Utilizing Microservices Architecture for Enhanced Service Sharing in IoT Edge Environments

Latency sensitive IoT (Internet of Things) applications at the edge are designed using a microservice-based architecture. This architecture is comprised of a set of microservices, each implementing a simple functionality with clearly-defined interfaces, and applications are constructed by selecting and interconnecting appropriate microservices. To understand the performance implications of using a microservice-based architecture for constructing IoT applications at the edge, this paper provides a detailed evaluation based on an actual prototpye implementation and performance measurement. In our setup, an edge server fulfills dual roles of being an administrative controller of the IoT infrastructure and satisfying application&rsquo;s latency and privacy constraints. We demonstrate the utility of this architecture by isolated and independent implementation of different microservices, constructing an IoT application by interconnecting these microservices, and potential sharing of microservices between different IoT applications running simultaneously to enhance interoperability. Finally, we provide an extensive performance evaluation focusing on application latency as well as CPU and memory consumption. &nbsp;</p

Scalable and High Available Kubernetes Cluster in Edge Environments for IoT Applications

The number of IoT and sensor devices is expected to reach 25 billion by 2030. Many IoT appli- cations, such as connected vehicle and smart factory that require high availability, scalability, low latency, and security have appeared in the world. There have been many attempts to use cloud computing for IoT applications, but the mentioned requirements cannot be ensured in cloud environments. To solve this problem, edge computing has appeared in the world. In edge environments, containerization technology is useful to deploy apps with limited resources. In this thesis, two types of high available Kubernetes architecture (2 nodes with an external DB and 3 nodes with embedded DB) were surveyed and implemented using K3s distribution that is suitable for edges. By having a few experiments with the implemented K3s clusters, this thesis shows that the K3s clusters can provide high availability and scalability. We discuss the limitations of the implementations and provide possible solutions too. In addition, we provide the resource usages of each cluster in terms of CPU, RAM, and disk. Both clusters need only less than 10% CPU and about 500MB RAM on average. However, we could see that the 3 nodes cluster with embedded DB uses more resources than the 2 nodes + external DB cluster when changing the status of clusters. Finally, we show that the implemented K3s clusters are suitable for many IoT applications such as connected vehicle and smart factory. If an application that needs high availability and scalability has to be deployed in edge environments, the K3s clusters can provide good solutions to achieve the goals of the applications. The 2 nodes + external DB cluster is suitable for the applications where the amount of data fluctuate often, or where there is a stable connection with the external DB. On the other hand, the 3 nodes cluster will be suitable for the applications that need high availability of the database even in poor internet connection. ACM Computing Classification System (CCS) Computer systems organization → Embedded and cyber-physical systems Human-centered computing → Ubiquitous and mobile computin

PLC Virtualization and Software Defined Architectures in Industrial Control Systems

Today’s automation systems are going through a transition called Industry 4.0, referring to the Fourth Industrial Revolution. New concepts, such as cyber-physical systems, mi-croservices and Smart Factory are introduced. This brings up the question of how some of these new technologies can be utilized in Industrial Control Systems. Machines and production lines are nowadays controlled by hardware PLCs and this is considered as a state-of-the-art solution. However, the market demands are continuously increasing and pushing the industry e.g. to lower the operational costs and to develop more agile solutions. Industry 4.0 provides promising approaches to take a step forward and consider PLC virtualization. The purpose of this thesis was to evaluate PLC virtualization possibilities using different Software Defined Architectures. Requirements and benefits of different solutions were evaluated. The major objective of the case study was to compare container- and hypervisor-based virtualization solutions using Docker and KVM. The case study provides a modular and scalable IIoT solution in which a virtual PLC takes over the control instead of a hardware PLC. Node-RED was used as a runtime environment and an I/O-module was needed to set up a control loop test. Response time of the control loop was measured by capturing Modbus traffic with tcpdump. Multiple iterations were performed to show minimum, maximum, average, median and 90th pctl. latencies. The results indicate that the container-based solution has a smaller overhead than the hypervisor-based solution and it has a very little overhead in general. Peak latencies are a concern and even the average latencies show that this solution would not be suitable for any hard real-time or safety-related applications. Further investigation on the topic would be needed to estimate the actual potential of PLC virtualization on hard real-time applications. First of all, a more powerful hardware PC would be needed to perform such tests. Secondly, a faster industrial protocol than Modbus TCP/IP would be required. Perhaps another kind of approach would be needed to overcome the issues that were experienced in this case study. It would be interesting to test a direct communication between virtual PLC and I/O and use Node-RED nodes for example to trigger inputs. Anyhow, it seems that container-based solution is holding much promise as a virtualization approach

