Strong Memory Consistency For Parallel Programming

Correctly synchronizing multithreaded programs is challenging, and errors can lead to program failures (e.g., atomicity violations). Existing memory consistency models rule out some possible failures, but are limited by depending on subtle programmer-defined locking code and by providing unintuitive semantics for incorrectly synchronized code. Stronger memory consistency models assist programmers by providing them with easier-to-understand semantics with regard to memory access interleavings in parallel code. This dissertation proposes a new strong memory consistency model based on ordering-free regions (OFRs), which are spans of dynamic instructions between consecutive ordering constructs (e.g. barriers). Atomicity over ordering-free regions provides stronger atomicity than existing strong memory consistency models with competitive performance. Ordering-free regions also simplify programmer reasoning by limiting the potential for atomicity violations to fewer points in the program’s execution. This dissertation explores both software-only and hardware-supported systems that provide OFR serializability

From Controlled Data-Center Environments to Open Distributed Environments: Scalable, Efficient, and Robust Systems with Extended Functionality

The past two decades have witnessed several paradigm shifts in computing environments. Starting from cloud computing which offers on-demand allocation of storage, network, compute, and memory resources, as well as other services, in a pay-as-you-go billingmodel. Ending with the rise of permissionless blockchain technology, a decentralized computing paradigm with lower trust assumptions and limitless number of participants. Unlike in the cloud, where all the computing resources are owned by some trusted cloud provider, permissionless blockchains allow computing resources owned by possibly malicious parties to join and leave their network without obtaining permission from some centralized trusted authority. Still, in the presence of malicious parties, permissionlessblockchain networks can perform general computations and make progress. Cloud computing is powered by geographically distributed data-centers controlled and managed by trusted cloud service providers and promises theoretically infinite computing resources. On the other hand, permissionless blockchains are powered by open networks of geographically distributed computing nodes owned by entities that are not necessarily known or trusted. This paradigm shift requires a reconsideration of distributed data management protocols and distributed system designs that assume low latency across system components, inelastic computing resources, or fully trusted computing resources.In this dissertation, we propose new system designs and optimizations that address scalability and efficiency of distributed data management systems in cloud environments. We also propose several protocols and new programming paradigms to extend the functionality and enhance the robustness of permissionless blockchains. The work presented spans global-scale transaction processing, large-scale stream processing, atomic transaction processing across permissionless blockchains, and extending the functionality and the use-cases of permissionless blockchains. In all these directions, the focus is on rethinking system and protocol designs to account for novel cloud and permissionless blockchain assumptions. For global-scale transaction processing, we propose GPlacer, a placement optimization framework that decides replica placement of fully and partial geo-replicated databases. For large-scale stream processing, we propose Cache-on-Track (CoT) an adaptive and elastic client-side cache that addresses server-side load-imbalances that occur in large-scale distributed storage layers. In permissionless blockchain transaction processing, we propose AC3WN, the first correct cross-chain commitment protocol that guarantees atomicity of cross-chain transactions. Also, we propose TXSC, a transactional smart contract programming framework. TXSC provides smart contract developers with transaction primitives. These primitives allow developers to write smart contracts without the need to reason about the anomalies that can arise due to concurrent smart contract function executions. In addition, we propose a forward-looking architecture that unifies both permissioned and permissionless blockchains and exploits the running infrastructure of permissionless blockchains to build global asset management systems

Database server workload characterization in an e-commerce environment

A typical E-commerce system that is deployed on the Internet has multiple layers that include Web users, Web servers, application servers, and a database server. As the system use and user request frequency increase, Web/application servers can be scaled up by replication. A load balancing proxy can be used to route user requests to individual machines that perform the same functionality. To address the increasing workload while avoiding replicating the database server, various dynamic caching policies have been proposed to reduce the database workload in E-commerce systems. However, the nature of the changes seen by the database server as a result of dynamic caching remains unknown. A good understanding of this change is fundamental for tuning a database server to get better performance. In this study, the TPC-W (a transactional Web E-commerce benchmark) workloads on a database server are characterized under two different dynamic caching mechanisms, which are generalized and implemented as query-result cache and table cache. The characterization focuses on response time, CPU computation, buffer pool references, disk I/O references, and workload classification. This thesis combines a variety of analysis techniques: simulation, real time measurement and data mining. The experimental results in this thesis reveal some interesting effects that the dynamic caching has on the database server workload characteristics. The main observations include: (a) dynamic cache can considerably reduce the CPU usage of the database server and the number of database page references when it is heavily loaded; (b) dynamic cache can also reduce the database reference locality, but to a smaller degree than that reported in file servers. The data classification results in this thesis show that with dynamic cache, the database server sees TPC-W profiles more like on-line transaction processing workloads

