High Speed Simulation Analytics

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0

Commercial Use of WS-PGRADE/gUSE

Although originally an academic and research product, the WS-PGRADE/gUSE framework is increasingly applied by commercial institutions too. Within the SCI-BUS project, several commercial gateways have been developed by various companies. WS-PGRADE/gUSE is also intensively used within another European research project, CloudSME (Cloud-based Simulation Platform for Manufacturing and Engineering). This chapter provides an overview and de-scribes in detail some commercial WS-PGRADE/gUSE based gateway implemen-tations. Two representative case studies from the SCI-BUS project, the Build and Test portal and the eDOX Archiver Gateway are introduced. An overview of WS-PGRADE/gUSE based gateways for running simulation applications in the cloud within the CloudSME project is also provided

The CloudSME Simulation Platform and its Applications: A Generic Multi-cloud Platform for Developing and Executing Commercial Cloud-based Simulations

Simulation is used in industry to study a large variety of problems ranging from increasing the productivity of a manufacturing system to optimizing the design of a wind turbine. However, some simulation models can be computationally demanding and some simulation projects require time consuming experimentation. High performance computing infrastructures such as clusters can be used to speed up the execution of large models or multiple experiments but at a cost that is often too much for Small and Medium-sized Enterprises (SMEs). Cloud computing presents an attractive, lower cost alternative. However, developing a cloud-based simulation application can again be costly for an SME due to training and development needs, especially if software vendors need to use resources of different heterogeneous clouds to avoid being locked-in to one particular cloud provider. In an attempt to reduce the cost of development of commercial cloud-based simulations, the CloudSME Simulation Platform (CSSP) has been developed as a generic approach that combines an AppCenter with the workflow of the WS-PGRADE/gUSE science gateway framework and the multi-cloud-based capabilities of the CloudBroker Platform. The paper presents the CSSP and two representative case studies from distinctly different areas that illustrate how commercial multi-cloud-based simulations can be created

Enabling Cloud-based Computational Fluid Dynamics with a Platform-as-a-Service Solution

Computational Fluid Dynamics (CFD) is widely used in manufacturing and engineering from product design to testing. CFD requires intensive computational power and typically needs high performance computing to reduce potentially long experimentation times. Dedicated high performance computing systems are often expensive for small-to-medium enterprises (SMEs). Cloud computing claims to enable low cost access to high performance computing without the need for capital investment. The CloudSME Simulation Platform aims to provide a flexible and easy to use cloud-based Platform-as-a-Service (PaaS) technology that can enable SMEs to realize the benefits of high performance computing. Our Platform incorporates workflow management and multi-cloud implementation across various cloud resources. Here we present the components of our technology and experiences in using it to create a cloud-based version of the TransAT CFD software. Three case studies favourably compare the performance of a local cluster and two different clouds and demonstrate the viability of our cloud-based approach

Business models for cloud computing: Experiences from developing Modeling & Simulation as a Service applications in industry

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Exploring the E-science Knowledge Base through co-citation analysis

E-Science is the “science of this age”; it is realized through collaborative scientific enquiry which requires utilization of non-trivial amounts of computing resources and massive data sets. In this paper we explore the e-Science knowledge base through co-citation analysis of extant literature. Our objective is to use the knowledge domain visualization software CiteSpace to identifying the turning point articles and authors. In other words, our analysis is not solely based on tabulating the frequency of co-cited articles and authors, but the identification of landmark articles and authors irrespective of their co-citation count. The dataset for this analysis is downloaded from the ISI Web of Science and includes approx. 1000 articles. It is expected that this paper will be an important source of reference for academics and researchers working in the area of e-Science and its three technology enablers - grid computing, desktop grids and cloud computing

Towards a Deadline-Based Simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework

Facilitating the analysis of a UK national blood service supply chain using distributed simulation

In an attempt to investigate blood unit ordering policies, researchers have created a discrete-event model of the UK National Blood Service (NBS) supply chain in the Southampton area of the UK. The model has been created using Simul8, a commercial-off-the-shelf discrete-event simulation package (CSP). However, as more hospitals were added to the model, it was discovered that the length of time needed to perform a single simulation severely increased. It has been claimed that distributed simulation, a technique that uses the resources of many computers to execute a simulation model, can reduce simulation runtime. Further, an emerging standardized approach exists that supports distributed simulation with CSPs. These CSP Interoperability (CSPI) standards are compatible with the IEEE 1516 standard The High Level Architecture, the defacto interoperability standard for distributed simulation. To investigate if distributed simulation can reduce the execution time of NBS supply chain simulation, this paper presents experiences of creating a distributed version of the CSP Simul8 according to the CSPI/HLA standards. It shows that the distributed version of the simulation does indeed run faster when the model reaches a certain size. Further, we argue that understanding the relationship of model features is key to performance. This is illustrated by experimentation with two different protocols implementations (using Time Advance Request (TAR) and Next Event Request (NER)). Our contribution is therefore the demonstration that distributed simulation is a useful technique in the timely execution of supply chains of this type and that careful analysis of model features can further increase performance

Innovations in Simulation: Experiences with Cloud-based Simulation Experimentation

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud Computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud Computing can offer. Based on several years’ experience of innovation in this area, this article presents our experiences in developing Cloud Computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community

Industry Simulation Gateway on a Scalable Cloud

Large scale simulation experimentation typically requires significant computational resources due to an excessive number of simulation runs and replications to be performed. The traditional approach to provide such computational power, both in academic research and industry/business applications, was to use computing clusters or desktop grid resources. However, such resources not only require upfront capital investment but also lack the flexibility and scalability that is required to serve a variable number of clients/users efficiently. This paper presents how SakerGrid, a commercial desktop grid based simulation platform and its associated science gateway have been extended towards a scalable cloud computing solution. The integration of SakerGrid with the MiCADO automated deployment and autoscaling framework supports the execution of multiple simulation experiments by dynamically allocating virtual machines in the cloud in order to complete the experiment by a user-defined deadline