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

Microservices: Granularity vs. Performance

Microservice Architectures (MA) have the potential to increase the agility of software development. In an era where businesses require software applications to evolve to support software emerging requirements, particularly for Internet of Things (IoT) applications, we examine the issue of microservice granularity and explore its effect upon application latency. Two approaches to microservice deployment are simulated; the first with microservices in a single container, and the second with microservices partitioned across separate containers. We observed a neglibible increase in service latency for the multiple container deployment over a single container.Comment: 6 pages, conferenc

A Microservice Infrastructure for Distributed Communities of Practice

Non-formal learning in Communities of Practice (CoPs) makes up a significant portion of today’s knowledge gain. However, only little technological support is tailored specifically towards CoPs and their particular strengths and challenges. Even worse, CoPs often do not possess the resources to host or even develop a software ecosystem to support their activities. In this paper, we describe a distributed, microservice-based Web infrastructure for non-formal learning in CoPs. It mitigates the need for central infrastructures, coordination or facilitation and takes into account the constant change of these communities. As a real use case, we implement an inquiry-based learning application on-top of our infrastructure. Our evaluation results indicate the usefulness of this learning application, which shows promise for future work in the domain of community-hosted, microservice-based Web infrastructures for learning outside of formal settings

Container-based microservice architecture for local IoT services

Abstract. Edge services are needed to save networking and computational resources on higher tiers, enable operation during network problems, and to help limiting private data propagation to higher tiers if the function needing it can be handled locally. MEC at access network level provides most of these features but cannot help when access network is down. Local services, in addition, help alleviating the MEC load and limit the data propagation even more, on local level. This thesis focuses on the local IoT service provisioning. Local service provisioning is subject to several requirements, related to resource/energy-efficiency, performance and reliability. This thesis introduces a novel way to design and implement a Docker container-based micro-service system for gadget-free future IoT (Internet of Things) network. It introduces a use case scenario and proposes few possible required micro-services as of solution to the scenario. Some of these services deployed on different virtual platforms along with software components that can process sensor data providing storage capacity to make decisions based on their algorithm and business logic while few other services deployed with gateway components to connect rest of the devices to the system of solution. It also includes a state-of-the-art study for design, implementation, and evaluation as a Proof-of-Concept (PoC) based on container-based microservices with Docker. The used IoT devices are Raspberry Pi embedded computers along with an Ubuntu machine with a rich set of features and interfaces, capable of running virtualized services. This thesis evaluates the solution based on practical implementation. In addition, the thesis also discusses the benefits and drawbacks of the system with respect to the empirical solution. The output of the thesis shows that the virtualized microservices could be efficiently utilized at the local and resource constrained IoT using Dockers. This validates that the approach taken in this thesis is feasible for providing such services and functionalities to the micro and nanoservice architecture. Finally, this thesis proposes numerous improvements for future iterations

Provalets: Component-Based Mobile Agents as Microservices for Rule-Based Data Access, Processing and Analytics

Provalets are mobile rule agents for rule-based data access, semantic processing, and inference analytics. They can be dynamically deployed as microservices from Maven repositories into standardized container environments such as OSGi, where they can be used via simple REST calls. The programming model supports rapid prototyping and reuse of Provalets components to build Linked Enterprise Data applications where the sensible corporate data is not transmitted outside the enterprise, but instead the Provalets providing data processing and knowledge inference capabilities are moved closer to the data