Progress in Certifying Hardware Model Checking Results

We present a formal framework to certify k-induction-based model checking results. The key idea is the notion of a k-witness circuit which simulates the given circuit and has a simple inductive invariant serving as proof certificate. Our approach allows to check proofs with an independent proof checker by reducing the certification problem to pure SAT checks and checking a simple QBF with one quantifier alternation. We also present Certifaiger, the resulting certification toolkit, and evaluate it on instances from the hardware model checking competition. Our experiments show the practical use of our certification method.Peer reviewe

A Symbolic Model Checking Approach to Verifying Satellite Onboard Software

This paper discusses the use of symbolic model checking technology to verify the design of an embedded software control system called attitude and orbit control system (AOCS). This system is mission-critical because it is responsible for maintaining the attitude of the satellite and for performing fault detection, isolation, and recovery decisions of the satellite. An executable AOCS implementation by Space Systems Finland has been provided to us in Ada source code form. In order to use symbolic model checking methods, the Ada implementation of the system was modeled at a quite detailed implementation level using the input language of the symbolic model checker NuSMV 2. We describe the modeling techniques and abstractions used to alleviate the state explosion problem due to handling of timers and the large number of system components controlled by AOCS. The specification of the required system behavior was also provided to us in a form of extended state machine diagrams with prioritized transitions. These diagrams have been translated to a set of temporal logic properties, allowing the piecewise checking of the system behavior one extended state machine transition at a time. We also report on the scalability of symbolic model checking tools for the case study at hand as well as discuss potential topics for future work

Complexity results for checking distributed implementability

We consider the distributed implementability problem as: Given a labelled transition system TS together with a distribution D of its actions over a set of processes, does there exist a distributed system over D such that its global transition system is equivalent' to TS? We consider the distributed system models of synchronous products of transition systems and Zielonka's asynchronous automata. In this paper we provide complexity bounds for the above problem with three interpretations of equivalent': as transition system isomorphism, as language equivalence, and as bisimilarity. In particular, we solve two problems left open in the literature. We also describe a logic programming implementation which complements the existing implementation for the synthesis of asynchronous automata initiated by the second author

Linear Encodings of Bounded LTL Model Checking

We consider the problem of bounded model checking (BMC) for linear temporal logic (LTL). We present several efficient encodings that have size linear in the bound. Furthermore, we show how the encodings can be extended to LTL with past operators (PLTL). The generalised encoding is still of linear size, but cannot detect minimal length counterexamples. By using the virtual unrolling technique minimal length counterexamples can be captured, however, the size of the encoding is quadratic in the specification. We also extend virtual unrolling to Buchi automata, enabling them to accept minimal length counterexamples. Our BMC encodings can be made incremental in order to benefit from incremental SAT technology. With fairly small modifications the incremental encoding can be further enhanced with a termination check, allowing us to prove properties with BMC. Experiments clearly show that our new encodings improve performance of BMC considerably, particularly in the case of the incremental encoding, and that they are very competitive for finding bugs. An analysis of the liveness-to-safety transformation reveals many similarities to the BMC encodings in this paper. Using the liveness-to-safety translation with BDD-based invariant checking results in an efficient method to find shortest counterexamples that complements the BMC-based approach.Comment: Final version for Logical Methods in Computer Science CAV 2005 special issu

Surrogate-based optimization of a periodic rescheduling algorithm

Periodic rescheduling is an iterative method for real-time decision-making on industrial process operations. The design of such methods involves high-level when-to-schedule and how-to-schedule decisions, the optimal choices of which depend on the operating environment. The evaluation of the choices typically requires computationally costly simulation of the process, which-if not sufficiently efficient-may result in a failure to deploy the system in practice. We propose the continuous control parameter choices, such as the re-optimization frequency and horizon length, to be determined using surrogate-based optimization. We demonstrate the method on real-time rebalancing of a bike sharing system. Our results on three test cases indicate that the method is useful in reducing the computational cost of optimizing an online algorithm in comparison to the full factorial sampling.Peer reviewe