5G-Kube: Complex Telco Core Infrastructure Deployment Made Low-Cost

Network Function Virtualization (NFV) along with Software Defined Networking (SDN) have brought an evolution in telecommunications laying out the bases for 5G networks and its softwarization. Accordingly, new implementations of telecom standards, such as the 3GPP 5G Core, are defined as fully-virtualized infrastructures consisting of different components and leveraging a cloud-native approach. At the same time, standard-oriented solutions, such as ETSI Management and Orchestration (MANO), have emerged to master the complexity of Virtualized Network Functions (VNFs) orchestration, including 5G Core VNFs. While MANO operates at the NFV level, it also leverages existing cloud infrastructures for the deployment of VNFs by interoperating with resource orchestrators at the cloud level. From the business perspective, that requires telco operators to interact with different technology providers, from NFV/MANO software producers to cloud computing providers, and to hire technicians proficient in the technologies of both telco and computing worlds, that are a rather difficult human resourcing to find. The main claim of the article is that the Development and Operations (DevOps) tools in the IT world are mature enough to leverage them directly in the telco world, without superimposing other interlaced standard/software. That allows to significantly reduce OPEX cost of complex telco infrastructures by supporting all needed automation and by avoiding the combined use of (too) complex layered standards/software stacks, such as in the case of MANO. Accordingly, in this article, we leverage container-based technologies and Kubernetes to design and evaluate a novel deployment approach, called 5G-Kube, for softwarized 5G core networks. 5G-Kube, which is openly to the community, has been also evaluated in two different use cases of the 5G Core and Kubernetes deployment fitting, namely, Industry 4.0 and Smart Cities

Docker containers usage in the internet of things: a survey

The Internet of Things (IoT) opened the way for enabling many of our everyday objects (things) interact with their environment to collect data, analyze and automating jobs based on specific rules. Within the constraint environment, the requirement of lightweight IoT application are tremendously indeed required to ensure the IoT application can be run efficiently. Docker containers is a promising technology to enable IoT application running smoothly, fast and efficient. In this paper, an introduction to Docker is presented. Then we explore the usage of Docker containers in the IoT application. Finally, we briefly discuss why Docker containers are usage in the IoT application

Building an IoT platform based on service containerisation

IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.publishe

Microservices for Continuous Deployment, Monitoring and Validation in Cyber-Physical Systems: an Industrial Case Study for Elevators Systems

Cyber-Physical Systems (CPSs) are systems that integrate digital cyber computations with physical processes. The software embedded in CPSs has a long life-cycle, requiring constant evolution to support new requirements, bug fixes, and deal with hardware obsolescence. To date, the development of software for CPSs is fragmented, which makes it extremely expensive. This could be substantially enhanced by tightly connecting the development and operation phases, as is done in other software engineering domains (e.g., web engineering through DevOps). Nevertheless, there are still complex issues that make it difficult to use DevOps techniques in the CPS domain, such as those related to hardware-software co-design. To pave the way towards DevOps in the CPS domain, in this paper we instantiate part of the reference architecture presented in the H2020 Adeptness project, which is based on microservices that allow for the continuous deployment, monitoring and validation of CPSs. To this end, we elaborate a systematic methodology that considers as input both domain expertise and a previously defined taxonomy for DevOps in the CPS domain. We obtain a generic microservice template that can be used in any kind of CPS. In addition, we instantiate this architecture in the context of an industrial case study from the elevation domain

Towards Modular and Plug-and-Produce Manufacturing Apps

Industry 4.0 redefines manufacturing systems as smart and connected systems where software solutions provide additional capabilities to the manufacturing equipment. However, the connection of manufacturing equipment with software solutions is challenging due to poor interoperability between different original equipment manufacturers (OEMs), making it difficult to integrate into the manufacturing system. Hence, there is a need for a methodology to develop modular "plug-and-produce" applications in the manufacturing domain to meet the requirements of Industry 4.0. This work investigates the "appification" of manufacturing processes where the goal is to subdivide the process into independent, re-configurable digital manufacturing applications. In this context, "appification" means separating the digital implementation from the physical implementation of the system by making the former modular and independent so that digital implementations can be re-used without depending on the physical parts of the system. In this paper a framework for the development of such manufacturing "apps" is presented. This framework consists of four main elements: a modular plug-and-produce architecture, a manufacturing apps development kit, a communication protocol, and a construction methodology. The modular plug-and-produce architecture is developed using the recent advances in microservices, containerization, and communication technologies. The manufacturing apps development kit (MAPPDK) has been developed to facilitate the implementation of manufacturing apps using high-level programming languages. MAPPDK allows to control manufacturing equipment from external computational devices. The methodology for developing different modules for different types of manufacturing processes is also provided. The proof of concept is shown experimentally by the "appification" of a sorting process using an industrial robot arm, a gripping end-effector, a third-party vision camera, and an intelligent vision module