Compressing and Performing Algorithms on Massively Large Networks

Networks are represented as a set of nodes (vertices) and the arcs (links) connecting them. Such networks can model various real-world structures such as social networks (e.g., Facebook), information networks (e.g., citation networks), technological networks (e.g., the Internet), and biological networks (e.g., gene-phenotype network). Analysis of such structures is a heavily studied area with many applications. However, in this era of big data, we find ourselves with networks so massive that the space requirements inhibit network analysis. Since many of these networks have nodes and arcs on the order of billions to trillions, even basic data structures such as adjacency lists could cost petabytes to zettabytes of storage. Storing these networks in secondary memory would require I/O access (i.e., disk access) during analysis, thus drastically slowing analysis time. To perform analysis efficiently on such extensive data, we either need enough main memory for the data structures and algorithms, or we need to develop compressions that require much less space while still being able to answer queries efficiently. In this dissertation, we develop several compression techniques that succinctly represent these real-world networks while still being able to efficiently query the network (e.g., check if an arc exists between two nodes). Furthermore, since many of these networks continue to grow over time, our compression techniques also support the ability to add and remove nodes and edges directly on the compressed structure. We also provide a way to compress the data quickly without any intermediate structure, thus giving minimal memory overhead. We provide detailed analysis and prove that our compression is indeed succinct (i.e., achieves the information-theoretic lower bound). Also, we empirically show that our compression rates outperform or are equal to existing compression algorithms on many benchmark datasets. We also extend our technique to time-evolving networks. That is, we store the entire state of the network at each time frame. Studying time-evolving networks allows us to find patterns throughout the time that would not be available in regular, static network analysis. A succinct representation for time-evolving networks is arguably more important than static graphs, due to the extra dimension inflating the space requirements of basic data structures even more. Again, we manage to achieve succinctness while also providing fast encoding, minimal memory overhead during encoding, fast queries, and fast, direct modification. We also compare against several benchmarks and empirically show that we achieve compression rates better than or equal to the best performing benchmark for each dataset. Finally, we also develop both static and time-evolving algorithms that run directly on our compressed structures. Using our static graph compression combined with our differential technique, we find that we can speed up matrix-vector multiplication by reusing previously computed products. We compare our results against a similar technique using the Webgraph Framework, and we see that not only are our base query speeds faster, but we also gain a more significant speed-up from reusing products. Then, we use our time-evolving compression to solve the earliest arrival paths problem and time-evolving transitive closure. We found that not only were we the first to run such algorithms directly on compressed data, but that our technique was particularly efficient at doing so

Compression techniques for extreme-scale graphs and matrices: sequential and parallel algorithms

A graph G = (V, E) is an ordered tuple where V is a non-empty set of elements called vertices (nodes), and E is a set of an unordered pair of elements called links (edges), and a time-evolving graph is a change in the states of the edges over time. With the growing popularity of social networks and the massive influx of users, it is becoming a challenging task to store the network/graph and process them as fast as possible before the property of the graph changes with the graph evolution. Graphs or networks are a collection of entities (individuals in a social network) and their relationships (friends, followers); ways to represent a graph can help how the information could be extracted. The increase in the number of users increases the relationship the user has, which makes the graphs massive and nearly impossible to store them in friendly structures such as a matrix or an adjacency list. Therefore, an exciting area of research is storing these massive graphs with a smaller memory footprint and processing with very little extra memory. But there is always a trade-off with time and space; to get a small memory footprint, one has to remove the redundancy rigorously, which consumes time. In the same way, when traversing these tight spaces, the time required to query also increases compared to a matrix or an adjacency list. In this dissertation, we provide the encoding technique to store the arrays in the Compressed Sparse Row (CSR) data structure and extend the encoding to store time-evolving graphs in the form of a CSR. We also propose combinations of two structures (CSR + CBT) to store the time-evolving graphs and to improve the time and space trade-off. Encoding also enables one to access a node without decompressing the entire structure, which means that the data structure can be accessed. We then provide four ways to store multi-dimensional data, which represents intricate relations within the social network. Once the data are stored in compressed format, it is important to provide algorithms that support the structures. One such computation which is the basis for any graph algorithm is matrix-multiplication. We now extend our work to perform value-based matrix multiplication on compressed structures. We test our algorithm on extremely large matrices, in the order of 100s of millions with various levels of sparsity. Using matrix-matrix multiplication and keeping the theme of storing the data in small spaces, we propose another way of compression is through the dimensionality reduction, which is referred to as Matrix Factorization. Performing any of these operations on a compressed structure without decompressing would be time consuming. Therefore, in this dissertation, we introduce a parallel technique to construct the graph and also run a list of queries using the querying algorithms, such as fetching neighbor or edge existence in parallel. We also extend our work to propose parallel time-evolving differential compression of CSR using the prefix sum approach

