Though many industrial problems involving gas/liquid flows can be simulated via fairly simple models, there are other cases where the number of different forces and their direction can not be handled by this approach. A typical example is that of flow in a bend. If it is just the pressure drop across the bend that is required, then there are simple methods, more or less accurate, which can be invoked. However, if more detailed information is required, such as how are the liquid and gas disposed about the bend, then more advanced methods are required, methods which hitherto are not available. Calculation methods for multiphase flow are not yet at a stage that they can handle all the problem industry has to solve. Therefore developments have to be produced. However, to achieve these developments there is a need for information from experiment to inform the modeling and to validate the product models. In spite of the extensive multiphase flow literature, such information if often limited and most certainly confined to pipe diameters far smaller than used in industry and with physical properties very different to those which industry is dealing with. The programme of work proposed here aims to push forward developments in modeling and provide experimental observations/measurement to help this development.

Cloud environments are notorious for their lack of stability in performance characteristics, a feature that makes it extremely difficult for owners of time-critical applications to make the decisive step for migration and owners of SaaS to be unable to present performance vs cost tradeoffs to their customers when acting as IaaS customers. CLOUDPERFECT aims at delivering a set of tools and processes that will enable a) Cloud providers to enhance the stability and performance effectiveness of their infrastructures, through modelling/understanding of the overheads, optimal groupings of concurrently running services, runtime analysis and adaptation, thus gaining a competitive advantage b) Cloud adopters to understand the computational nature of their applications, investigate abstracted and understandable QoS metrics for providers ranking, minimize the time of procurement and provider selection processes, automate deployment and orchestration processes, balance their selection between cost and performance to optimize their competitiveness, define according SLA levels (if on the SaaS level) and monitor the maintenance of their SLA c) 3rd parties to act as independent validators of Cloud QoS features, through a constant monitoring, benchmarking and evaluation process, filling a gap in the current brokerage/consultancy domain for performance evaluation and SLA auditing. The innovation action starts from mature existing prototypes derived from previous EC funded projects and aims at extending their TRL levels in order to support a spin-off entity to be created for the role of QoE Assessment Broker and Toolkit Consultant. The project will cover extensive experimentation with relation to the applicability of the envisioned toolkits in 4 facilities, while focusing on two heavy-weight industrial cases such as CFD and ERP/CRM, for which the overall value chain has been represented in the consortium, targeting domains such as manufacturing and telecom.

MORPHEMIC proposes a unique way of adapting and optimizing Cloud computing applications by introducing the novel concepts of polymorph architecture and proactive adaptation. The former is when a component can run in different technical forms, i.e. in a Virtual Machine (VM), in a container, as a big data job, or as serverless components, etc. The technical form of deployment is chosen during the optimization process to fulfil the user’s requirements and needs. The quality of the deployment is measured by a user defined and application specific utility. Depending on the application’s requirements and its current workload, its components could be deployed in various forms in different environments to maximize the utility of the application deployment and the satisfaction of the user. Proactive adaptation is not only based on the current execution context and conditions but aims to forecast future resource needs and possible deployment configurations. This ensures that adaptation can be done effectively and seamlessly for the users of the application. The MORPHEMIC deployment platform will therefore be very beneficial for heterogeneous deployment in distributed environments combining various Cloud levels including Cloud data centres, edge Clouds, 5G base stations, and fog devices. Advanced forecasting methods, including the ES-Hybrid method recently winning the M4 forecasting competition, will be used to achieve the most accurate predictions. The outcome of the project will be implemented in the form of the complete solution, starting from modelling, through profiling, optimization, runtime reconfiguration and monitoring. Then the MORPHEMIC implementation will be integrated as a pre-processor for the existing MELODIC platform extending its deployment and adaptation capabilities beyond the multi-cloud and cross-cloud to the edge, 5G, and fog. This approach allows for a path to early demonstrations and commercial exploitation of the project results.