How To Touch a Running System

The increasing importance of distributed and decentralized software architectures entails more and more attention for adaptive software. Obtaining adaptiveness, however, is a difficult task as the software design needs to foresee and cope with a variety of situations. Using reconfiguration of components facilitates this task, as the adaptivity is conducted on an architecture level instead of directly in the code. This results in a separation of concerns; the appropriate reconfiguration can be devised on a coarse level, while the implementation of the components can remain largely unaware of reconfiguration scenarios. We study reconfiguration in component frameworks based on formal theory. We first discuss programming with components, exemplified with the development of the cmc model checker. This highly efficient model checker is made of C++ components and serves as an example for component-based software development practice in general, and also provides insights into the principles of adaptivity. However, the component model focuses on high performance and is not geared towards using the structuring principle of components for controlled reconfiguration. We thus complement this highly optimized model by a message passing-based component model which takes reconfigurability to be its central principle. Supporting reconfiguration in a framework is about alleviating the programmer from caring about the peculiarities as much as possible. We utilize the formal description of the component model to provide an algorithm for reconfiguration that retains as much flexibility as possible, while avoiding most problems that arise due to concurrency. This algorithm is embedded in a general four-stage adaptivity model inspired by physical control loops. The reconfiguration is devised to work with stateful components, retaining their data and unprocessed messages. Reconfiguration plans, which are provided with a formal semantics, form the input of the reconfiguration algorithm. We show that the algorithm achieves perceived atomicity of the reconfiguration process for an important class of plans, i.e., the whole process of reconfiguration is perceived as one atomic step, while minimizing the use of blocking of components. We illustrate the applicability of our approach to reconfiguration by providing several examples like fault-tolerance and automated resource control

