Keynote Lectures

Abstract
A presentation that will point out the similarities of the previous socio-economic revolutions that changed human live and explain the positive and the disastrous economic consequences that have become visible already. It will focus as well in the Europe society that is not yet prepared well for this massive change. Why is this and how one can participate actively in this game to help providers and the customer to add some learnings.

Abstract
The use of scientific workflows (or simply workflows) has a long history in computer science. They became particularly popular when large distributed computing systems like the computational grid became available to solve very complex scientific problems. During the history of workflows many approaches and concrete workflow systems have been elaborated and many of them were intensively used by scientific communities. However, just this large variety of available workflow systems raised several important issues that should be solved in order to make workflows even more accepted and used for everyday science.

One important issue is the reuse and reproducibility of workflows. Scientific communities using different kind of workflow systems would like to collaborate and reuse the workflows developed by other scientific communities. The SHIWA European project has proposed several solutions to solve this problem. Interestingly their method called coarse-grain workflow interoperability became really usable when clouds appeared. Cloud systems provide the required technology by which workflows became really reproducible, shareable and even reusable inside new workflows.

Clouds also provide the possibility of constructing workflows as infrastructures that can dynamically be deployed in the cloud when needed in order to use them by other workflows. The WaaS (Workflow as a Service) concept enabled the introduction of the so-called infrastructure-aware workflows which is a new step in making workflows even more flexible.

The other direction where the WaaS concept can fruitfully be used is the creation of workflows that enable the processing of very large scientific data sets. A new workflow system called Flowbster has been developed based on the concept of workflow choreography and WaaS. It was designed to create efficient data pipelines in clouds by which very large data sets can efficiently be processed. The Flowbster workflow can be deployed in the target cloud as a virtual infrastructure through which the data to be processed can flow and meanwhile it flows through the workflow, it is transformed as the business logic of the workflow defines it. Instead of using the enactor based workflow concept Flowbster applies the service choreography concept where the workflow nodes directly communicate with each other. Workflow nodes are able to recognize if they can be activated with a certain data set without the interaction of a central control service like the enactor in service orchestration workflows. As a result Flowbster workflows implement a much more efficient data path through the workflow than service orchestration workflows. A Flowbster workflow works as a data pipeline enabling the exploitation of pipeline parallelism, workflow parallel branch parallelism and node scalability parallelism. The Flowbster workflow can be deployed in the target cloud on-demand based on the underlying Occopus cloud deployment and orchestrator tool. Occopus guarantees that the workflow can be deployed in any major type of IaaS clouds (OpenStack, OpenNebula, Amazon, CloudSigma). Performance results show the viability of using Flowbster workflows on top of even hybrid clouds.

Abstract
Cloud has tended to be characterised by large-scale data centre provision, with economic advantages as come with ease of centralized provision at relatively few locations. This has allowed for, amongst other advantages, ease of dynamic usage scaling, uptake of increasingly diverse capabilities, and use of significant storage capacity. However, such provisions have tended to be at some distance from specific needs, proving problematic where responsiveness is critical, and this offers motivation for reappraisal of more proximate provisions. Consequently, notions such as Multi-access Edge Computing (MEC), are becoming attractive, particularly when given close relationship to future telecommunications capabilities as promised by 5G. This talk will discuss the extension of Cloud capabilities to Edge, identifying some of the continued and upcoming challenges to address when creating systems able to exploit architectures involving either, or both, of these.

Abstract
Traditional manufacturing has been characterized by limited data exchange between systems, machines and processes throughout the product development life cycle. Recent initiatives such as the Industrial IoT, or Industry 4.0, as it has been dubbed, are fundamentally reshaping the industrial landscape by promoting connected smart manufacturing solutions that realize a “digital thread” which connects all aspects of manufacturing including all data and operations involved in the production of goods and services. These developments enable a “digital-twin” model of the connected ‘smart’ factory of the future where computer-driven systems create a virtual copy of the physical world and help make decentralised decisions with much higher degree of accuracy. The end result is an ecosystem of connected factory sites, plants, and self-regulating machines able to customize output, allocate resources optimally to offer a seamless transition between the physical and digital worlds of design, construction, assembly and production. It is connectivity that provides complete end-to-end data and service visibility throughout the entire product development lifecycle.
This talk will examine new concepts of manufacturing in the factory of the future and its enabling technologies, which include cloud, service-oriented computing, IoT, big data and analytics, and augmented reality. It will explain how these technologies have the ability to efficiently extract meaningful data and insights from manufacturing systems and processes, and transform traditional plants into connected smart collaborative factories. Here, collected data is harnessed to offer as-a-service products: products on the assembly line that have the ability to inform users and shop floor machinery how they are to be processed and how impending issues can be resolved. This new direction is highly-connected and knowledge-enabled factories, where devices, production equipment, production services and processes are connected - offering decision-making support on the basis of real-time production data - are continuously monitored, and optimized.