Performance evaluation of database replication systems.

One of the most demanding needs in cloud computing is that of having scalable and highly available databases. One of the ways to attend these needs is to leverage the scalable replication techniques developed in the last decade. These techniques allow increasing both the availability and scalability of databases. Many replication protocols have been proposed during the last decade. The main research challenge was how to scale under the eager replication model, the one that provides consistency across replicas. In this paper, we examine three eager database replication systems available today: Middle-R, C-JDBC and MySQL Cluster using TPC-W benchmark. We analyze their architecture, replication protocols and compare the performance both in the absence of failures and when there are failures.

Comparison of Architectures and Performance of Database Replication Systems

One of the most demanding needs in cloud computing and big data is that of having scalable and highly available databases. One of the ways to attend these needs is to leverage the scalable replication techniques developed in the last decade. These techniques allow increasing both the availability and scalability of databases. Many replication protocols have been proposed during the last decade. The main research challenge was how to scale under the eager replication model, the one that provides consistency across replicas. This thesis provides an in depth study of three eager database replication systems based on relational systems: Middle-R, C-JDBC and MySQL Cluster and three systems based on In-Memory Data Grids: JBoss Data Grid, Oracle Coherence and Terracotta Ehcache. Thesis explore these systems based on their architecture, replication protocols, fault tolerance and various other functionalities. It also provides experimental analysis of these systems using state-of-the art benchmarks: TPC-C and TPC-W (for relational systems) and Yahoo! Cloud Serving Benchmark (In- Memory Data Grids). Thesis also discusses three Graph Databases, Neo4j, Titan and Sparksee based on their architecture and transactional capabilities and highlights the weaker transactional consistencies provided by these systems. It discusses an implementation of snapshot isolation in Neo4j graph database to provide stronger isolation guarantees for transactions.

Flexible processing architecture for maintaining QoS in embedded systems applications

Comunicación presentada en las V Jornadas de Computación Empotrada, Valladolid, 17-19 Septiembre 2014

Scheduling medical application workloads on virtualized computing systems

This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of "cloud computing" services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves. In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions: (1) An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments. (2) A performance prediction methodology applicable to the target environment. (3) A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees. In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia. Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20–30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.

Cloud query manager: uso de web semântica para evitar o problema de aprisionamento em IaaS

Cloud computing can be defined as a distributed computational model by through resources (hardware, storage, development platforms and communication) are shared, as paid services accessible with minimal management effort and interaction. A great benefit of this model is to enable the use of various providers (e.g a multi-cloud architecture) to compose a set of services in order to obtain an optimal configuration for performance and cost. However, the multi-cloud use is precluded by the problem of cloud lock-in. The cloud lock-in is the dependency between an application and a cloud platform. It is commonly addressed by three strategies: (i) use of intermediate layer that stands to consumers of cloud services and the provider, (ii) use of standardized interfaces to access the cloud, or (iii) use of models with open specifications. This paper outlines an approach to evaluate these strategies. This approach was performed and it was found that despite the advances made by these strategies, none of them actually solves the problem of lock-in cloud. In this sense, this work proposes the use of Semantic Web to avoid cloud lock-in, where RDF models are used to specify the features of a cloud, which are managed by SPARQL queries. In this direction, this work: (i) presents an evaluation model that quantifies the problem of cloud lock-in, (ii) evaluates the cloud lock-in from three multi-cloud solutions and three cloud platforms, (iii) proposes using RDF and SPARQL on management of cloud resources, (iv) presents the cloud Query Manager (CQM), an SPARQL server that implements the proposal, and (v) comparing three multi-cloud solutions in relation to CQM on the response time and the effectiveness in the resolution of cloud lock-in.

Impact of Thresholds and Load Patterns when Executing HPC Applications with Cloud Elasticity

Elasticity is one of the most known capabilities related to cloud computing, being largely deployed reactively using thresholds. In this way, maximum and minimum limits are used to drive resource allocation and deallocation actions, leading to the following problem statements: How can cloud users set the threshold values to enable elasticity in their cloud applications? And what is the impact of the application’s load pattern in the elasticity? This article tries to answer these questions for iterative high performance computing applications, showing the impact of both thresholds and load patterns on application performance and resource consumption. To accomplish this, we developed a reactive and PaaS-based elasticity model called AutoElastic and employed it over a private cloud to execute a numerical integration application. Here, we are presenting an analysis of best practices and possible optimizations regarding the elasticity and HPC pair. Considering the results, we observed that the maximum threshold influences the application time more than the minimum one. We concluded that threshold values close to 100% of CPU load are directly related to a weaker reactivity, postponing resource reconfiguration when its activation in advance could be pertinent for reducing the application runtime.