Reinforcement learning of adaptive online rescheduling timing and computing time allocation

Mathematical optimization methods have been developed to a vast variety of complex problems in the field of process systems engineering (e.g., the scheduling of chemical batch processes). However, the use of these methods in online scheduling is hindered by the stochastic nature of the processes and prohibitively long solution times when optimized over long time horizons. The following questions are raised: When to trigger a rescheduling, how much computing resources to allocate, what optimization strategy to use, and how far ahead to schedule? We propose an approach where a reinforcement learning agent is trained to make the first two decisions (i.e., rescheduling timing and computing time allocation). Using neuroevolution of augmenting topologies (NEAT) as the reinforcement learning algorithm, the approach yields, on average, better closed-loop solutions than conventional rescheduling methods on three out of four studied routing problems. We also reflect on expanding the agent's decision-making to all four decisions. (C) 2020 Elsevier Ltd. All rights reserved.Peer reviewe

IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications

The evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and fault-tolerant applications. Nonetheless, many of these applications administer data collection on the edge and offer data analytic and storage capabilities in the cloud. This raises the problem of separate software stacks between the edge and the cloud with no unified fault-tolerant management, hindering dynamic relocation of data processing. In such systems, the data must also be preserved from being corrupted or duplicated in the case of intermittent long-distance network connectivity issues, malicious harming of edge devices, or other hostile environments. Within this context, the contributions of this paper are threefold: (i) to propose a new Internet of Things Edge-Cloud Federation (IoTEF) architecture for multi-cluster IoT applications by adapting our earlier Cloud and Edge Fault-Tolerant IoT (CEFIoT) layered design. We address the fault tolerance issue by employing the Apache Kafka publish/subscribe platform as the unified data replication solution. We also deploy Kubernetes for fault-tolerant management, combined with the federated scheme, offering a single management interface and allowing automatic reconfiguration of the data processing pipeline, (ii) to formulate functional and non-functional requirements of our proposed solution by comparing several IoT architectures, and (iii) to implement a smart buildings use case of the ongoing Otaniemi3D project as proof-of-concept for assessing IoTEF capabilities. The experimental results conclude that the architecture minimizes latency, saves network bandwidth, and handles both hardware and network connectivity based failures.Peer reviewe

Stratified Certification for k-Induction

Our recently proposed certification framework for bit-level k-induction-based model checking has been shown to be quite effective in increasing the trust of verification results even though it partially involved quantifier reasoning. In this paper we show how to simplify the approach by assuming reset functions to be stratified. This way it can be lifted to word-level and in principle to other theories where quantifier reasoning is difficult. Our new method requires six simple SAT checks and one polynomial-time check, allowing certification to remain in co-NP while the previous approach required five SAT checks and one QBF check. Experimental results show a substantial performance gain for our new approach. Finally we present and evaluate our new tool CERTIFAIGER-WL which is able to certify k-induction-based word-level model checking.Peer reviewe

Scalability of a cloud-based data store: improving HBase performance

Cloud computing has opened doors to a new era of enterprises that harness the new Cloud enabled business. More and more novel applications are leveraging this paradigm every day, which translates to a never seen increase in the amount of stored data. This phenomenon is commonly known as Big Data; the presence of rapidly expanding high-volume data sets. Many of these applications bring new challenges to databases and therefore the scalability of the Cloud-based databases has become a top-research issue of the Cloud Computing infrastructure. As an alternative to the well-known relational databases, NoSQL databases have born to fit Big Data application requirements. Traditional relational databases as they are often implemented are not sufficient anymore for Internet scale distributed systems dealing with Big Data. Nevertheless, NoSQL have proved to be robust in Big Data applications. The purpose of this project is to scaling out the data of a San Diego company that produces software for wireless multimedia, which is currently implemented on a MySQL cluster. In order to improve the performance of the computation, we propose a solution using Apache HBase, a NoSQL database. This final project proposes implementations as well as comparison details of a number of computation techniques conducted in HBase along with different open-source distributed computing components such as Hadoop HDFS and MapReduce, and presents benchmarks of our developed solution