Redundant disk arrays: Reliable, parallel secondary storage

During the past decade, advances in processor and memory technology have given rise to increases in computational performance that far outstrip increases in the performance of secondary storage technology. Coupled with emerging small-disk technology, disk arrays provide the cost, volume, and capacity of current disk subsystems, by leveraging parallelism, many times their performance. Unfortunately, arrays of small disks may have much higher failure rates than the single large disks they replace. Redundant arrays of inexpensive disks (RAID) use simple redundancy schemes to provide high data reliability. The data encoding, performance, and reliability of redundant disk arrays are investigated. Organizing redundant data into a disk array is treated as a coding problem. Among alternatives examined, codes as simple as parity are shown to effectively correct single, self-identifying disk failures

Advanced management techniques for many-core communication systems

The way computer processors are built is changing. Nowadays, computer processor performance is increased by adding more processing cores on a single chip instead of making processors larger and faster. The traditional approach is no longer viable, due to limits in transistor scaling. Both industry and academia agree that scaling the number of processing cores to hundreds or thousands on a single chip is the only way to scale computer processor performance from now on. Consequently, the performance of these future many-core systems with thousands of cores will heavily depend on the Network-on-Chip (NoC) architecture to provide scalable communication. Therefore, as the number of cores increases the locality will only become more important. Communication locality is essential to reduce latency and increase performance. Many-core systems should be designed such that cores communicate mainly to the neighbouring cores, in order to minimise the communication cost. We investigate the network performance of different topologies using the ITRS physical data for the year 2023. For this reason, we propose abstract synthetic traffic generation models to explore the locality behaviour in many-core NoC systems. Using the synthetic traffic models - group clustering model and ring clustering model - traffic distance metrics may be adjusted with locality parameters. We choose two many-core NoC architectures - distributed memory architecture and shared memory architecture - to examine whether enforcing locality on different architectures may have a diverse effect on the network performance of different topologies. Distributed memory architecture uses the message passing method of communication to communicate between cores. Our results show that the degree of locality and the clustering model strongly affect the performance of the network. Scale-invariant topologies, such as the fat quadtree, perform worse than flat ones because the reduced hop count is outweighed by the longer wire delays. In shared memory architecture, threads communicate with each other by storing data in shared cache lines. We design a hierarchical cache model that benefits from communication locality because many-core cache hierarchy that fails to exploit locality may end up having more cores delayed, thereby decreasing the network performance. Our results show that the locality model of thread placement and the distance of placing them significantly affect the NoC performance. Furthermore, they show that scale-invariant topologies perform better than flat topologies. Then, we demonstrate that implementing directory-based cache coherency has only a small overhead on the cache size. Using cache coherency protocol in our proposed hierarchical cache model, we show that network performance decreases only slightly. Hence, cache coherency scales, and it is possible to have shared memory architecture with thousands of cores

Automated Storage Layout for Database Systems

Modern storage systems are complex. Simple direct-attached storage devices are giving way to storage systems that are flexible, network-attached, consolidated and virtualized. Today, storage systems have their own administrators, who use specialized tools and expertise to configure and manage storage resources. As a result, database administrators are no longer in direct control of the design and configuration of their database systems' underlying storage resources. This introduces problems because database physical design and storage configuration are closely related tasks, and the separation makes it more difficult to achieve a good end-to-end design. For instance, the performance of a database system depends strongly on the storage layout of database objects, such as tables and indexes, and the separation makes it hard to design a storage layout that is tuned to the I/O workload generated by the database system. In this thesis we address this problem and attempt to close the information gap between database and storage tiers by addressing the problem of predicting the storage (I/O) workload that will be generated by a database management system. Specifically, we show how to translate a database workload description, together with a database physical design, into a characterization of the I/O workload that will result. Such a characterization can directly be used by a storage configuration tool and thus enables effective end-to-end design and configuration spanning both the database and storage tiers. We then introduce our storage layout optimization tool, which leverages such workload characterizations to generate an optimized layout for a given set of database objects. We formulate the layout problem as a non-linear programming (NLP) problem and use the I/O characterization as input to an NLP solver. We have incorporated our I/O estimation technique into the PostgreSQL database management system and our layout optimization technique into a database layout advisor. We present an empirical assessment of the cost of both tools as well as the efficacy and accuracy of their results