An architecture for network acceleration as a service in the cloud continuum

The pervasive availability of connected devices in any industrial and societal sector is pushing for an evolution of the well-established cloud computing model. The emerging paradigm of the cloud continuum embraces this decentralization trend and envisions virtualized computing resources physically located between traditional datacenters and data sources. By totally or partially executing closer to the network edge, applications can have quicker reactions to events, thus enabling advanced forms of automation and intelligence. However, these applications also induce new data-intensive workloads with low-latency constraints that require the adoption of specialized resources, such as high-performance communication options (e.g., RDMA, DPDK, XDP, etc.). Unfortunately, cloud providers still struggle to integrate these options into their infrastructures. That risks undermining the principle of generality that underlies the cloud computing scale economy by forcing developers to tailor their code to low-level APIs, non-standard programming models, and static execution environments. This thesis proposes a novel system architecture to empower cloud platforms across the whole cloud continuum with Network Acceleration as a Service (NAaaS). To provide commodity yet efficient access to acceleration, this architecture defines a layer of agnostic high-performance I/O APIs, exposed to applications and clearly separated from the heterogeneous protocols, interfaces, and hardware devices that implement it. A novel system component embodies this decoupling by offering a set of agnostic OS features to applications: memory management for zero-copy transfers, asynchronous I/O processing, and efficient packet scheduling. This thesis also explores the design space of the possible implementations of this architecture by proposing two reference middleware systems and by adopting them to support interactive use cases in the cloud continuum: a serverless platform and an Industry 4.0 scenario. A detailed discussion and a thorough performance evaluation demonstrate that the proposed architecture is suitable to enable the easy-to-use, flexible integration of modern network acceleration into next-generation cloud platforms.

Ottimizzazione di un Data Warehouse con Google Cloud Platform

I dati sono una risorsa di valore inestimabile per tutte le organizzazioni. Queste informazioni vanno da una parte gestite tramite i classici sistemi operazionali, dall’altra parte analizzate per ottenere approfondimenti che possano guidare le scelte di business. Uno degli strumenti fondamentali a supporto delle scelte di business è il data warehouse. Questo elaborato è il frutto di un percorso di tirocinio svolto con l'azienda Injenia S.r.l. Il focus del percorso era rivolto all'ottimizzazione di un data warehouse che l'azienda vende come modulo aggiuntivo di un software di nome Interacta. Questo data warehouse, Interacta Analytics, ha espresso nel tempo notevoli criticità architetturali e di performance. L’architettura attualmente usata per la creazione e la gestione dei dati all'interno di Interacta Analytics utilizza un approccio batch, pertanto, l’obiettivo cardine dello studio è quello di trovare soluzioni alternative batch che garantiscano un risparmio sia in termini economici che di tempo, esplorando anche la possibilità di una transizione ad un’architettura streaming. Gli strumenti da utilizzare in questa ricerca dovevano inoltre mantenersi in linea con le tecnologie utilizzate per Interacta, ossia i servizi della Google Cloud Platform. Dopo una breve dissertazione sul background teorico di questa area tematica, l'elaborato si concentra sul funzionamento del software principale e sulla struttura logica del modulo di analisi. Infine, si espone il lavoro sperimentale, innanzitutto proponendo un'analisi delle criticità principali del sistema as-is, dopodiché ipotizzando e valutando quattro ipotesi migliorative batch e due streaming. Queste, come viene espresso nelle conclusioni della ricerca, migliorano di molto le performance del sistema di analisi in termini di tempistiche di elaborazione, di costo totale e di semplicità dell'architettura, in particolare grazie all'utilizzo dei servizi serverless con container e FaaS della piattaforma cloud di Google.

Serverless Computing il caso Google Cloud Functions

Il serverless é un paradigma del cloud computing al giorno d’oggi sempre più diffuso; si basa sulla scrittura di funzioni stateless in quanto le attività relative alla loro manutenzione e scalabilità fanno capo al provider dei servizi cloud. Lo sviluppatore deve quindi solamente concentrarsi sulla creazione del prodotto. Questo lavoro si apre con un’analisi del cloud computing introducendo i principali modelli di applicazione, passando dal parlare di servizi cloud, con le varie sottocategorie e i relativi utilizzi per poi arrivare a parlare di serverless. Si é scelto di focalizzarsi sulla piattaforma Google con la suite: Google Cloud Platform. In particolare, si parlerà di Google Cloud Functions, una nuova offerta serverless della compagnia, di recente sviluppo e in continuo aggiornamento. Partiremo dalle prime release, analizzeremo l’ambiente di sviluppo, i casi d’uso, vantaggi, svantaggi, parleremo poi di portabilità e verranno mostrati alcuni esempi del loro utilizzo.

