Distributed simulation optimization and parameter exploration framework for the cloud

Simulation models are becoming an increasingly popular tool for the analysis and optimization of complex real systems in different fields. Finding an optimal system design requires performing a large sweep over the parameter space in an organized way. Hence, the model optimization process is extremely demanding from a computational point of view, as it requires careful, time-consuming, complex orchestration of coordinated executions. In this paper, we present the design of SOF (Simulation Optimization and exploration Framework in the cloud), a framework which exploits the computing power of a cloud computational environment in order to carry out effective and efficient simulation optimization strategies. SOF offers several attractive features. Firstly, SOF requires “zero configuration” as it does not require any additional software installed on the remote node; only standard Apache Hadoop and SSH access are sufficient. Secondly, SOF is transparent to the user, since the user is totally unaware that the system operates on a distributed environment. Finally, SOF is highly customizable and programmable, since it enables the running of different simulation optimization scenarios using diverse programming languages – provided that the hosting platform supports them – and different simulation toolkits, as developed by the modeler. The tool has been fully developed and is available on a public repository1 under the terms of the open source Apache License. It has been tested and validated on several private platforms, such as a dedicated cluster of workstations, as well as on public platforms, including the Hortonworks Data Platform and Amazon Web Services Elastic MapReduce solution

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

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

Individual-based modeling of white-tailed deer (Odocoileus virginianus) movements and epizootiology

White-tailed deer (Odocoileus virginianus) are important game mammals and potential reservoirs of diseases of domestic livestock, so diseases of deer are of great concern to wildlife managers. In many situations, models can be useful for integrating existing data, understanding disease transmission patterns, and predicting effects on host populations. Individual-based modeling (IBM) has become more commonplace in ecology as a tool to link individual behavior to population dynamics and community interactions, especially for gauging the effects of management actions. Spatially explicit IBMs are especially useful when ecological processes, such as disease transmission, are affected by the spatial composition of the environment. I developed a spatially explicit IBM, DeerLandscapeDisease (DLD), to simulate direct and indirect disease transmission in white-tailed deer. Using data from GPS-collared deer in southern Illinois, I developed methods to identify habitats and times of high contact probability. I parameterized movement models, for use in DLD, using field data from GPS-collared deer in both southern and east-central Illinois. I then used DLD to simulate deer movements and epizootiology in two different landscapes: a predominantly agricultural landscape with fragmented forest patches in east-central Illinois and a landscape dominated by forest in southern Illinois. Behavioral and demographic parameters that could not be estimated from the field data were estimated using published literature of deer ecology. I assumed that bioavailability of infectious pathogens deposited in the environment decreased exponentially. Transmission probabilities were estimated by fitting to published trends in infection prevalence, assuming that infection probability during an encounter was equal for all age classes, so infection prevalence varied with sex- and age-specific behavior. DLD simulations of chronic wasting disease epizootiology demonstrated significant effects of landscape structure, social behavior, and mode of transmission on prevalence, emphasizing the importance of spatial, temporal and behavioral heterogeneity in disease modeling. These results demonstrate the utility of IBMs in incorporating spatio-temporal variables as well as animal behavior when predicting and modeling disease spread

A methodology for developing scientific software applications in science gateways

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDistributed Computing Infrastructures (DCIs) have emerged as a viable and affordable solution to the computing needs of communities of practice that may require the need to improve system performance or enhance the availability of their scientific applications. According to the literature, the ease of access and several other issues which relate to the interoperability among different resources are the biggest challenges surrounding the use of these infrastructures. The traditional method of using a Command Line Interface (CLI) to access these resources is difficult and can make the learning curve quite steep. This approach can result in the low uptake of DCIs as it prevents potential users of the infrastructures from adopting the technology. Science Gateways have emerged as a viable option that are used to realise the high-level scientific domain-specific user interfaces that hide all the details of the underlying infrastructures and expose only the science-specific aspects of the scientific applications to be executed in the various DCIs. A Science Gateway is a digital interface to advanced technologies which is used to provide adequate support for science and engineering research and education. The focus of this study therefore is to propose and implement a Methodology for dEveloping Scientific Software Applications in science GatEways (MESSAGE). This will be achieved by testing an approach which is considered to be appropriate for developing applications in Science Gateways. In the course of this study, several Science Gateway functionalities obtained from the review of literature which may be utilised to provide services for different communities of practice are highlighted. To implement the identified functionalities, this study utilises the methodology for developing scientific software applications in Science Gateways. In order to achieve this purpose, this research therefore adopts the Catania Science Gateway Framework (CSGF) and the Future Gateway approach to implement the methods and ideas described in the proposed methodology, as well the essential services of Science Gateways discussed throughout the thesis. In addition, three different set of scientific software applications are utilised for the implementation of the proposed methodology. While the first application primarily serves as the case study for implementing the methodology discussed in this thesis, a second application is used to evaluate the entire process. Furthermore, several other real-life scientific applications developed (using two distinctly different Science Gateway frameworks) are also utilised for the purpose of evaluation. Subsequently, a revised MESSAGE methodology for developing scientific software applications in Science Gateways is discussed in the latter Chapter of this thesis. Following from the implementation of both scientific software applications which sees the use of portlets to execute single experiments, a study was also conducted to investigate ways in which Science Gateways may be utilised for the execution of multiple experiments in a distributed environment. Finally, similar to making different scientific software applications accessible and available (worldwide) to the communities that need them, the processes involved in making their associated research outputs (such as data, software and results) easily accessible and readily available are also discussed. The main contribution of this thesis is the MESSAGE methodology for developing scientific software applications in Science Gateways. Other contributions which are also made in different aspects of this research include a framework of the essential services required in generic Science Gateways and an approach to developing and executing multiple experiments (via Science Gateway interfaces) within a distributed environment. To a lesser extent, this study also utilises the Open Access Document Repository (OADR) (and other related technologies) to demonstrate accessibility and availability of research outputs associated with specific scientific software applications, thereby introducing the concept (and thus laying the foundation) of an Open Science research

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Collaborative learning and coordination across agency boundaries to tackle wicked problems

Conventional approaches to government are confounded by issues that cross agency, stakeholder, jurisdictional, and geopolitical boundaries. These open-ended and highly interdependent issues are often characterized in the literature as ‘wicked problems. Typically, policies and budgets are developed to align with organizational boundaries, making it difficult to bring the appropriate talent, knowledge and assets into an interagency approach to tackle the interdependencies of whatever wicked problem is at hand. Many governments have recognized the need for interagency coordination in the face of highly complex problems; and in response, there has been advocacy for improved approaches to increase collaboration and synchronized interagency working. However, without appreciating that the perspectives and values of the various government agencies and other stakeholders can vary widely, and often can be in conflict, interagency endeavors often start out to solve very different perceived problems. Furthermore, interagency constructs are frequently organized through periodic meetings and loose agreements. They do not develop concrete strategic and operational plans for how an integrated approach will be organized and implemented. The research described in this thesis was conducted to develop and evaluate a Systemic Intervention (boundary-exploring and multi-method) approach to designing interagency responses to wicked problems. This multi-method approach attempts to address many of the challenges to interagency design found in the literature. The Systemic Intervention approach was trialled on the wicked problem of international organized drug trafficking and its interface with local gangs in Chicago, USA. This wicked problem illustrates extreme complexity and the need for a cross-cutting design that cut across agencies, jurisdictions, and geographical boundaries. The research was conducted in two phases: (1) the creation of a common understanding of a wicked problem among multiple agencies using Boundary Critique and a new participatory Problem Structuring Method (PSM) called ‘Systemic Perspective Mapping’; and (2) the design of an interagency meta-organization using the Viable System Model (VSM), introduced to participants through a novel board game layout, so drug crime could be addressed at multiple scales. The research findings indicate that the combined use of Boundary Critique and Systemic Perspective Mapping was able to generate enough of a common understanding to provide a foundation for the design of an interagency organization. Also, the VSM Board Game effectively enabled multiple agency representatives to intimately interact with their representation of the V wicked problem and with each other in order to clearly delineate new agency responsibilities, communication mechanisms and channels, adaptive operations management, and an anticipatory function – all tailored to address the wicked problem they had structured as a group. The methodological approach developed in this research shows significant promise for transfer and adaptation to help tackle the design of interagency organizations for other wicked problems