exploited by existing model checking algorithms. In this paper, we propose a novel disjunctive image computation algorithm and other simplifications based on these characteristics. Our algorithm divides an image computation into a disjunctive set of easier ones that can be performed in isolation. Hypergraph partitioning is used to minimize the number of live variables in each disjunctive component. We use the live variables to simplify transition relations and reachable state subsets. Our experiments on a set of real-world C programs show that the new algorithm achieves orders-of-magnitude performance improvement over the best known conjunctive image computation algorithm
193663
SAT-Based Image Computation with Application in Reachability Analysis
Image computation nds wide application in VLSI CAD, such as state reachability analysis in formal veri cation and synthesis, combinational veri cation, combinational and sequential test. Existing BDD-based symbolic algorithms for image computation are limited by memory resources in practice, while SAT-based algorithms that can obtain the image by enumerating satisfying assignments to a CNF representation of the Boolean relation are potentially limited by time resources. We propose new algorithms that combine BDDs and SAT in order to exploit their complementary bene ts, and to o er a mechanism for trading o space vs. time. In particular, (1) our integrated algorithm uses BDDs to represent the input and image sets, and a CNF formula to represent the Boolean relation, (2) a fundamental enhancement called BDD Bounding is used whereby the SAT solver uses the BDDs for the input set and the dynamically changing image set to prune the search space of all solutions, (3) BDDs are used to compute all solutions below intermediate points in the SAT decision tree, (4) a ne-grained variable quanti cation schedule is used for each BDD subproblem, based on the CNF representation of the Boolean relation. These enhancements coupled with more engineering heuristics lead to an overall algorithm that can potentially handle larger problems. This is supported by our preliminary results on exact reachability analysis of ISCAS benchmark circuits.
2501005
Coordinating Multiple Agents via Reinforcement Learning
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ‘‘coarse-grained’’ and ‘‘fine-grained’’, are proposed to address agents coordination behavior at two different levels. The ‘‘coarse-grained’’ algorithm operates at one level and tackle hard system constraints, and the ‘‘fine-grained’’ at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.
36782214
Comparison of Point-, Line- and Boundary-Sampled Intercepts Inside a Circle or Sphere
Point-, line- or boundary-sampled intercepts may be measured inside a particle as a measure of particle size. Each intercept is primarily characterized by three geometric properties: length, location and orientation. Circle and sphere models are used in the present study to analyze these properties. The probability distribution function, probability density function, expectation and coefficient of variation for each of the properties were presented based on geometric probability and mathematical statistics. Such presentation would be helpful for potential users of stereology to better understand the concept of intercepts and implement stereological intercept measurement for estimation of particle sizes in practice.
16679508
Regularized Neighborhood Component Analysis
Discriminative feature extraction is one of the fundamental problems in pattern recognition and signal processing. It was recently proposed that maximizing the class prediction by neighboring samples in the transformed space is an effective objective for learning a low-dimensional linear embedding of labeled data. The associated methods, Neighborhood Component Analysis (NCA) and Relevant Component Analysis (RCA), have been proven to be useful preprocessing techniques for discriminative information visualization and classification. We point out here that NCA and RCA are prone to overfitting and therefore regularization is required. NCA and RCA's failure for high-dimensional data is demonstrated in this paper by experiments in facial image processing. We also propose to incorporate a Gaussian prior into the NCA objective and obtain the Regularized Neighborhood Component Analysis (RNCA). The empirical results show that the generalization can be significantly enhanced by using the proposed regularization method.
17126561
Experimental Analysis of Privacy-Preserving Statistics Computation
The recent investigation of privacy-preserving data mining and other kinds of privacy-preserving distributed computation has been motivated by the growing concern about the privacy of individuals when their data is stored, aggregated, and mined for information. Building on the study of selective private function evaluation and the efforts towards practical algorithms for privacy-preserving data mining solutions, we analyze and implement solutions to an important primitive, that of computing statistics of selected data in a remote database in a privacy-preserving manner. We examine solutions in different scenarios ranging from a high speed communications medium, such as a LAN or high-speed Internet connection, to a decelerated communications medium to account for worst-case communication delays such as might be provided in a wireless multihop setting.
34587947
Guaranteed Mutually Consistent Checkpointing in Distributed Computations
In this paper, we emplore the isomorphism between vector time and causality to characterize consistency of a set of checkpoints in a distributed computing. A necessary and sufficient condition, to determine if a set of checkpoints can form a consistent global checkpoint, is presented and proved using the isomorphic power of vector time and causality. To the best of our knowledge, this is the first attempt to use the isomorphism for this purpose. This condition leads to a simple and straightforward algorithm for a guaranteed mutually consistent global checkpointing. In our approach, a process can take a checkpoint whenever and wherever it wants while other related process may be asked to take an additional checkpoint for ensuring the mutual consistency. We also show how this condition and the resulting algorithm can be used to obtain a maximum and minimum global checkpoints, another important paradigm for distributed applications.