Incremental Lossless Graph Summarization

Given a fully dynamic graph, represented as a stream of edge insertions and deletions, how can we obtain and incrementally update a lossless summary of its current snapshot? As large-scale graphs are prevalent, concisely representing them is inevitable for efficient storage and analysis. Lossless graph summarization is an effective graph-compression technique with many desirable properties. It aims to compactly represent the input graph as (a) a summary graph consisting of supernodes (i.e., sets of nodes) and superedges (i.e., edges between supernodes), which provide a rough description, and (b) edge corrections which fix errors induced by the rough description. While a number of batch algorithms, suited for static graphs, have been developed for rapid and compact graph summarization, they are highly inefficient in terms of time and space for dynamic graphs, which are common in practice. In this work, we propose MoSSo, the first incremental algorithm for lossless summarization of fully dynamic graphs. In response to each change in the input graph, MoSSo updates the output representation by repeatedly moving nodes among supernodes. MoSSo decides nodes to be moved and their destinations carefully but rapidly based on several novel ideas. Through extensive experiments on 10 real graphs, we show MoSSo is (a) Fast and 'any time': processing each change in near-constant time (less than 0.1 millisecond), up to 7 orders of magnitude faster than running state-of-the-art batch methods, (b) Scalable: summarizing graphs with hundreds of millions of edges, requiring sub-linear memory during the process, and (c) Effective: achieving comparable compression ratios even to state-of-the-art batch methods.Comment: to appear at the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '20

Cloud Based IoT Architecture

The Internet of Things (IoT) and cloud computing have grown in popularity over the past decade as the internet becomes faster and more ubiquitous. Cloud platforms are well suited to handle IoT systems as they are accessible and resilient, and they provide a scalable solution to store and analyze large amounts of IoT data. IoT applications are complex software systems and software developers need to have a thorough understanding of the capabilities, limitations, architecture, and design patterns of cloud platforms and cloud-based IoT tools to build an efficient, maintainable, and customizable IoT application. As the IoT landscape is constantly changing, research into cloud-based IoT platforms is either lacking or out of date. The goal of this thesis is to describe the basic components and requirements for a cloud-based IoT platform, to provide useful insights and experiences in implementing a cloud-based IoT solution using Microsoft Azure, and to discuss some of the shortcomings when combining IoT with a cloud platform