Integrating mobile devices into a scalable Grid Computing architecture designed to perform distributed computations

The idea of Grid Computing originated in the nineties and found its concrete applications in contexts like the SETI@home project where a lot of computers (offered by volunteers) cooperated, performing distributed computations, inside the Grid environment analyzing radio signals trying to find extraterrestrial life. The Grid was composed of traditional personal computers but, with the emergence of the first mobile devices like Personal Digital Assistants (PDAs), researchers started theorizing the inclusion of mobile devices into Grid Computing; although impressive theoretical work was done, the idea was discarded due to the limitations (mainly technological) of mobile devices available at the time. Decades have passed, and now mobile devices are extremely more performant and numerous than before, leaving a great amount of resources available on mobile devices, such as smartphones and tablets, untapped. Here we propose a solution for performing distributed computations over a Grid Computing environment that utilizes both desktop and mobile devices, exploiting the resources from day-to-day mobile users that alternatively would end up unused. The work starts with an introduction on what Grid Computing is, the evolution of mobile devices, the idea of integrating such devices into the Grid and how to convince device owners to participate in the Grid. Then, the tone becomes more technical, starting with an explanation on how Grid Computing actually works, followed by the technical challenges of integrating mobile devices into the Grid. Next, the model, which constitutes the solution offered by this study, is explained, followed by a chapter regarding the realization of a prototype that proves the feasibility of distributed computations over a Grid composed by both mobile and desktop devices. To conclude future developments and ideas to improve this project are presented.

Android as a cloud ticket validator

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Informática e de Computadores

Partial utility-driven scheduling for flexible SLA and pricing arbitration in clouds

Cloud SLAs compensate customers with credits when average availability drops below certain levels. This is too inflexible because consumers lose non-measurable amounts of performance being only compensated later, in next charging cycles. We propose to schedule virtual machines (VMs), driven by range-based non-linear reductions of utility, different for classes of users and across different ranges of resource allocations: partial utility. This customer-defined metric, allows providers transferring resources between VMs in meaningful and economically efficient ways. We define a comprehensive cost model incorporating partial utility given by clients to a certain level of degradation, when VMs are allocated in overcommitted environments (Public, Private, Community Clouds). CloudSim was extended to support our scheduling model. Several simulation scenarios with synthetic and real workloads are presented, using datacenters with different dimensions regarding the number of servers and computational capacity. We show the partial utility-driven driven scheduling allows more VMs to be allocated. It brings benefits to providers, regarding revenue and resource utilization, allowing for more revenue per resource allocated and scaling well with the size of datacenters when comparing with an utility-oblivious redistribution of resources. Regarding clients, their workloads’ execution time is also improved, by incorporating an SLA-based redistribution of their VM’s computational power.

Data analytics in the cloud with flexible mapreduced workflows

Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.

Key-CRDT stores

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

O Futuro dos Centros de Dados

Atualmente, as Tecnologias de Informação (TI) são cada vez mais vitais dentro das organizações. As TI são o motor de suporte do negócio. Para grande parte das organizações, o funcionamento e desenvolvimento das TI têm como base infraestruturas dedicadas (internas ou externas) denominadas por Centro de Dados (CD). Nestas infraestruturas estão concentrados os equipamentos de processamento e armazenamento de dados de uma organização, por isso, são e serão cada vez mais desafiadas relativamente a diversos fatores tais como a escalabilidade, disponibilidade, tolerância à falha, desempenho, recursos disponíveis ou disponibilizados, segurança, eficiência energética e inevitavelmente os custos associados. Com o aparecimento das tecnologias baseadas em computação em nuvem e virtualização, abrese todo um leque de novas formas de endereçar os desafios anteriormente descritos. Perante este novo paradigma, surgem novas oportunidades de consolidação dos CD que podem representar novos desafios para os gestores de CD. Por isso, é no mínimo irrealista para as organizações simplesmente eliminarem os CD ou transforma-los segundo os mais altos padrões de qualidade. As organizações devem otimizar os seus CD, contudo um projeto eficiente desta natureza, com capacidade para suportar as necessidades impostas pelo mercado, necessidades dos negócios e a velocidade da evolução tecnológica, exigem soluções complexas e dispendiosas tanto para a sua implementação como a sua gestão. É neste âmbito que surge o presente trabalho. Com o objetivo de estudar os CD inicia-se um estudo sobre esta temática, onde é detalhado o seu conceito, evolução histórica, a sua topologia, arquitetura e normas existentes que regem os mesmos. Posteriormente o estudo detalha algumas das principais tendências condicionadoras do futuro dos CD. Explorando o conhecimento teórico resultante do estudo anterior, desenvolve-se uma metodologia de avaliação dos CD baseado em critérios de decisão. O estudo culmina com uma análise sobre uma nova solução tecnológica e a avaliação de três possíveis cenários de implementação: a primeira baseada na manutenção do atual CD; a segunda baseada na implementação da nova solução em outro CD em regime de hosting externo; e finalmente a terceira baseada numa implementação em regime de IaaS.