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)

Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry

Investigating grid computing technologies for use with commercial simulation packages

As simulation experimentation in industry become more computationally demanding, grid computing can be seen as a promising technology that has the potential to bind together the computational resources needed to quickly execute such simulations. To investigate how this might be possible, this paper reviews the grid technologies that can be used together with commercial-off-the-shelf simulation packages (CSPs) used in industry. The paper identifies two specific forms of grid computing (Public Resource Computing and Enterprise-wide Desktop Grid Computing) and the middleware associated with them (BOINC and Condor) as being suitable for grid-enabling existing CSPs. It further proposes three different CSP-grid integration approaches and identifies one of them to be the most appropriate. It is hoped that this research will encourage simulation practitioners to consider grid computing as a technologically viable means of executing CSP-based experiments faster

Middleware for Wireless Sensor Networks: An Outlook

In modern distributed computing, applications are rarely built directly atop operating system facilities, e.g., sockets. Higher-level middleware abstractions and systems are often employed to simplify the programmer’s chore or to achieve interoperability. In contrast, real-world wireless sensor network (WSN) applications are almost always developed by relying directly on the operating system. Why is this the case? Does it make sense to include a middleware layer in the design of WSNs? And, if so, is it the same kind of software system as in traditional distributed computing? What are the fundamental concepts, reasonable assumptions, and key criteria guiding its design? What are the main open research challenges, and the potential pitfalls? Most importantly, is it worth pursuing research in this field? This paper provides a (biased) answer to these and other research questions, preceded by a brief account on the state of the art in the field

Middleware for managing a large, heterogeneous programmable network

The links between BTexact Technologies and the Department of Computing Science at University College London are becomingincreasingly beneficial for the development of the middleware area for the management of programmable networks. This paperdescribes the work that has been done to date, and outlines the plans for future research

Requirements of a middleware for managing a large, heterogeneous programmable network

Programmable networking is an increasingly popular area of research in both industry and academia. Although most programmable network research projects seem to focus on the router architecture rather than on issues relating to the management of programmable networks, there are numerous research groups that have incorporated management middleware into the programmable network router software. However, none seem to be concerned with the effective management of a large heterogeneous programmable network. The requirements of such a middleware are outlined in this paper. There are a number of fundamental middleware principles that are addressed in this paper; these include management paradigms, configuration delivery, scalability and transactions. Security, fault tolerance and usability are also examined—although these are not essential parts of the middleware, they must be addressed if the programmable network management middleware is to be accepted by industry and adopted by other research projects

Investigating IoT Middleware Platforms for Smart Application Development

With the growing number of Internet of Things (IoT) devices, the data generated through these devices is also increasing. By 2030, it is been predicted that the number of IoT devices will exceed the number of human beings on earth. This gives rise to the requirement of middleware platform that can manage IoT devices, intelligently store and process gigantic data generated for building smart applications such as Smart Cities, Smart Healthcare, Smart Industry, and others. At present, market is overwhelming with the number of IoT middleware platforms with specific features. This raises one of the most serious and least discussed challenge for application developer to choose suitable platform for their application development. Across the literature, very little attempt is done in classifying or comparing IoT middleware platforms for the applications. This paper categorizes IoT platforms into four categories namely-publicly traded, open source, developer friendly and end-to-end connectivity. Some of the popular middleware platforms in each category are investigated based on general IoT architecture. Comparison of IoT middleware platforms in each category, based on basic, sensing, communication and application development features is presented. This study can be useful for IoT application developers to select the most appropriate platform according to their application requirement

A Conceptual Architecture for a Quantum-HPC Middleware

Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved from monolithic systems towards modular architectures comprising multiple quantum processing units (QPUs) coupled to classical computing nodes (HPC). With the increasing scale, middleware systems that facilitate the efficient coupling of quantum-classical computing are becoming critical. Through an in-depth analysis of quantum applications, integration patterns and systems, we identified a gap in understanding Quantum-HPC middleware systems. We present a conceptual middleware to facilitate reasoning about quantum-classical integration and serve as the basis for a future middleware system. An essential contribution of this paper lies in leveraging well-established high-performance computing abstractions for managing workloads, tasks, and resources to integrate quantum computing into HPC systems seamlessly.Comment: 12 pages, 3 figure