PiCo: A Domain-Specific Language for Data Analytics Pipelines

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models—for which only informal (and often confusing) semantics is generally provided—all share a common under- lying model, namely, the Dataflow model. Using this model as a starting point, it is possible to categorize and analyze almost all aspects about Big Data analytics tools from a high level perspective. This analysis can be considered as a first step toward a formal model to be exploited in the design of a (new) framework for Big Data analytics. By putting clear separations between all levels of abstraction (i.e., from the runtime to the user API), it is easier for a programmer or software designer to avoid mixing low level with high level aspects, as we are often used to see in state-of-the-art Big Data analytics frameworks. From the user-level perspective, we think that a clearer and simple semantics is preferable, together with a strong separation of concerns. For this reason, we use the Dataflow model as a starting point to build a programming environment with a simplified programming model implemented as a Domain-Specific Language, that is on top of a stack of layers that build a prototypical framework for Big Data analytics. The contribution of this thesis is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm, Google Dataflow), thus making it easier to understand high-level data-processing applications written in such frameworks. As result of this analysis, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level. Second, we propose a programming environment based on such layered model in the form of a Domain-Specific Language (DSL) for processing data collections, called PiCo (Pipeline Composition). The main entity of this programming model is the Pipeline, basically a DAG-composition of processing elements. This model is intended to give the user an unique interface for both stream and batch processing, hiding completely data management and focusing only on operations, which are represented by Pipeline stages. Our DSL will be built on top of the FastFlow library, exploiting both shared and distributed parallelism, and implemented in C++11/14 with the aim of porting C++ into the Big Data world

Workload Management for Data-Intensive Services

<p>Data-intensive web services are typically composed of three tiers: i) a display tier that interacts with users and serves rich content to them, ii) a storage tier that stores the user-generated or machine-generated data used to create this content, and iii) an analytics tier that runs data analysis tasks in order to create and optimize new content. Each tier has different workloads and requirements that result in a diverse set of systems being used in modern data-intensive web services.</p><p>Servers are provisioned dynamically in the display tier to ensure that interactive client requests are served as per the latency and throughput requirements. The challenge is not only deciding automatically how many servers to provision but also when to provision them, while ensuring stable system performance and high resource utilization. To address these challenges, we have developed a new control policy for provisioning resources dynamically in coarse-grained units (e.g., adding or removing servers or virtual machines in cloud platforms). Our new policy, called proportional thresholding, converts a user-specified performance target value into a target range in order to account for the relative effect of provisioning a server on the overall workload performance.</p><p>The storage tier is similar to the display tier in some respects, but poses the additional challenge of needing redistribution of stored data when new storage nodes are added or removed. Thus, there will be some delay before the effects of changing a resource allocation will appear. Moreover, redistributing data can cause some interference to the current workload because it uses resources that can otherwise be used for processing requests. We have developed a system, called Elastore, that addresses the new challenges found in the storage tier. Elastore not only coordinates resource allocation and data redistribution to preserve stability during dynamic resource provisioning, but it also finds the best tradeoff between workload interference and data redistribution time.</p><p>The workload in the analytics tier consists of data-parallel workflows that can either be run in a batch fashion or continuously as new data becomes available. Each workflow is composed of smaller units that have producer-consumer relationships based on data. These workflows are often generated from declarative specifications in languages like SQL, so there is a need for a cost-based optimizer that can generate an efficient execution plan for a given workflow. There are a number of challenges when building a cost-based optimizer for data-parallel workflows, which includes characterizing the large execution plan space, developing cost models to estimate the execution costs, and efficiently searching for the best execution plan. We have built two cost-based optimizers: Stubby for batch data-parallel workflows running on MapReduce systems, and Cyclops for continuous data-parallel workflows where the choice of execution system is made a part of the execution plan space.</p><p>We have conducted a comprehensive evaluation that shows the effectiveness of each tier's automated workload management solution.</p>Dissertatio

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Journalistic Knowledge Platforms: from Idea to Realisation