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

User modeling servers - requirements, design, and evaluation

Softwaresysteme, die ihre Services an Charakteristika individueller Benutzer anpassen haben sich bereits als effektiver und/oder benutzerfreundlicher als statische Systeme in mehreren Anwendungsdomänen erwiesen. Um solche Anpassungsleistungen anbieten zu können, greifen benutzeradaptive Systeme auf Modelle von Benutzercharakteristika zurück. Der Aufbau und die Verwaltung dieser Modelle wird durch dezidierte Benutzermodellierungskomponenten vorgenommen. Ein wichtiger Zweig der Benutzermodellierungsforschung beschäftigt sich mit der Entwicklung sogenannter ?Benutzermodellierungs-Shells?, d.h. generischen Benutzermodellierungssystemen, die die Entwicklung anwendungsspezifischer Benutzermodellierungskomponenten erleichtern. Die Bestimmung des Leistungsumfangs dieser generischen Benutzermodellierungssysteme und deren Dienste bzw. Funktionalitäten wurde bisher in den meisten Fällen intuitiv vorgenommen und/oder aus Beschreibungen weniger benutzeradaptiver Systeme in der Literatur abgeleitet. In der jüngeren Vergangenheit führte der Trend zur Personalisierung im World Wide Web zur Entwicklung mehrerer kommerzieller Benutzermodellierungsserver. Die für diese Systeme als wichtig erachteten Eigenschaften stehen im krassen Gegensatz zu denen, die bei der Entwicklung der Benutzermodellierungs-Shells im Vordergrund standen und umgekehrt. Vor diesem Hintergrund ist das Ziel dieser Dissertation (i) Anforderungen an Benutzermodellierungsserver aus einer multi-disziplinären wissenschaftlichen und einer einsatzorientierten (kommerziellen) Perspektive zu analysieren, (ii) einen Server zu entwerfen und zu implementieren, der diesen Anforderungen genügt, und (iii) die Performanz und Skalierbarkeit dieses Servers unter der Arbeitslast kleinerer und mittlerer Einsatzumgebungen gegen die diesbezüglichen Anforderungen zu überprüfen. Um dieses Ziel zu erreichen, verfolgen wir einen anforderungszentrierten Ansatz, der auf Erfahrungen aus verschiedenen Forschungsbereichen aufbaut. Wir entwickeln eine generische Architektur für einen Benutzermodellierungsserver, die aus einem Serverkern für das Datenmanagement und modular hinzufügbaren Benutzermodellierungskomponenten besteht, von denen jede eine wichtige Benutzermodellierungstechnik implementiert. Wir zeigen, dass wir durch die Integration dieser Benutzermodellierungskomponenten in einem Server Synergieeffekte zwischen den eingesetzten Lerntechniken erzielen und bekannte Defizite einzelner Verfahren kompensieren können, beispielsweise bezüglich Performanz, Skalierbarkeit, Integration von Domänenwissen, Datenmangel und Kaltstart. Abschließend präsentieren wir die wichtigsten Ergebnisse der Experimente, die wir durchgeführt haben um empirisch nachzuweisen, dass der von uns entwickelte Benutzermodellierungsserver zentralen Performanz- und Skalierbarkeitskriterien genügt. Wir zeigen, dass unser Benutzermodellierungsserver die vorbesagten Kriterien in Anwendungsumgebungen mit kleiner und mittlerer Arbeitslast in vollem Umfang erfüllt. Ein Test in einer Anwendungsumgebung mit mehreren Millionen Benutzerprofilen und einer Arbeitslast, die als repräsentativ für größere Web Sites angesehen werden kann bestätigte, dass die Performanz der Benutzermodellierung unseres Servers keine signifikante Mehrbelastung für eine personalisierte Web Site darstellt. Gleichzeitig können die Anforderungen an die verfügbare Hardware als moderat eingestuft werden

Adaptive Big Data Pipeline

Over the past three decades, data has exponentially evolved from being a simple software by-product to one of the most important companies’ assets used to understand their customers and foresee trends. Deep learning has demonstrated that big volumes of clean data generally provide more flexibility and accuracy when modeling a phenomenon. However, handling ever-increasing data volumes entail new challenges: the lack of expertise to select the appropriate big data tools for the processing pipelines, as well as the speed at which engineers can take such pipelines into production reliably, leveraging the cloud. We introduce a system called Adaptive Big Data Pipelines: a platform to automate data pipelines creation. It provides an interface to capture the data sources, transformations, destinations and execution schedule. The system builds up the cloud infrastructure, schedules and fine-tunes the transformations, and creates the data lineage graph. This system has been tested on data sets of 50 gigabytes, processing them in just a few minutes without user intervention.ITESO, A. C

Engineering Automation for Reliable Software Interim Progress Report (10/01/2000 - 09/30/2001)

Prepared for: U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211The objective of our effort is to develop a scientific basis for producing reliable software that is also flexible and cost effective for the DoD distributed software domain. This objective addresses the long term goals of increasing the quality of service provided by complex systems while reducing development risks, costs, and time. Our work focuses on "wrap and glue" technology based on a domain specific distributed prototype model. The key to making the proposed approach reliable, flexible, and cost-effective is the automatic generation of glue and wrappers based on a designer's specification. The "wrap and glue" approach allows system designers to concentrate on the difficult interoperability problems and defines solutions in terms of deeper and more difficult interoperability issues, while freeing designers from implementation details. Specific research areas for the proposed effort include technology enabling rapid prototyping, inference for design checking, automatic program generation, distributed real-time scheduling, wrapper and glue technology, and reliability assessment and improvement. The proposed technology will be integrated with past research results to enable a quantum leap forward in the state of the art for rapid prototyping.U. S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-22110473-MA-SPApproved for public release; distribution is unlimited