Transactional and analytical data management on persistent memory

Die zunehmende Anzahl von Smart-Geräten und Sensoren, aber auch die sozialen Medien lassen das Datenvolumen und damit die geforderte Verarbeitungsgeschwindigkeit stetig wachsen. Gleichzeitig müssen viele Anwendungen Daten persistent speichern oder sogar strenge Transaktionsgarantien einhalten. Die neuartige Speichertechnologie Persistent Memory (PMem) mit ihren einzigartigen Eigenschaften scheint ein natürlicher Anwärter zu sein, um diesen Anforderungen effizient nachzukommen. Sie ist im Vergleich zu DRAM skalierbarer, günstiger und dauerhaft. Im Gegensatz zu Disks ist sie deutlich schneller und direkt adressierbar. Daher wird in dieser Dissertation der gezielte Einsatz von PMem untersucht, um den Anforderungen moderner Anwendung gerecht zu werden. Nach der Darlegung der grundlegenden Arbeitsweise von und mit PMem, konzentrieren wir uns primär auf drei Aspekte der Datenverwaltung. Zunächst zerlegen wir mehrere persistente Daten- und Indexstrukturen in ihre zugrundeliegenden Entwurfsprimitive, um Abwägungen für verschiedene Zugriffsmuster aufzuzeigen. So können wir ihre besten Anwendungsfälle und Schwachstellen, aber auch allgemeine Erkenntnisse über das Entwerfen von PMem-basierten Datenstrukturen ermitteln. Zweitens schlagen wir zwei Speicherlayouts vor, die auf analytische Arbeitslasten abzielen und eine effiziente Abfrageausführung auf beliebigen Attributen ermöglichen. Während der erste Ansatz eine verknüpfte Liste von mehrdimensionalen gruppierten Blöcken verwendet, handelt es sich beim zweiten Ansatz um einen mehrdimensionalen Index, der Knoten im DRAM zwischenspeichert. Drittens zeigen wir unter Verwendung der bisherigen Datenstrukturen und Erkenntnisse, wie Datenstrom- und Ereignisverarbeitungssysteme mit transaktionaler Zustandsverwaltung verbessert werden können. Dabei schlagen wir ein neuartiges Transactional Stream Processing (TSP) Modell mit geeigneten Konsistenz- und Nebenläufigkeitsprotokollen vor, die an PMem angepasst sind. Zusammen sollen die diskutierten Aspekte eine Grundlage für die Entwicklung noch ausgereifterer PMem-fähiger Systeme bilden. Gleichzeitig zeigen sie, wie Datenverwaltungsaufgaben PMem ausnutzen können, indem sie neue Anwendungsgebiete erschließen, die Leistung, Skalierbarkeit und Wiederherstellungsgarantien verbessern, die Codekomplexität vereinfachen sowie die ökonomischen und ökologischen Kosten reduzieren.The increasing number of smart devices and sensors, but also social media are causing the volume of data and thus the demanded processing speed to grow steadily. At the same time, many applications need to store data persistently or even comply with strict transactional guarantees. The novel storage technology Persistent Memory (PMem), with its unique properties, seems to be a natural candidate to meet these requirements efficiently. Compared to DRAM, it is more scalable, less expensive, and durable. In contrast to disks, it is significantly faster and directly addressable. Therefore, this dissertation investigates the deliberate employment of PMem to fit the needs of modern applications. After presenting the fundamental work of and with PMem, we focus primarily on three aspects of data management. First, we disassemble several persistent data and index structures into their underlying design primitives to reveal the trade-offs for various access patterns. It allows us to identify their best use cases and vulnerabilities but also to gain general insights into the design of PMem-based data structures. Second, we propose two storage layouts that target analytical workloads and enable an efficient query execution on arbitrary attributes. While the first approach employs a linked list of multi-dimensional clustered blocks that potentially span several storage layers, the second approach is a multi-dimensional index that caches nodes in DRAM. Third, we show how to improve stream and event processing systems involving transactional state management using the preceding data structures and insights. In this context, we propose a novel Transactional Stream Processing (TSP) model with appropriate consistency and concurrency protocols adapted to PMem. Together, the discussed aspects are intended to provide a foundation for developing even more sophisticated PMemenabled systems. At the same time, they show how data management tasks can take advantage of PMem by opening up new application domains, improving performance, scalability, and recovery guarantees, simplifying code complexity, plus reducing economic and environmental costs

Arquitectura, técnicas y modelos para posibilitar la Ciencia de Datos en el Archivo de la Misión Gaia

