Graph Reachability on Parallel Many-Core Architectures

Many modern applications are modeled using graphs of some kind. Given a graph, reachability, that is, discovering whether there is a path between two given nodes, is a fundamental problem as well as one of the most important steps of many other algorithms. The rapid accumulation of very large graphs (up to tens of millions of vertices and edges) from a diversity of disciplines demand efficient and scalable solutions to the reachability problem. General-purpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize algorithms that present a high degree of regularity. In this paper, we extend the applicability of GPU processing to graph-based manipulation, by re-designing a simple but efficient state-of-the-art graph-labeling method, namely the GRAIL (Graph Reachability Indexing via RAndomized Interval) algorithm, to many-core CUDA-based GPUs. This algorithm firstly generates a label for each vertex of the graph, then it exploits these labels to answer reachability queries. Unfortunately, the original algorithm executes a sequence of depth-first visits which are intrinsically recursive and cannot be efficiently implemented on parallel systems. For that reason, we design an alternative approach in which a sequence of breadth-first visits substitute the original depth-first traversal to generate the labeling, and in which a high number of concurrent visits is exploited during query evaluation. The paper describes our strategy to re-design these steps, the difficulties we encountered to implement them, and the solutions adopted to overcome the main inefficiencies. To prove the validity of our approach, we compare (in terms of time and memory requirements) our GPU-based approach with the original sequential CPU-based tool. Finally, we report some hints on how to conduct further research in the area

Two Decades of Maude

This paper is a tribute to José Meseguer, from the rest of us in the Maude team, reviewing the past, the present, and the future of the language and system with which we have been working for around two decades under his leadership. After reviewing the origins and the language's main features, we present the latest additions to the language and some features currently under development. This paper is not an introduction to Maude, and some familiarity with it and with rewriting logic are indeed assumed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Consistent SDNs through Network State Fuzzing

The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Unfortunately, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to periodically test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly while outperforming baseline approaches.Comment: Added three extra relevant references, the arXiv later was accepted in IEEE Transactions of Network and Service Management (TNSM), 2019 with the title "Towards Consistent SDNs: A Case for Network State Fuzzing

Consistent SDNs through Network State Fuzzing

The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Nevertheless, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to continuously test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly

Lifelong topological visual navigation

La possibilité pour un robot de naviguer en utilisant uniquement la vision est attrayante en raison de sa simplicité. Les approches de navigation traditionnelles basées sur la vision nécessitent une étape préalable de construction de carte qui est ardue et sujette à l'échec, ou ne peuvent que suivre exactement des trajectoires précédemment exécutées. Les nouvelles techniques de navigation visuelle basées sur l'apprentissage réduisent la dépendance à l'égard d'une carte et apprennent plutôt directement des politiques de navigation à partir des images. Il existe actuellement deux paradigmes dominants : les approches de bout en bout qui renoncent entièrement à la représentation explicite de la carte, et les approches topologiques qui préservent toujours une certaine connectivité de l'espace. Cependant, alors que les méthodes de bout en bout ont tendance à éprouver des difficultés dans les tâches de navigation sur de longues distances, les solutions basées sur les cartes topologiques sont sujettes à des défaillances dues à des arêtes erronées dans le graphe. Dans ce document, nous proposons une méthode de navigation visuelle topologique basée sur l'apprentissage, avec des stratégies de mise à jour du graphe, qui améliore les performances de navigation sur toute la durée de vie du robot. Nous nous inspirons des algorithmes de planification basés sur l'échantillonnage pour construire des graphes topologiques basés sur l'image, ce qui permet d'obtenir des graphes plus épars et d'améliorer les performances de navigation par rapport aux méthodes de base. En outre, contrairement aux contrôleurs qui apprennent à partir d'environnements d'entraînement fixes, nous montrons que notre modèle peut être affiné à l'aide d'un ensemble de données relativement petit provenant de l'environnement réel où le robot est déployé. Enfin, nous démontrons la forte performance du système dans des expériences de navigation de robots dans le monde réel.The ability for a robot to navigate using vision only is appealing due to its simplicity. Traditional vision-based navigation approaches require a prior map-building step that was arduous and prone to failure, or could only exactly follow previously executed trajectories. Newer learning-based visual navigation techniques reduce the reliance on a map and instead directly learn policies from image inputs for navigation. There are currently two prevalent paradigms: end-to-end approaches forego the explicit map representation entirely, and topological approaches which still preserve some loose connectivity of the space. However, while end-to-end methods tend to struggle in long-distance navigation tasks, topological map-based solutions are prone to failure due to spurious edges in the graph. In this work, we propose a learning-based topological visual navigation method with graph update strategies that improves lifelong navigation performance over time. We take inspiration from sampling-based planning algorithms to build image-based topological graphs, resulting in sparser graphs with higher navigation performance compared to baseline methods. Also, unlike controllers that learn from fixed training environments, we show that our model can be finetuned using a relatively small dataset from the real-world environment where the robot is deployed. Finally, we demonstrate strong system performance in real world robot navigation experiments

Abduction-Based Explanations for Machine Learning Models

The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper develops a constraint-agnostic solution for computing explanations for any ML model. The proposed solution exploits abductive reasoning, and imposes the requirement that the ML model can be represented as sets of constraints using some target constraint reasoning system for which the decision problem can be answered with some oracle. The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions

Spatial Database Support for Virtual Engineering

The development, design, manufacturing and maintenance of modern engineering products is a very expensive and complex task. Shorter product cycles and a greater diversity of models are becoming decisive competitive factors in the hard-fought automobile and plane market. In order to support engineers to create complex products when being pressed for time, systems are required which answer collision and similarity queries effectively and efficiently. In order to achieve industrial strength, the required specialized functionality has to be integrated into fully-fledged database systems, so that fundamental services of these systems can be fully reused, including transactions, concurrency control and recovery. This thesis aims at the development of theoretical sound and practical realizable algorithms which effectively and efficiently detect colliding and similar complex spatial objects. After a short introductory Part I, we look in Part II at different spatial index structures and discuss their integrability into object-relational database systems. Based on this discussion, we present two generic approaches for accelerating collision queries. The first approach exploits available statistical information in order to accelerate the query process. The second approach is based on a cost-based decompositioning of complex spatial objects. In a broad experimental evaluation based on real-world test data sets, we demonstrate the usefulness of the presented techniques which allow interactive query response times even for large data sets of complex objects. In Part III of the thesis, we discuss several similarity models for spatial objects. We show by means of a new evaluation method that data-partitioning similarity models yield more meaningful results than space-partitioning similarity models. We introduce a very effective similarity model which is based on a new paradigm in similarity search, namely the use of vector set represented objects. In order to guarantee efficient query processing, suitable filters are introduced for accelerating similarity queries on complex spatial objects. Based on clustering and the introduced similarity models we present an industrial prototype which helps the user to navigate through massive data sets.Ein schneller und reibungsloser Entwicklungsprozess neuer Produkte ist ein wichtiger Faktor für den wirtschaftlichen Erfolg vieler Unternehmen insbesondere aus der Luft- und Raumfahrttechnik und der Automobilindustrie. Damit Ingenieure in immer kürzerer Zeit immer anspruchsvollere Produkte entwickeln können, werden effektive und effiziente Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten benötigt. Um den hohen Anforderungen eines produktiven Einsatzes zu genügen, müssen entsprechend spezialisierte Zugriffsmethoden in vollwertige Datenbanksysteme integriert werden, so dass zentrale Datenbankdienste wie Trans-aktionen, kontrollierte Nebenläufigkeit und Wiederanlauf sichergestellt sind. Ziel dieser Doktorarbeit ist es deshalb, effektive und effiziente Algorithmen für Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten zu ent-wickeln und diese in kommerzielle Objekt-Relationale Datenbanksysteme zu integrieren. Im ersten Teil der Arbeit werden verschiedene räumliche Indexstrukturen zur effizienten Bearbeitung von Kollisionsanfragen diskutiert und auf ihre Integrationsfähigkeit in Objekt-Relationale Datenbanksysteme hin untersucht. Daran an-knüpfend werden zwei generische Verfahren zur Beschleunigung von Kollisionsanfragen vorgestellt. Das erste Verfahren benutzt statistische Informationen räumlicher Indexstrukturen, um eine gegebene Anfrage zu beschleunigen. Das zweite Verfahren beruht auf einer kostenbasierten Zerlegung komplexer räumlicher Datenbank- Objekte. Diese beiden Verfahren ergänzen sich gegenseitig und können unabhängig voneinander oder zusammen eingesetzt werden. In einer ausführlichen experimentellen Evaluation wird gezeigt, dass die beiden vorgestellten Verfahren interaktive Kollisionsanfragen auf umfangreichen Datenmengen und komplexen Objekten ermöglichen. Im zweiten Teil der Arbeit werden verschiedene Ähnlichkeitsmodelle für räum-liche Objekte vorgestellt. Es wird experimentell aufgezeigt, dass datenpartitionierende Modelle effektiver sind als raumpartitionierende Verfahren. Weiterhin werden geeignete Filtertechniken zur Beschleunigung des Anfrageprozesses entwickelt und experimentell untersucht. Basierend auf Clustering und den entwickelten Ähnlichkeitsmodellen wird ein industrietauglicher Prototyp vorgestellt, der Benutzern hilft, durch große Datenmengen zu navigieren

The BGP Visibility Toolkit: detecting anomalous internet routing behavior

In this paper, we propose the BGP Visibility Toolkit, a system for detecting and analyzing anomalous behavior in the Internet. We show that interdomain prefix visibility can be used to single out cases of erroneous demeanors resulting from misconfiguration or bogus routing policies. The implementation of routing policies with BGP is a complicated process, involving fine-tuning operations and interactions with the policies of the other active ASes. Network operators might end up with faulty configurations or unintended routing policies that prevent the success of their strategies and impact their revenues. As part of the Visibility Toolkit, we propose the BGP Visibility Scanner, a tool which identifies limited visibility prefixes in the Internet. The tool enables operators to provide feedback on the expected visibility status of prefixes. We build a unique set of ground-truth prefixes qualified by their ASes as intended or unintended to have limited visibility. Using a machine learning algorithm, we train on this unique dataset an alarm system that separates with 95% accuracy the prefixes with unintended limited visibility. Hence, we find that visibility features are generally powerful to detect prefixes which are suffering from inadvertent effects of routing policies. Limited visibility could render a whole prefix globally unreachable. This points towards a serious problem, as limited reachability of a non-negligible set of prefixes undermines the global connectivity of the Internet. We thus verify the correlation between global visibility and global connectivity of prefixes.This work was sup-ported in part by the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant 317647 (Leone)