Automatic performance optimisation of component-based enterprise systems via redundancy

Component technologies, such as J2EE and .NET have been extensively adopted for building complex enterprise applications. These technologies help address complex functionality and flexibility problems and reduce development and maintenance costs. Nonetheless, current component technologies provide little support for predicting and controlling the emerging performance of software systems that are assembled from distinct components. Static component testing and tuning procedures provide insufficient performance guarantees for components deployed and run in diverse assemblies, under unpredictable workloads and on different platforms. Often, there is no single component implementation or deployment configuration that can yield optimal performance in all possible conditions under which a component may run. Manually optimising and adapting complex applications to changes in their running environment is a costly and error-prone management task. The thesis presents a solution for automatically optimising the performance of component-based enterprise systems. The proposed approach is based on the alternate usage of multiple component variants with equivalent functional characteristics, each one optimized for a different execution environment. A management framework automatically administers the available redundant variants and adapts the system to external changes. The framework uses runtime monitoring data to detect performance anomalies and significant variations in the application's execution environment. It automatically adapts the application so as to use the optimal component configuration under the current running conditions. An automatic clustering mechanism analyses monitoring data and infers information on the components' performance characteristics. System administrators use decision policies to state high-level performance goals and configure system management processes. A framework prototype has been implemented and tested for automatically managing a J2EE application. Obtained results prove the framework's capability to successfully manage a software system without human intervention. The management overhead induced during normal system execution and through management operations indicate the framework's feasibility

Online failure prediction in air traffic control systems

This thesis introduces a novel approach to online failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The thesis presents an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of Casper has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system developed by Selex ES, a Finmeccanica Company. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures

Online failure prediction in air traffic control systems

This thesis introduces a novel approach to online failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The thesis presents an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of Casper has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system developed by Selex ES, a Finmeccanica Company. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures

The impact of microservices: an empirical analysis of the emerging software architecture

Dissertação de mestrado em Informatics EngineeringThe applications’ development paradigm has faced changes in recent years, with modern development being characterized by the need to continuously deliver new software iterations. With great affinity with those principles, microservices is a software architecture which features characteristics that potentially promote multiple quality attributes often required by modern, large-scale applications. Its recent growth in popularity and acceptance in the industry made this architectural style often described as a form of modernizing applications that allegedly solves all the traditional monolithic applications’ inconveniences. However, there are multiple worth mentioning costs associated with its adoption, which seem to be very vaguely described in existing empirical research, being often summarized as "the complexity of a distributed system". The adoption of microservices provides the agility to achieve its promised benefits, but to actually reach them, several key implementation principles have to be honored. Given that it is still a fairly recent approach to developing applications, the lack of established principles and knowledge from development teams results in the misjudgment of both costs and values of this architectural style. The outcome is often implementations that conflict with its promised benefits. In order to implement a microservices-based architecture that achieves its alleged benefits, there are multiple patterns and methodologies involved that add a considerable amount of complexity. To evaluate its impact in a concrete and empirical way, one same e-commerce platform was developed from scratch following a monolithic architectural style and two architectural patterns based on microservices, featuring distinct inter-service communication and data management mechanisms. The effort involved in dealing with eventual consistency, maintaining a communication infrastructure, and managing data in a distributed way portrayed significant overheads not existent in the development of traditional applications. Nonetheless, migrating from a monolithic architecture to a microservicesbased is currently accepted as the modern way of developing software and this ideology is not often contested, nor the involved technical challenges are appropriately emphasized. Sometimes considered over-engineering, other times necessary, this dissertation contributes with empirical data from insights that showcase the impact of the migration to microservices in several topics. From the trade-offs associated with the use of specific patterns, the development of the functionalities in a distributed way, and the processes to assure a variety of quality attributes, to performance benchmarks experiments and the use of observability techniques, the entire development process is described and constitutes the object of study of this dissertation.O paradigma de desenvolvimento de aplicações tem visto alterações nos últimos anos, sendo o desenvolvimento moderno caracterizado pela necessidade de entrega contínua de novas iterações de software. Com grande afinidade com esses princípios, microsserviços são uma arquitetura de software que conta com características que potencialmente promovem múltiplos atributos de qualidade frequentemente requisitados por aplicações modernas de grandes dimensões. O seu recente crescimento em popularidade e aceitação na industria fez com que este estilo arquitetural se comumente descrito como uma forma de modernizar aplicações que alegadamente resolve todos os inconvenientes apresentados por aplicações monolíticas tradicionais. Contudo, existem vários custos associados à sua adoção, aparentemente descritos de forma muito vaga, frequentemente sumarizados como a "complexidade de um sistema distribuído". A adoção de microsserviços fornece a agilidade para atingir os seus benefícios prometidos, mas para os alcançar, vários princípios de implementação devem ser honrados. Dado que ainda se trata de uma forma recente de desenvolver aplicações, a falta de princípios estabelecidos e conhecimento por parte das equipas de desenvolvimento resulta em julgamentos errados dos custos e valores deste estilo arquitetural. O resultado geralmente são implementações que entram em conflito com os seus benefícios prometidos. De modo a implementar uma arquitetura baseada em microsserviços com os benefícios prometidos existem múltiplos padrões que adicionam considerável complexidade. De modo a avaliar o impacto dos microsserviços de forma concreta e empírica, foi desenvolvida uma mesma plataforma e-commerce de raiz segundo uma arquitetura monolítica e duas arquitetura baseadas em microsserviços, contando com diferentes mecanismos de comunicação entre os serviços. O esforço envolvido em lidar com consistência eventual, manter a infraestrutura de comunicação e gerir os dados de uma forma distribuída representaram desafios não existentes no desenvolvimento de aplicações tradicionais. Apesar disso, a ideologia de migração de uma arquitetura monolítica para uma baseada em microsserviços é atualmente aceite como a forma moderna de desenvolver aplicações, não sendo frequentemente contestada nem os seus desafios técnicos são apropriadamente enfatizados. Por vezes considerado overengineering, outras vezes necessário, a presente dissertação visa contribuir com dados práticos relativamente ao impacto da migração para arquiteturas baseadas em microsserviços em diversos tópicos. Desde os trade-offs envolvidos no uso de padrões específicos, o desenvolvimento das funcionalidades de uma forma distribuída e nos processos para assegurar uma variedade de atributos de qualidade, até análise de benchmarks de performance e uso de técnicas de observabilidade, todo o desenvolvimento é descrito e constitui o objeto de estudo da dissertação

