803 research outputs found
Management and Security of IoT systems using Microservices
Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and fault tolerance. We aim to solve the above issues in a microservice architecture. We build a data pipeline for IoT devices to send data through a messaging platform Kafka and monitor the devices using the collected data by making real time dashboards and a machine learning model to give better insights of the data. For proof of concept, we test the proposed solution on a heterogeneous cluster, including Raspberry Pi’s and IoT devices from different vendors. We validate our design by presenting some simple experimental results
Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions
Edge and Fog computing paradigms utilise distributed, heterogeneous and
resource-constrained devices at the edge of the network for efficient
deployment of latency-critical and bandwidth-hungry IoT application services.
Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up
with the rapid development and deployment needs of the fast-evolving IoT
applications. Due to the fine-grained modularity of the microservices along
with their independently deployable and scalable nature, MSA exhibits great
potential in harnessing both Fog and Cloud resources to meet diverse QoS
requirements of the IoT application services, thus giving rise to novel
paradigms like Osmotic computing. However, efficient and scalable scheduling
algorithms are required to utilise the said characteristics of the MSA while
overcoming novel challenges introduced by the architecture. To this end, we
present a comprehensive taxonomy of recent literature on microservices-based
IoT applications scheduling in Edge and Fog computing environments.
Furthermore, we organise multiple taxonomies to capture the main aspects of the
scheduling problem, analyse and classify related works, identify research gaps
within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey
Microservice Architecture Reconstruction and Visualization Techniques: A Review
Microservice system solutions are driving digital transformation; however,
fundamental tools and system perspectives are missing to better observe,
understand, and manage these systems, their properties, and their dependencies.
Microservices architecture leads towards decentralization, which implies many
advantages to system operation; it, however, brings challenges to their
development. Microservice systems often lack a system-centric perspective that
would help engineers better cope with system evolution and quality assessment.
In this work, we explored microservice-specific architecture reconstruction
based on static analysis. Such reconstruction typically results in system
models to visualize selected system-centric perspectives. Conventional models
involve 2D methods; however, these methods are limited in utility when services
proliferate. We considered various architectural perspectives relevant to
microservices and assessed the relevancy of the traditional method, comparing
it to alternative data visualization using 3D space. As a representative of the
3D method, we considered a 3D graph model presented in augmented reality. To
begin testing the feasibility of deriving such perspectives from microservice
systems, we developed and implemented prototype tools for software architecture
reconstruction and visualization of compared perspectives. Using these
prototypes, we performed a small user study with software practitioners to
highlight the potentials and limitations of these innovative visualizations
used for common practitioner reasoning and tasks
Implementation of Data Abstraction Layer Using Kafka on SEMAR Platform for Air Quality Monitoring
Urbanization and fast-growing industries causing air quality in urban areas to be bad and even tend to be dangerous. In addition, the largest percentage of energy emissions come from the transportation sector, specifically on road transportation. Therefore, the need for a quality detection system that is capable of distributing and displaying large data information in real-time cannot be resolved by the system currently used by the government. This research offers a solution to the implementation of data abstraction in cloud computing which is built using the concept microservice architecture and integrated with mobile-based sensors to detect air quality in real-time. This solution consists of integrated cloud computing services using Smart Environment Monitoring and Analytical in Real-time (SEMAR) and Vehicles as Mobile Sensor Networks (VaaMSN) to detecting air quality. SEMAR was built with microservice references consist of data abstraction, communication, data analytical with business analytics proccess, data storage with Big data service and also real-time visualization in maps, chart, and table through dasboard website. Through the experiments that we did show that the microservice of data abstraction layer can be installed at the SEMAR stage indicating that the average delay in sending information is around 0.09 ms (90μs), this indicates that the system can be said to be real-time. With specific and real-time locations in data visualization, the government can use this method as an new alternative method of air quality
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