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/05/2017.The massive amounts of data that the world produces every day pose new challenges to modern societies in terms of how to leverage their inherent value. Social networks, instant messaging, video, smart devices and scientific missions are just mere examples of the vast number of sources generating data every second. As the world becomes more and more digitalized, new needs arise for organizing, archiving, sharing, analyzing, visualizing and protecting the ever-increasing data sets, so that we can truly develop into a data-driven economy that reduces inefficiencies and increases sustainability, creating new business opportunities on the way. Traditional approaches for harnessing data are not suitable any more as they lack the means for scaling to the larger volumes in a timely and cost efficient manner. This has somehow changed with the advent of Internet companies like Google and Facebook, which have devised new ways of tackling this issue. However, the variety and complexity of the value chains in the private sector as well as the increasing demands and constraints in which the public one operates, needs an ongoing research that can yield newer strategies for dealing with data, facilitate the integration of providers and consumers of information, and guarantee a smooth and prompt transition when adopting these cutting-edge technological advances. This thesis aims at providing novel architectures and techniques that will help perform this transition towards Big Data in massive scientific archives. It highlights the common pitfalls that must be faced when embracing it and how to overcome them, especially when the data sets, their transformation pipelines and the tools used for the analysis are already present in the organizations. Furthermore, a new perspective for facilitating a smoother transition is laid out. It involves the usage of higher-level and use case specific frameworks and models, which will naturally bridge the gap between the technological and scientific domains. This alternative will effectively widen the possibilities of scientific archives and therefore will contribute to the reduction of the time to science. The research will be applied to the European Space Agency cornerstone mission Gaia, whose final data archive will represent a tremendous discovery potential. It will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), providing unprecedented position, parallax and proper motion measurements for about one billion stars. The successful exploitation of this data archive will depend to a large degree on the ability to offer the proper architecture, i.e. infrastructure and middleware, upon which scientists will be able to do exploration and modeling with this huge data set. In consequence, the approach taken needs to enable data fusion with other scientific archives, as this will produce the synergies leading to an increment in scientific outcome, both in volume and in quality. The set of novel techniques and frameworks presented in this work addresses these issues by contextualizing them with the data products that will be generated in the Gaia mission. All these considerations have led to the foundations of the architecture that will be leveraged by the Science Enabling Applications Work Package. Last but not least, the effectiveness of the proposed solution will be demonstrated through the implementation of some ambitious statistical problems that will require significant computational capabilities, and which will use Gaia-like simulated data (the first Gaia data release has recently taken place on September 14th, 2016). These ambitious problems will be referred to as the Grand Challenge, a somewhat grandiloquent name that consists in inferring a set of parameters from a probabilistic point of view for the Initial Mass Function (IMF) and Star Formation Rate (SFR) of a given set of stars (with a huge sample size), from noisy estimates of their masses and ages respectively. This will be achieved by using Hierarchical Bayesian Modeling (HBM). In principle, the HBM can incorporate stellar evolution models to infer the IMF and SFR directly, but in this first step presented in this thesis, we will start with a somewhat less ambitious goal: inferring the PDMF and PDAD. Moreover, the performance and scalability analyses carried out will also prove the suitability of the models for the large amounts of data that will be available in the Gaia data archive.Las grandes cantidades de datos que se producen en el mundo diariamente plantean nuevos retos a la sociedad en términos de cómo extraer su valor inherente. Las redes sociales, mensajería instantánea, los dispositivos inteligentes y las misiones científicas son meros ejemplos del gran número de fuentes generando datos en cada momento. Al mismo tiempo que el mundo se digitaliza cada vez más, aparecen nuevas necesidades para organizar, archivar, compartir, analizar, visualizar y proteger la creciente cantidad de datos, para que podamos desarrollar economías basadas en datos e información que sean capaces de reducir las ineficiencias e incrementar la sostenibilidad, creando nuevas oportunidades de negocio por el camino. La forma en la que se han manejado los datos tradicionalmente no es la adecuada hoy en día, ya que carece de los medios para escalar a los volúmenes más grandes de datos de una forma oportuna y eficiente. Esto ha cambiado de alguna manera con la llegada de compañías que operan en Internet como Google o Facebook, ya que han concebido nuevas aproximaciones para abordar el problema. Sin embargo, la variedad y complejidad de las cadenas de valor en el sector privado y las crecientes demandas y limitaciones en las que el sector público opera, necesitan una investigación continua en la materia que pueda proporcionar nuevas estrategias para procesar las enormes cantidades de datos, facilitar la integración de productores y consumidores de información, y garantizar una transición rápida y fluida a la hora de adoptar estos avances tecnológicos innovadores. Esta tesis tiene como objetivo proporcionar nuevas arquitecturas y técnicas que ayudarán a realizar esta transición hacia Big Data en archivos científicos masivos. La investigación destaca los escollos principales a encarar cuando se adoptan estas nuevas tecnologías y cómo afrontarlos, principalmente cuando los datos y las herramientas de transformación utilizadas en el análisis existen en la organización. Además, se exponen nuevas medidas para facilitar una transición más fluida. Éstas incluyen la utilización de software de alto nivel y específico al caso de uso en cuestión, que haga de puente entre el dominio científico y tecnológico. Esta alternativa ampliará de una forma efectiva las posibilidades de los archivos científicos y por tanto contribuirá a la reducción del tiempo necesario para generar resultados científicos a partir de los datos recogidos en las misiones de astronomía espacial y planetaria. La investigación se aplicará a la misión de la Agencia Espacial Europea (ESA) Gaia, cuyo archivo final de datos presentará un gran potencial para el descubrimiento y hallazgo desde el punto de vista científico. La misión creará el catálogo en tres dimensiones más grande y preciso de nuestra galaxia (la Vía Láctea), proporcionando medidas sin precedente acerca del posicionamiento, paralaje y movimiento propio de alrededor de mil millones de estrellas. Las oportunidades para la explotación exitosa de este archivo de datos dependerán en gran medida de la capacidad de ofrecer la arquitectura adecuada, es decir infraestructura y servicios, sobre la cual los científicos puedan realizar la exploración y modelado con esta inmensa cantidad de datos. Por tanto, la estrategia a realizar debe ser capaz de combinar los datos con otros archivos científicos, ya que esto producirá sinergias que contribuirán a un incremento en la ciencia producida, tanto en volumen como en calidad de la misma. El conjunto de técnicas e infraestructuras innovadoras presentadas en este trabajo aborda estos problemas, contextualizándolos con los productos de datos que se generarán en la misión Gaia. Todas estas consideraciones han conducido a los fundamentos de la arquitectura que se utilizará en el paquete de trabajo de aplicaciones que posibilitarán la ciencia en el archivo de la misión Gaia (Science Enabling Applications). Por último, la eficacia de la solución propuesta se demostrará a través de la implementación de dos problemas estadísticos que requerirán cantidades significativas de cómputo, y que usarán datos simulados en el mismo formato en el que se producirán en el archivo de la misión Gaia (la primera versión de datos recogidos por la misión está disponible desde el día 14 de Septiembre de 2016). Estos ambiciosos problemas representan el Gran Reto (Grand Challenge), un nombre grandilocuente que consiste en inferir una serie de parámetros desde un punto de vista probabilístico para la función de masa inicial (Initial Mass Function) y la tasa de formación estelar (Star Formation Rate) dado un conjunto de estrellas (con una muestra grande), desde estimaciones con ruido de sus masas y edades respectivamente. Esto se abordará utilizando modelos jerárquicos bayesianos (Hierarchical Bayesian Modeling). Enprincipio,losmodelospropuestos pueden incorporar otros modelos de evolución estelar para inferir directamente la función de masa inicial y la tasa de formación estelar, pero en este primer paso presentado en esta tesis, empezaremos con un objetivo algo menos ambicioso: la inferencia de la función de masa y distribución de edades actual (Present-Day Mass Function y Present-Day Age Distribution respectivamente). Además, se llevará a cabo el análisis de rendimiento y escalabilidad para probar la idoneidad de la implementación de dichos modelos dadas las enormes cantidades de datos que estarán disponibles en el archivo de la misión Gaia...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Doctor of Philosophy

