In recent decades, it has become a significant tendency for industrial manufacturers to adopt decentralization as a new manufacturing paradigm. This enables more efficient operations and facilitates the shift from mass to customized production. At the same time, advances in data analytics give more insights into the production lines, thus improving its overall productivity. The primary objective of this paper is to apply a decentralized architecture to address new challenges in industrial analytics. The main contributions of this work are therefore two-fold: (1) an assessment of the microservices’ feasibility in industrial environments, and (2) a microservices-based architecture for industrial data analytics. Also, a prototype has been developed, analyzed, and evaluated, to provide further practical insights. Initial evaluation results of this prototype underpin the adoption of microservices in industrial analytics with less than 20ms end-to-end processing latency for predicting movement paths for 100 autonomous robots on a commodity hardware server. However, it also identifies several drawbacks of the approach, which is, among others, the complexity in structure, leading to higher resource consumption.
@cite_19 propose a simulation-based architecture for ) Cyber-Physical systems are systems with the seamless, real-time interaction between computing elements and physical assets using intelligent data management, analytics and computational capability @cite_7 . at shop-floor level, providing an environment for Digital Twins are representations of real-world assets (including the assets in designing building stage) created with the ability to collect and synthesize data from various sources including physical data, manufacturing data, operational data and insights from analytics software @cite_2 . along the whole plant life-cycle. The proposed platform implements a microservice IoT-Big Data architecture supporting the publication of multidisciplinary simulation models and managing streams of data coming from the shop-floor for real-digital synchronization. Microservices architecture is applied in their support infrastructure, in order to manage the DTs. In our proposal, we extend this work by employing microservices also for simulating physical assets to decentralize the whole system. Rather than storing all digital copies of physical assets in one place, building them as microservices allows more flexibility in deployment strategies. For each service, a best physical location to deploy could be determined based on various criteria. This is an important foundation to enable automated and QoS-aware deployment strategies.
@cite_19 @cite_7 @cite_2
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Abstract In recent years a considerable effort has been spent by research and industrial communities in the digitalization of production environments with the main objective of achieving a new automation paradigm, more flexible, responsive to changes, and safe. This paper presents the architecture, and discusses the benefits, of a distributed middleware prototype supporting a new generation of smart-factory-enabled applications with special attention paid to simulation tools. Devised within the scope of MAYA EU project, the proposed platform aims at being the first solution capable of empowering shop-floor Cyber-Physical-Systems (CPSs), providing an environment for their Digital Twin along the whole plant life-cycle. The platform implements a microservice IoT-Big Data architecture supporting the distributed publication of multidisciplinary simulation models, managing in an optimized way streams of data coming from the shop-floor for real-digital synchronization, ensuring security and confidentiality of sensible data. In order to accurately predict future states of a smart cyber-physical system, which can change its behavior to a large degree in response to environmental influences, the existence of precise models of the system and its surroundings is demandable. In machine engineering, ultra-high fidelity simulations have been developed to better understand both constraints in system design and possible consequences of external influences during the system's operation. These digital twins enable further applications in software design for complex cyber-physical systems as online planning methods can utilize good simulations to continuously optimize the system behavior, yielding a software architecture framework based on the information flow between the cyber-physical system, its physical environment and the digital twin model. Recent advances in ICT enable the evolution of the manufacturing industry to meet the new requirements of the society. Cyber-physical systems, Internet-of-Things (IoT), and Cloud computing, play a key role in the fourth industrial revolution known as Industry 4.0. The microservice architecture has evolved as an alternative to SOA and promises to address many of the challenges in software development. In this paper, we adopt the concept of microservice and describe a framework for manufacturing systems that has the cyber-physical microservice as the key construct. The manufacturing plant processes are defined as compositions of primitive cyber-physical microservices adopting either the orchestration or the choreography pattern. IoT technologies are used for system integration and model-driven engineering is utilized to semi-automate the development process for the industrial engineer, who is not familiar with microservices and IoT. Two case studies demonstrate the feasibility of the proposed approach. Cyber-physical systems are networked, component-based, real-time systems that control and monitor the physical world. We need software architectures that limit fault-propagation across unreliable components. This paper introduces our simplex reference model which is distinguished by: a plant being controlled in an external context, a machine performing the control, a domain model that estimates the plant state, and the safety requirements that must be met. The simplex reference model assists with constructing CPS architectures which limit fault-propagation. We present a representative case study to highlight the ideas behind the model and our particular decomposition.