Aspect-oriented technology for dependable operating systems

Modern computer devices exhibit transient hardware faults that disturb the electrical behavior but do not cause permanent physical damage to the devices. Transient faults are caused by a multitude of sources, such as fluctuation of the supply voltage, electromagnetic interference, and radiation from the natural environment. Therefore, dependable computer systems must incorporate methods of fault tolerance to cope with transient faults. Software-implemented fault tolerance represents a promising approach that does not need expensive hardware redundancy for reducing the probability of failure to an acceptable level. This thesis focuses on software-implemented fault tolerance for operating systems because they are the most critical pieces of software in a computer system: All computer programs depend on the integrity of the operating system. However, the C/C++ source code of common operating systems tends to be already exceedingly complex, so that a manual extension by fault tolerance is no viable solution. Thus, this thesis proposes a generic solution based on Aspect-Oriented Programming (AOP). To evaluate AOP as a means to improve the dependability of operating systems, this thesis presents the design and implementation of a library of aspect-oriented fault-tolerance mechanisms. These mechanisms constitute separate program modules that can be integrated automatically into common off-the-shelf operating systems using a compiler for the AOP language. Thus, the aspect-oriented approach facilitates improving the dependability of large-scale software systems without affecting the maintainability of the source code. The library allows choosing between several error-detection and error-correction schemes, and provides wait-free synchronization for handling asynchronous and multi-threaded operating-system code. This thesis evaluates the aspect-oriented approach to fault tolerance on the basis of two off-the-shelf operating systems. Furthermore, the evaluation also considers one user-level program for protection, as the library of fault-tolerance mechanisms is highly generic and transparent and, thus, not limited to operating systems. Exhaustive fault-injection experiments show an excellent trade-off between runtime overhead and fault tolerance, which can be adjusted and optimized by fine-grained selective placement of the fault-tolerance mechanisms. Finally, this thesis provides evidence for the effectiveness of the approach in detecting and correcting radiation-induced hardware faults: High-energy particle radiation experiments confirm improvements in fault tolerance by almost 80 percent

On Information-centric Resiliency and System-level Security in Constrained, Wireless Communication

The Internet of Things (IoT) interconnects many heterogeneous embedded devices either locally between each other, or globally with the Internet. These things are resource-constrained, e.g., powered by battery, and typically communicate via low-power and lossy wireless links. Communication needs to be secured and relies on crypto-operations that are often resource-intensive and in conflict with the device constraints. These challenging operational conditions on the cheapest hardware possible, the unreliable wireless transmission, and the need for protection against common threats of the inter-network, impose severe challenges to IoT networks. In this thesis, we advance the current state of the art in two dimensions. Part I assesses Information-centric networking (ICN) for the IoT, a network paradigm that promises enhanced reliability for data retrieval in constrained edge networks. ICN lacks a lower layer definition, which, however, is the key to enable device sleep cycles and exclusive wireless media access. This part of the thesis designs and evaluates an effective media access strategy for ICN to reduce the energy consumption and wireless interference on constrained IoT nodes. Part II examines the performance of hardware and software crypto-operations, executed on off-the-shelf IoT platforms. A novel system design enables the accessibility and auto-configuration of crypto-hardware through an operating system. One main focus is the generation of random numbers in the IoT. This part of the thesis further designs and evaluates Physical Unclonable Functions (PUFs) to provide novel randomness sources that generate highly unpredictable secrets, on low-cost devices that lack hardware-based security features. This thesis takes a practical view on the constrained IoT and is accompanied by real-world implementations and measurements. We contribute open source software, automation tools, a simulator, and reproducible measurement results from real IoT deployments using off-the-shelf hardware. The large-scale experiments in an open access testbed provide a direct starting point for future research

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Annotierte interaktive nichtlineare Videos - Software Suite, Download- und Cache-Management