Journalistiske kunnskapsplattformer (JKPer) er en type intelligente informasjonssystemer designet for å forbedre nyhetsproduksjonsprosesser ved å kombinere stordata, kunstig intelligens (KI) og kunnskapsbaser for å støtte journalister. Til tross for sitt potensial for å revolusjonere journalistikkfeltet, har adopsjonen av JKPer vært treg, med forskere og store nyhetsutløp involvert i forskning og utvikling av JKPer. Den langsomme adopsjonen kan tilskrives den tekniske kompleksiteten til JKPer, som har ført til at nyhetsorganisasjoner stoler på flere uavhengige og oppgavespesifikke produksjonssystemer. Denne situasjonen kan øke ressurs- og koordineringsbehovet og kostnadene, samtidig som den utgjør en trussel om å miste kontrollen over data og havne i leverandørlåssituasjoner. De tekniske kompleksitetene forblir en stor hindring, ettersom det ikke finnes en allerede godt utformet systemarkitektur som ville lette realiseringen og integreringen av JKPer på en sammenhengende måte over tid. Denne doktoravhandlingen bidrar til teorien og praksisen rundt kunnskapsgrafbaserte JKPer ved å studere og designe en programvarearkitektur som referanse for å lette iverksettelsen av konkrete løsninger og adopsjonen av JKPer. Den første bidraget til denne doktoravhandlingen gir en grundig og forståelig analyse av ideen bak JKPer, fra deres opprinnelse til deres nåværende tilstand. Denne analysen gir den første studien noensinne av faktorene som har bidratt til den langsomme adopsjonen, inkludert kompleksiteten i deres sosiale og tekniske aspekter, og identifiserer de største utfordringene og fremtidige retninger for JKPer. Den andre bidraget presenterer programvarearkitekturen som referanse, som gir en generisk blåkopi for design og utvikling av konkrete JKPer. Den foreslåtte referansearkitekturen definerer også to nye typer komponenter ment for å opprettholde og videreutvikle KI-modeller og kunnskapsrepresentasjoner. Den tredje presenterer et eksempel på iverksettelse av programvarearkitekturen som referanse og beskriver en prosess for å forbedre effektiviteten til informasjonsekstraksjonspipelines. Denne rammen muliggjør en fleksibel, parallell og samtidig integrering av teknikker for naturlig språkbehandling og KI-verktøy. I tillegg diskuterer denne avhandlingen konsekvensene av de nyeste KI-fremgangene for JKPer og ulike etiske aspekter ved bruk av JKPer. Totalt sett gir denne PhD-avhandlingen en omfattende og grundig analyse av JKPer, fra teorien til designet av deres tekniske aspekter. Denne forskningen tar sikte på å lette vedtaket av JKPer og fremme forskning på dette feltet.Journalistic Knowledge Platforms (JKPs) are a type of intelligent information systems designed to augment news creation processes by combining big data, artificial intelligence (AI) and knowledge bases to support journalists. Despite their potential to revolutionise the field of journalism, the adoption of JKPs has been slow, with scholars and large news outlets involved in the research and development of JKPs. The slow adoption can be attributed to the technical complexity of JKPs that led news organisation to rely on multiple independent and task-specific production system. This situation can increase the resource and coordination footprint and costs, at the same time it poses a threat to lose control over data and face vendor lock-in scenarios. The technical complexities remain a major obstacle as there is no existing well-designed system architecture that would facilitate the realisation and integration of JKPs in a coherent manner over time. This PhD Thesis contributes to the theory and practice on knowledge-graph based JKPs by studying and designing a software reference architecture to facilitate the instantiation of concrete solutions and the adoption of JKPs. The first contribution of this PhD Thesis provides a thorough and comprehensible analysis of the idea of JKPs, from their origins to their current state. This analysis provides the first-ever study of the factors that have contributed to the slow adoption, including the complexity of their social and technical aspects, and identifies the major challenges and future directions of JKPs. The second contribution presents the software reference architecture that provides a generic blueprint for designing and developing concrete JKPs. The proposed reference architecture also defines two novel types of components intended to maintain and evolve AI models and knowledge representations. The third presents an instantiation example of the software reference architecture and details a process for improving the efficiency of information extraction pipelines. This framework facilitates a flexible, parallel and concurrent integration of natural language processing techniques and AI tools. Additionally, this Thesis discusses the implications of the recent AI advances on JKPs and diverse ethical aspects of using JKPs. Overall, this PhD Thesis provides a comprehensive and in-depth analysis of JKPs, from the theory to the design of their technical aspects. This research aims to facilitate the adoption of JKPs and advance research in this field.Doktorgradsavhandlin

