Configuration Management for Industrial Automation

Core Issue: Developing and maintaining industrial automation systems requires a lot of coordination of programming code. To support this task a configuration management system can be used but many companies of today find it hard to select a system that suites them best. Purpose: The purpose of this thesis is to examine various configuration management systems and evaluate them according to the needs that Tetra Pak Processing Systems 14 Business Unit Dairy, Beverage and Prepared Food (BU DBF) has. The Platform & Standards department, where the thesis has been written, has started to work with templates that are shared between different projects. This leads to more versions which make it harder to organize and structure the work. Methodology: Qualitative information has been gathered through interviews and quantitative information has been gathered through a survey. Using configuration management requires the user to understand the theory behind it and the area has been studied thoroughly. Various tests on different tools were performed according to test specifications. Conclusions: For BU DBF the largest problem was to find a configuration management tool that supported binary files, which are used for PLC and HMI, from several manufacturers. Both freeware applications and commercial tools were tested. The tool that best fulfilled the test specifications was VersionWorks from GepaSoft/Rockwell Automation. Implementing configuration management within BU DBF is something that definitely should be done and based on the results of this thesis

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Cloud Testing: A Survey on Tools and Open Challenges

Cloud Computing is growing exponentially across organizations and it has a vast impact on the way traditional computation and software testing is conducted. The web based applications these days have different configuration setting, and different deployment requirements. The main focus of Cloud Computing is todeliverreliable, secured, fault-tolerant and elastic infrastructures for hosting Internet-based web applications. Computing the scheduling policies and allocation policy for resources which affect the cloud infrastructure (i.e. hardware, software services) for various web application under fluctuating load and system size is highly challenging problem to deal with. Testing cloud based web applicationsdemands for novel testing methods and tools. This paper is a survey on the growing need of cloud testing, the tools used and the open challenges in the area of cloud testing

An Industry-Based Study on the Efficiency Benefits of Utilising Public Cloud Infrastructure and Infrastructure as Code Tools in the IT Environment Creation Process

The traditional approaches to IT infrastructure management typically involve the procuring, housing and running of company-owned and maintained physical servers. In recent years, alternative solutions to IT infrastructure management based on public cloud technologies have emerged. Infrastructure as a Service (IaaS), also known as public cloud infrastructure, allows for the on-demand provisioning of IT infrastructure resources via the Internet. Cloud Service Providers (CSP) such as Amazon Web Services (AWS) offer integration of their cloud-based infrastructure with Infrastructure as Code (IaC) tools. These tools allow for the entire configuration of public cloud based infrastructure to be scripted out and defined as code. This thesis hypothesises that the correct utilization of IaaS and IaC can offer an organisation a more efficient type of IT infrastructure creation system than that of the organisations traditional method. To investigate this claim, an industry-based case study and survey questionnaire were carried out as part of this body of work. The case study involved the replacement of a manually managed IT infrastructure with that of the public cloud, the creation of which was automated via a framework consisting of IaC and related automation tools. The survey questionnaire was created with the intent to corroborate or refute the results obtained in the case study in the context of a wider audience of organisations. The results show that the correct utilization of IaaS and IaC technologies can provide greater efficiency in the management of IT networks than the traditional approac

Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe

Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study

Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users

The Making of Cloud Applications An Empirical Study on Software Development for the Cloud

Cloud computing is gaining more and more traction as a deployment and provisioning model for software. While a large body of research already covers how to optimally operate a cloud system, we still lack insights into how professional software engineers actually use clouds, and how the cloud impacts development practices. This paper reports on the first systematic study on how software developers build applications in the cloud. We conducted a mixed-method study, consisting of qualitative interviews of 25 professional developers and a quantitative survey with 294 responses. Our results show that adopting the cloud has a profound impact throughout the software development process, as well as on how developers utilize tools and data in their daily work. Among other things, we found that (1) developers need better means to anticipate runtime problems and rigorously define metrics for improved fault localization and (2) the cloud offers an abundance of operational data, however, developers still often rely on their experience and intuition rather than utilizing metrics. From our findings, we extracted a set of guidelines for cloud development and identified challenges for researchers and tool vendors