dissertationLinked data are the de-facto standard in publishing and sharing data on the web. To date, we have been inundated with large amounts of ever-increasing linked data in constantly evolving structures. The proliferation of the data and the need to access and harvest knowledge from distributed data sources motivate us to revisit several classic problems in query processing and query optimization. The problem of answering queries over views is commonly encountered in a number of settings, including while enforcing security policies to access linked data, or when integrating data from disparate sources. We approach this problem by efficiently rewriting queries over the views to equivalent queries over the underlying linked data, thus avoiding the costs entailed by view materialization and maintenance. An outstanding problem of query rewriting is the number of rewritten queries is exponential to the size of the query and the views, which motivates us to study problem of multiquery optimization in the context of linked data. Our solutions are declarative and make no assumption for the underlying storage, i.e., being store-independent. Unlike relational and XML data, linked data are schema-less. While tracking the evolution of schema for linked data is hard, keyword search is an ideal tool to perform data integration. Existing works make crippling assumptions for the data and hence fall short in handling massive linked data with tens to hundreds of millions of facts. Our study for keyword search on linked data brought together the classical techniques in the literature and our novel ideas, which leads to much better query efficiency and quality of the results. Linked data also contain rich temporal semantics. To cope with the ever-increasing data, we have investigated how to partition and store large temporal or multiversion linked data for distributed and parallel computation, in an effort to achieve load-balancing to support scalable data analytics for massive linked data

