Formula partitioning revisited

Dividing a Boolean formula into smaller independent sub-formulae can be a useful technique for accelerating the solution of Boolean problems, including SAT and #SAT. Nevertheless, and despite promising early results, formula partitioning is hardly used in state-of-the-art solvers. In this paper, we show that this is rooted in a lack of consistency of the usefulness of formula partitioning techniques. In particular, we evaluate two existing and a novel partitioning model, coupled with two existing and two novel partitioning algorithms, on a wide range of benchmark instances. Our results show that there is no one-size-fits-all solution: for different formula types, different partitioning models and algorithms are the most suitable. While these results might seem negative, they help to improve our understanding about formula partitioning; moreover, the findings also give guidance as to which method to use for what kinds of formulae

Large-scale parallelism for constraint-based local search: the costas array case study

International audienceWe present the parallel implementation of a constraint-based Local Search algorithm and investigate its performance on several hardware plat-forms with several hundreds or thousands of cores. We chose as the basis for these experiments the Adaptive Search method, an efficient sequential Local Search method for Constraint Satisfaction Problems (CSP). After preliminary experiments on some CSPLib benchmarks, we detail the modeling and solving of a hard combinatorial problem related to radar and sonar applications: the Costas Array Problem. Performance evaluation on some classical CSP bench-marks shows that speedups are very good for a few tens of cores, and good up to a few hundreds of cores. However for a hard combinatorial search problem such as the Costas Array Problem, performance evaluation of the sequential version shows results outperforming previous Local Search implementations, while the parallel version shows nearly linear speedups up to 8,192 cores. The proposed parallel scheme is simple and based on independent multi-walks with no communication between processes during search. We also investigated a cooperative multi-walk scheme where processes share simple information, but this scheme does not seem to improve performance

Large-Scale Parallelism for Constraint-Based Local Search: The Costas Array Case Study

Abstract We present the parallel implementation of a constraint-based Local Search algorithm and investigate its performance on several hardware platforms with several hundreds or thousands of cores. We chose as the basis for these experiments the Adaptive Search method, an efficient sequential Local Search method for Constraint Satisfaction Problems (CSP). After preliminary experiments on some CSPLib benchmarks, we detail the modeling and solving of a hard combinatorial problem related to radar and sonar applications: the Costas Array Problem. Performance evaluation on some classical CSP benchmarks shows that speedups are very good for a few tens of cores, and good up to a few hundreds of cores. However for a hard combinatorial search problem such as the Costas Array Problem, performance evaluation of the sequential version shows results outperforming previous Local Search implementations, while the parallel version shows nearly linear speedups up to 8,192 cores. The proposed parallel scheme is simple and based on independent multi-walks with no communication between processes during search. We also investigated a cooperative multi-walk scheme where processes share simple information, but this scheme does not seem to improve performance

Towards Next Generation Sequential and Parallel SAT Solvers

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology

Security Auditing and Multi-Tenancy Threat Evaluation in Public Cloud Infrastructures

Cloud service providers typically adopt the multi-tenancy model to optimize resources usage and achieve the promised cost-effectiveness. However, multi-tenancy in the cloud is a double-edged sword. While it enables cost-effective resource sharing, it increases security risks for the hosted applications. Indeed, multiplexing virtual resources belonging to different tenants on the same physical substrate may lead to critical security concerns such as cross-tenant data leakage and denial of service. Therefore, there is an increased necessity and a pressing need to foster transparency and accountability in multi-tenant clouds. In this regard, auditing security compliance of the cloud provider’s infrastructure against standards, regulations and customers’ policies on one side, and evaluating the multi-tenancy threat on the other side, take on an increasing importance to boost the trust between the cloud stakeholders. However, auditing virtual infrastructures is challenging due to the dynamic and layered nature of the cloud. Particularly, inconsistencies in network isolation mechanisms across the cloud stack layers (e.g., the infrastructure management layer and the implementation layer), may lead to virtual network isolation breaches that might be undetectable at a single layer. Additionally, evaluating multi-tenancy threats in the cloud requires systematic ways and effective metrics, which are largely missing in the literature. This thesis work addresses the aforementioned challenges and limitations and articulates around two main topics, namely, security compliance auditing and multi-tenancy threat evaluation in the cloud. Our objective in the first topic is to propose an automated framework that allows auditing the cloud infrastructure from the structural point of view, while focusing on virtualization-related security properties and consistency between multiple control layers. To this end, we devise a multi-layered model related to each cloud stack layer’s view in order to capture the semantics of the audited data and its relation to consistent isolation requirements. Furthermore, we integrate our auditing system into OpenStack, and present our experimental results on assessing several properties related to virtual network isolation and consistency. Our results show that our approach can be successfully used to detect virtual network isolation breaches for large OpenStack-based data centers in a reasonable time. The objective of the second topic is to derive security metrics for evaluating the multi-tenancy threats in public clouds. To this end, we propose security metrics to quantify the proximity between tenants’ virtual resources inside the cloud. Those metrics are defined based on the configuration and deployment of a cloud, such that a cloud provider may apply them to evaluate and mitigate co-residency threats. To demonstrate the effectiveness of our metrics and show their usefulness, we conduct case studies based on both real and synthetic cloud data. We further perform extensive simulations using CloudSim and wellknown VM placement policies. The results show that our metrics effectively capture the impact of potential attacks, and the abnormal degrees of co-residency between a victim and potential attackers, which paves the way for the design of effective mitigation solutions against co-residency attacks