Monitoring and analysis system for performance troubleshooting in data centers

It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming. To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers. VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the performance issue. VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.Ph.D

Quality of Service in Distributed Stream Processing for large scale Smart Pervasive Environments

The wide diffusion of cheap, small, and portable sensors integrated in an unprecedented large variety of devices and the availability of almost ubiquitous Internet connectivity make it possible to collect an unprecedented amount of real time information about the environment we live in. These data streams, if properly and timely analyzed, can be exploited to build new intelligent and pervasive services that have the potential of improving people's quality of life in a variety of cross concerning domains such as entertainment, health-care, or energy management. The large heterogeneity of application domains, however, calls for a middleware-level infrastructure that can effectively support their different quality requirements. In this thesis we study the challenges related to the provisioning of differentiated quality-of-service (QoS) during the processing of data streams produced in pervasive environments. We analyze the trade-offs between guaranteed quality, cost, and scalability in streams distribution and processing by surveying existing state-of-the-art solutions and identifying and exploring their weaknesses. We propose an original model for QoS-centric distributed stream processing in data centers and we present Quasit, its prototype implementation offering a scalable and extensible platform that can be used by researchers to implement and validate novel QoS-enforcement mechanisms. To support our study, we also explore an original class of weaker quality guarantees that can reduce costs when application semantics do not require strict quality enforcement. We validate the effectiveness of this idea in a practical use-case scenario that investigates partial fault-tolerance policies in stream processing by performing a large experimental study on the prototype of our novel LAAR dynamic replication technique. Our modeling, prototyping, and experimental work demonstrates that, by providing data distribution and processing middleware with application-level knowledge of the different quality requirements associated to different pervasive data flows, it is possible to improve system scalability while reducing costs

A generic framework for process execution and secure multi-party transaction authorization

Process execution engines are not only an integral part of workflow and business process management systems but are increasingly used to build process-driven applications. In other words, they are potentially used in all kinds of software across all application domains. However, contemporary process engines and workflow systems are unsuitable for use in such diverse application scenarios for several reasons. The main shortcomings can be observed in the areas of interoperability, versatility, and programmability. Therefore, this thesis makes a step away from domain specific, monolithic workflow engines towards generic and versatile process runtime frameworks, which enable integration of process technology into all kinds of software. To achieve this, the idea and corresponding architecture of a generic and embeddable process virtual machine (ePVM), which supports defining process flows along the theoretical foundation of communicating extended finite state machines, are presented. The architecture focuses on the core process functionality such as control flow and state management, monitoring, persistence, and communication, while using JavaScript as a process definition language. This approach leads to a very generic yet easily programmable process framework. A fully functional prototype implementation of the proposed framework is provided along with multiple example applications. Despite the fact that business processes are increasingly automated and controlled by information systems, humans are still involved, directly or indirectly, in many of them. Thus, for process flows involving sensitive transactions, a highly secure authorization scheme supporting asynchronous multi-party transaction authorization must be available within process management systems. Therefore, along with the ePVM framework, this thesis presents a novel approach for secure remote multi-party transaction authentication - the zone trusted information channel (ZTIC). The ZTIC approach uniquely combines multiple desirable properties such as the highest level of security, ease-of-use, mobility, remote administration, and smooth integration with existing infrastructures into one device and method. Extensively evaluating both, the ePVM framework and the ZTIC, this thesis shows that ePVM in combination with the ZTIC approach represents a unique and very powerful framework for building workflow systems and process-driven applications including support for secure multi-party transaction authorization