QOE-AWARE CONTENT DISTRIBUTION SYSTEMS FOR ADAPTIVE BITRATE VIDEO STREAMING

A prodigious increase in video streaming content along with a simultaneous rise in end system capabilities has led to the proliferation of adaptive bit rate video streaming users in the Internet. Today, video streaming services range from Video-on-Demand services like traditional IP TV to more recent technologies such as immersive 3D experiences for live sports events. In order to meet the demands of these services, the multimedia and networking research community continues to strive toward efficiently delivering high quality content across the Internet while also trying to minimize content storage and delivery costs. The introduction of flexible and adaptable technologies such as compute and storage clouds, Network Function Virtualization and Software Defined Networking continue to fuel content provider revenue. Today, content providers such as Google and Facebook build their own Software-Defined WANs to efficiently serve millions of users worldwide, while NetFlix partners with ISPs such as ATT (using OpenConnect) and cloud providers such as Amazon EC2 to serve their content and manage the delivery of several petabytes of high-quality video content for millions of subscribers at a global scale, respectively. In recent years, the unprecedented growth of video traffic in the Internet has seen several innovative systems such as Software Defined Networks and Information Centric Networks as well as inventive protocols such as QUIC, in an effort to keep up with the effects of this remarkable growth. While most existing systems continue to sub-optimally satisfy user requirements, future video streaming systems will require optimal management of storage and bandwidth resources that are several orders of magnitude larger than what is implemented today. Moreover, Quality-of-Experience metrics are becoming increasingly fine-grained in order to accurately quantify diverse content and consumer needs. In this dissertation, we design and investigate innovative adaptive bit rate video streaming systems and analyze the implications of recent technologies on traditional streaming approaches using real-world experimentation methods. We provide useful insights for current and future content distribution network administrators to tackle Quality-of-Experience dilemmas and serve high quality video content to several users at a global scale. In order to show how Quality-of-Experience can benefit from core network architectural modifications, we design and evaluate prototypes for video streaming in Information Centric Networks and Software-Defined Networks. We also present a real-world, in-depth analysis of adaptive bitrate video streaming over protocols such as QUIC and MPQUIC to show how end-to-end protocol innovation can contribute to substantial Quality-of-Experience benefits for adaptive bit rate video streaming systems. We investigate a cross-layer approach based on QUIC and observe that application layer-based information can be successfully used to determine transport layer parameters for ABR streaming applications