Leveraging simulation practice in industry through use of desktop grid middleware

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources)

Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

A Simulation-Based Layered Framework Framework for the Development of Collaborative Autonomous Systems

The purpose of this thesis is to introduce a simulation-based software framework that facilitates the development of collaborative autonomous systems. Significant commonalities exist in the design approaches of both collaborative and autonomous systems, mirroring the sense, plan, act paradigm, and mostly adopting layered architectures. Unfortunately, the development of such systems is intricate and requires low-level interfacing which significantly detracts from development time. Frameworks for the development of collaborative and autonomous systems have been developed but are not flexible and center on narrow ranges of applications and platforms. The proposed framework utilizes an expandable layered structure that allows developers to define a layered structure and perform isolated development on different layers. The framework provides communication capabilities and allows message definition in order to define collaborative behavior across various applications. The framework is designed to be compatible with many robotic platforms and utilizes the concept of robotic middleware in order to interface with robots; attaching the framework on different platforms only requires changing the middleware. An example Fire Brigade application that was developed in the framework is presented; highlighting the design process and utilization of framework related features. The application is simulation-based, relying on kinematic models to simulate physical actions and a virtual environment to provide access to sensor data. While the results demonstrated interesting collaborative behavior, the ease of implementation and capacity to experiment by swapping layers is particularly noteworthy. The framework retains the advantages of layered architectures and provides greater flexibility, shielding developers from intricacies and providing enough tools to make collaboration easy to perform

Closing the loop between neural network simulators and the OpenAI Gym

Since the enormous breakthroughs in machine learning over the last decade, functional neural network models are of growing interest for many researchers in the field of computational neuroscience. One major branch of research is concerned with biologically plausible implementations of reinforcement learning, with a variety of different models developed over the recent years. However, most studies in this area are conducted with custom simulation scripts and manually implemented tasks. This makes it hard for other researchers to reproduce and build upon previous work and nearly impossible to compare the performance of different learning architectures. In this work, we present a novel approach to solve this problem, connecting benchmark tools from the field of machine learning and state-of-the-art neural network simulators from computational neuroscience. This toolchain enables researchers in both fields to make use of well-tested high-performance simulation software supporting biologically plausible neuron, synapse and network models and allows them to evaluate and compare their approach on the basis of standardized environments of varying complexity. We demonstrate the functionality of the toolchain by implementing a neuronal actor-critic architecture for reinforcement learning in the NEST simulator and successfully training it on two different environments from the OpenAI Gym

Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning

A framework for analyzing and testing cyber-physical interactions for smart grid applications

The reliable performance of the smart grid is a function of the configuration and cyber- physical nature of its constituting sub-systems. Therefore, the ability to capture the interactions between its cyber and physical domains is necessary to understand the effect that each one has on the other. As such, the work in this paper presents a co-simulation platform that formalizes the understanding of cyber information flow and the dynamic behavior of physical systems, and captures the interactions between them in smart grid applications. Power system simulation software packages, embedded microcontrollers, and a real communication infrastructure are combined together to provide a cohesive smart grid cyber-physical platform. A data-centric communication scheme, with automatic network discovery, was selected to provide an interoperability layer between multi-vendor devices and software packages, and to bridge different protocols. The effectiveness of the proposed framework was verified in three case studies: (1) hierarchical control of electric vehicles charging in microgrids, (2) International Electrotechnical Committee (IEC) 61850 protocol emulation for protection of active distribution networks, and (3) resiliency enhancement against fake data injection attacks. The results showed that the cosimulation platform provided a high-fidelity design, analysis, and testing environment for cyber information flow and their effect on the physical operation of the smart grid, as they were experimentally verified, down to the packet, over a real communication network

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications