Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study

Fog and Edge Oriented Embedded Enterprise Systems Patterns: Towards Distributed Enterprise Systems That Run on Edge and Fog Nodes

Enterprise software systems enable enterprises to enhance business and management reporting tasks in enterprise settings. Internet of Things (IoT) focuses on making interactions possible between a number of network-connected physical devices. Prominence of IoT sensors and multiple business drivers have created a contemporary need for enterprise software systems to interact with IoT devices. Business process implementations, business logic and microservices have traditionally been centralized in enterprise systems. Constraints like privacy, latency, bandwidth, connectivity and security have posed a new set of architectural challenges that can be resolved by designing enterprise systems differently so that parts of business logic and processes can run on fog and edge devices to improve privacy, minimize communication bandwidth and promote low-latency business process execution. This paper aims to propose a set of patterns for the expansion of previously-centralized enterprise systems to the edge of the network. Patterns are supported by a case study for contextualization and analysis

Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying "micro-ing" architectures. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modelling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table

Information Collection Platform for Smart Nudging. A Microservice-Based Approach.

This thesis aims to explore the problem of integrating heterogeneous data sources into the Smart Nudge system. The Smart Nudge system is a system that produces personalised nudges that are contextually relevant to each user. The system relies on access to live data that could be constructed and presented in specific ways to influence users behaviour towards an agreed-upon goal. The goal is to ascertain the suitability of a microservice-based approach to designing the component that is responsible for integrating various data sources. A small prototype of two microservices provided a practical look at integrating real-world sources, namely a Norwegian weather service and a bus tracking service in Chicago. The proposed architecture is analysed using a set of requirements derived from a theoretical examination of the Smart Nudge system and a general theoretical look at decomposition techniques used to evaluate microservice architectures. Evaluating the prototype revealed that the Smart Nudge system is highly dependant on augmenting data sources with additional meta-data to produce personalised nudges. The analysis indicates that a data-driven microservice-based architecture seems well suited to resolving some of the problems and requirements that are somewhat unique to the Smart Nudge system setting

Designing a Smart City Internet of Things Platform with Microservice Architecture

The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standard-ization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, microservice architecture style has emerged and gained a lot of popularity in the industry in recent years. In this work, we share our early experience of applying the microservice architecture style to design a Smart City IoT platform. Our experience suggests significant benefits provided by this architectural style compared to the more generic Service-Oriented Architecture (SOA) approaches, as well as highlights some of the challenges it introduces

Microservices architectures in healthcare with Apache Kafka

Dissertação de mestrado em Informatics EngineeringOver the past few years, we have seen an exponential increase in the amount of data produced. This increase in data is due, in large part, to the massive use of sensors, as well as the immense amount of existing applications. Due to this factor, and in order to obtain relevant information through the data, companies, institutions and the scientific community are constantly looking for new solutions to be able to respond to the challenges. One of the areas where evolution is most needed is the area of healthcare, an area on which we all depend as a society. Every day, traditional healthcare information systems produce a large amount of data, making it complex to manage. Much of this data is produced by IoT devices, such as vital signs monitors, and in many cases can be critical to the patient’s health, as in the case of Intensive Care Units. In this sense, the main objective of this dissertation is to expose the advantages and disadvantages of the applicability of microservices architectures and the use of Apache Kafka in the health area, more specifically in Intensive Care Units where the information flow is critical. In order to support these objectives, a Proof of Concept was developed, based on a future real applicability, which will support the carrying out of analyzes and tests.Durante os últimos anos, temos assistido a um aumento exponencial da quantidade de dados produzida. Este aumento de dados deve-se, em grande parte, à utilização massiva de sensores, assim como à enorme quantidade de aplicações existentes. Devido a esse fator, e de forma a conseguir obter informações relevantes através dos dados, empresas, instituições e comunidade científica, estão constantemente à procura de novas soluções para conseguir responder aos desafios. Uma das áreas onde a evolução é mais necessária é a área da saúde, uma área da qual todos dependemos enquanto sociedade. Todos os dias, os sistemas tradicionais de informação em saúde produzem uma grande quantidade de dados, tornando-os complexos de gerir. Muitos destes dados são produzidos pelos dispositivos IoT, como os monitores de sinais vitais, e em muitos dos casos podem ser fulcrais para a saúde do paciente, como é o caso das Unidade de Cuidados Intensivos. Neste sentido, a presente dissertação tem como objetivo principal expor as vantagens e desvantagens da aplicabilidade das arquiteturas de microservices e da utilização do Apache Kafka na área da saúde, mais concretamente nas Unidades de Cuidados Intensivos onde o fluxo de informação é crítico. De forma a auxiliar estes objetivos, foi desenvolvido uma Prova de Conceito, tendo por base uma futura aplicabilidade real, que servirá de suporte à realização de análises e testes