Modern Web technology makes the dream of fully interactive and enriched video come true. Nowadays it is possible to organize videos in a non-linear way playing in a sequence unknown in advance. Furthermore, additional information can be added to the video, ranging from short descriptions to animated images and further videos. This affords an easy and efficient to use authoring tool which is capable of the management of the single media objects, as well as a clear arrangement of the links between the parts. Tools of this kind can be found rarely and do mostly not provide the full range of needed functions. While providing an interactive experience to the viewer in the Web player, parallel plot sequences and additional information lead to an increased download volume. This may cause pauses during playback while elements have to be downloaded which are displayed with the video. A good quality of experience for these videos with small waiting times and a playback without interruptions is desired. This work presents the SIVA Suite to create the previously described annotated interactive non-linear videos. We propose a video model for interactivity, non-linearity, and annotations, which is implemented in an XML format, an authoring tool, and a player. Video is the main medium, whereby different scenes are linked to a scene graph. Time controlled additional content called annotations, like text, images, audio files, or videos, is added to the scenes. The user is able to navigate in the scene graph by selecting a button at a button panel. Furthermore, other navigational elements like a table of contents or a keyword search are provided. Besides the SIVA Suite, this thesis presents algorithms and strategies for download and cache management to provide a good quality of experience while watching the annotated interactive non-linear videos. Therefor, we implemented a standard-independent player framework. Integrated into a simulation environment, the framework allows to evaluate algorithms and strategies for the calculation of start-up times, and the selection of elements to pre-fetch into and delete from the cache. Their interaction during the playback of non-linear video contents can be analyzed. The algorithms and strategies can be used to minimize interruptions in the video flow after user interactions. Our extensive evaluation showed that our techniques result in faster start-up times and lesser interruptions in the video flow than those of other players. Knowledge of the structure of an interactive non-linear video can be used to minimize the start-up time at the beginning of a video while minimizing an increase in the overall download volume.Moderne Web-Technologien lassen den Traum von voll interaktiven und bereicherten Videos wahr werden. Heutzutage ist es möglich, Videos in nicht-linearer Art und Weise zu organisieren, welche dann in einer vorher unbekannten Reihenfolge abgespielt werden können. Weiterhin können den Videos Zusatzinformationen in Form von kurzen Beschreibungen über animierte Bilder bis hin zu weiteren Videos hinzugefügt werden. Dies erfordert ein einfach und effizient zu bedienendes Autorenwerkzeug, das in der Lage ist, sowohl einzelne Medien-Objekte zu verwalten, als auch die Verbindungen zwischen den einzelnen Teilen klar darzustellen. Tools dieser Art sind selten und bieten meist nicht den vollen benötigten Funktionsumfang. Während dem Betrachter dieses interaktive Erlebnis im Web Player zur Verfügung gestellt wird, führen parallele Handlungsstränge und zusätzliche Inhalte zu einem erhöhten Download-Volumen. Dies kann zu Pausen während der Wiedergabe führen, in denen Elemente vom Server geladen werden müssen, welche mit dem Video angezeigt werden sollen. Ein gutes Benutzungserlebnis für solche Videos kann durch geringe Wartezeiten und eine unterbrechungsfreie Wiedergabe erreicht werden. Diese Arbeit stellt die SIVA Suite vor, mit der die zuvor beschriebenen annotierten interaktiven nicht-linearen Videos erstellt werden können. Wir bilden Interaktivität, Nichtlinearität und Annotationen in einem Video-Model ab. Dieses wird in unserem XML-Format, Autorentool und Player umgesetzt. Als Leitmedium werden hierbei Videos verwendet, welche aufgeteilt in Szenen zu einer Graphstruktur zusammengefügt werden können. Zeitlich gesteuerte zusätzliche Inhalte, sogenannte Annotationen, wie Texte, Bilder, Audio-Dateien und Videos, werden den Szenen hinzugefügt. Der Betrachter kann im Szenengraph navigieren, indem er in einem bereitgestellten Button-Panel eine Nachfolgeszene auswählt. Andere Navigationselemente sind ein Inhaltsverzeichnis sowie eine Suchfunktion. Neben der SIVA Suite beschreibt diese Arbeit Algorithmen und Strategien für Download und Cache Management, um eine gute Nutzungserfahrung während der Betrachtung der annotierten interaktiven nicht-linearen Videos zu bieten. Ein Webstandard-unabhängiges Playerframework erlaubt es, das Zusammenspiel von Algorithmen und Strategien zu evaluieren, welche für die Berechnung der Start-Zeitpunkte für die Wiedergabe, sowie die Auswahl von vorauszuladenden sowie zu löschenden Elemente verwendet werden. Ziel ist es, Unterbrechungen zu minimieren, wenn der Ablauf des Videos durch Benutzerinteraktion beeinflusst wird. Unsere umfassende Evaluation zeigte, dass es möglich ist, kürzere Startup-Zeiten und weniger Unterbrechungen mit unseren Strategien zu erreichen, als bei der Verwendung der Strategien anderer Player. Die Kenntnis der Struktur des interaktiven nicht-linearen Videos kann dazu verwendet werden, die Startzeit am Anfang der Szenen zu minimieren, während das Download-Volumen nicht erhöht wird

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research