Industrial automation is critical in today's industry. The majority of new scientific and technological advancements are either enabling technologies or industrial automation application areas. In the past, the two main forms of control systems were distributed control systems (DCS) and programmable logic controllers (PLCs). PLCs have been referred as the "brain" of production systems because they provide the capacity to meet interoperability, reconfigurability, and portability criteria. Today's industrial automation systems rely heavily on control software to ensure that the automation process runs smoothly and efficiently. Furthermore, requirements like flexibility, adaptability, and robustness add to the control software's complexity. As a result, new approaches to building control software are required. The International Electrotechnical Commission attempted to meet these new and impending demands with the new IEC 61499 family of standards for distributed automation systems. The IEC 61499 standard specifies a high-level system design language for distributed data and control. With the advancement of these technologies like edge/fog computing and IIoT, how the control software in future smart factory managed is discussed here. This study aims to do a systematic literature review on PLC, software containers, edge/fog computing and IIoT for future industrial use. The objective is to identify the correspondence between the functional block (IEC 61499) and the container technology such as Docker. The impact of edge computing and the internet of things in industrial automation is also analysed. Since the aim is to do a comparative study, a qualitative explorative study is done, with the purpose to gather rich insight about the field. The analysis of the study mainly focused on four major areas such as deployment, run time, performance and security of these technologies. The result shows that containerisation or container based solutions is the basis for future automation as it outperforms virtual machines in terms of deployment, run time, performance and security.

Under de senaste åren har stora datamängder blivit allt vanligare för beslutsfattande och analys. Maskininlärning och matematiska beräkningar är två avgörande metoder som används för detta. Dessa beräkningar kan dock vara tidskrävande, och de kräver högpresterande datorer som är utmanande att skala upp. Raspberry Pi är en liten, kraftfull och billig dator som lämpar sig för parallella beräkningar. Kubernetes är en öppen källkodsplattform för att hantera containerbaserade applikationer som tillåter automatisk skalning av mjukvaruapplikationer. Genom att kombinera Raspberry Pi med Kubernetes kan ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning skapas. I denna studie undersöks möjligheten att bygga ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning med hjälp av Raspberry Pi och Kubernetes. Det kommer att göras teoretisk forskning kring Kubernetes och Raspberry Pi, designa ett system för matematiska beräkningar och maskininlärning, implementera systemet genom att installera och konfigurera Kubernetes på flera Raspberry Pi:s, mäta och utvärdera systemets prestanda och skalbarhet samt presentera studiens resultat. Resultatet visade att användningen av Raspberry Pi i kombination med Kubernetes för att utföra matematiska beräkningar är både kostnadseffektiv och skalbar. När det gäller prestanda kunde systemet hantera intensiva beräkningsuppgifter på ett tillfredsställande sätt, vilket visar sin potential som en lösning för storskalig dataanalys. Förbättringar i systemdesign och mjukvaruoptimering kan ytterligare öka effektiviteten och prestanda / In the recent years, large data sets have become more often used for decision-making and analysis. Machine learning and mathematical calculations are two crucial methods employed for this. However, these computations may be time-consuming, and they require highperformance computers that are challenging to scale up. Raspberry Pi is a small, powerful, and cheap computer suitable for parallel calculations. Kubernetes is an open-source platform for managing container-based applications that allows automatic scaling of software applications. By combining Raspberry Pi with Kubernetes, a cost-effective and scalable system for mathematical calculations and machine learning can be created. In this study, the possibility of building a cost-effective and scalable system for mathematical calculations and machine learning using Raspberry Pi and Kubernetes is investigated. There will be theoretical research on Kubernetes and Raspberry Pi, design a system for mathematical calculations and machine learning, implement the system by installing and configuring Kubernetes on multiple Raspberry Pi's, measure and evaluate the system's performance and scalability, and present the study's results. The result showed that the use of Raspberry Pi in combination with Kubernetes to perform mathematical calculations is both cost-effective and scalable. In terms of performance, the system was able to handle intensive computational tasks satisfactorily, demonstrating its potential as a solution for large-scale data analysis. Improvements in system design and software optimization can further increase efficiency and performance.