FPGA-based Query Acceleration for Non-relational Databases

Database management systems are an integral part of today’s everyday life. Trends like smart applications, the internet of things, and business and social networks require applications to deal efficiently with data in various data models close to the underlying domain. Therefore, non-relational database systems provide a wide variety of database models, like graphs and documents. However, current non-relational database systems face performance challenges due to the end of Dennard scaling and therefore performance scaling of CPUs. In the meanwhile, FPGAs have gained traction as accelerators for data management. Our goal is to tackle the performance challenges of non-relational database systems with FPGA acceleration and, at the same time, address design challenges of FPGA acceleration itself. Therefore, we split this thesis up into two main lines of work: graph processing and flexible data processing. Because of the lacking benchmark practices for graph processing accelerators, we propose GraphSim. GraphSim is able to reproduce runtimes of these accelerators based on a memory access model of the approach. Through this simulation environment, we extract three performance-critical accelerator properties: asynchronous graph processing, compressed graph data structure, and multi-channel memory. Since these accelerator properties have not been combined in one system, we propose GraphScale. GraphScale is the first scalable, asynchronous graph processing accelerator working on a compressed graph and outperforms all state-of-the-art graph processing accelerators. Focusing on accelerator flexibility, we propose PipeJSON as the first FPGA-based JSON parser for arbitrary JSON documents. PipeJSON is able to achieve parsing at line-speed, outperforming the fastest, vectorized parsers for CPUs. Lastly, we propose the subgraph query processing accelerator GraphMatch which outperforms state-of-the-art CPU systems for subgraph query processing and is able to flexibly switch queries during runtime in a matter of clock cycles

On the use of intelligent models towards meeting the challenges of the edge mesh

Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain

Performance Isolation in Multi-Tenant Applications

The thesis presents methods to isolate different tenants, sharing one application instance, with regards to he performance they observe. Therefore, a request based admission control is introduced. Furthermore, the publication presents methods and novel metrics to evaluate the degree of isolation a system achieves. These insights are used to evaluate the developed isolation methods, resulting in recommendations of methods for various scenarios

Automated anomaly recognition in real time data streams for oil and gas industry.

There is a growing demand for computer-assisted real-time anomaly detection - from the identification of suspicious activities in cyber security, to the monitoring of engineering data for various applications across the oil and gas, automotive and other engineering industries. To reduce the reliance on field experts' knowledge for identification of these anomalies, this thesis proposes a deep-learning anomaly-detection framework that can help to create an effective real-time condition-monitoring framework. The aim of this research is to develop a real-time and re-trainable generic anomaly-detection framework, which is capable of predicting and identifying anomalies with a high level of accuracy - even when a specific anomalous event has no precedent. Machine-based condition monitoring is preferable in many practical situations where fast data analysis is required, and where there are harsh climates or otherwise life-threatening environments. For example, automated conditional monitoring systems are ideal in deep sea exploration studies, offshore installations and space exploration. This thesis firstly reviews studies about anomaly detection using machine learning. It then adopts the best practices from those studies in order to propose a multi-tiered framework for anomaly detection with heterogeneous input sources, which can deal with unseen anomalies in a real-time dynamic problem environment. The thesis then applies the developed generic multi-tiered framework to two fields of engineering: data analysis and malicious cyber attack detection. Finally, the framework is further refined based on the outcomes of those case studies and is used to develop a secure cross-platform API, capable of re-training and data classification on a real-time data feed