A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Developing interest management techniques in distributed interactive simulation using Java

Bandwidth consumption in distributed real time simulation, or networked real time simulation, is a major problem as the number of participants and the sophistication of joint simulation exercises grow in size. The paper briefly reviews distributed real time simulation and bandwidth reduction techniques and introduces the Generic Runtime Infrastructure for Distributed Simulation (GRIDS) as a research architecture for studying such problems. GRIDS uses Java abstract classes to promote distributed services called thin agents, a novel approach to implementing distributed simulation services, such as user defined bandwidth reduction mechanisms, and to distributing the executable code across the simulation. Thin agents offer the advantages of traditional agents without the overhead imposed by mobility or continuous state, which are unnecessary in this context. We present our implementation and some predicted results from message reduction studies using thin agent

GRIDCC - Providing a real-time grid for distributed instrumentation

The GRIDCC project is extending the use of Grid computing to include access to and control of distributed instrumentation. Access to the instruments will be via an interface to a Virtual Instrument Grid Service (VIGS). VIGS is a new concept and its design and implementation, together with middleware that can provide the appropriate Quality of Service (QoS), is a key part of the GRIDCC development plan. An overall architecture for GRIDCC has been defined and some of the application areas, which include distributed power systems, remote control of an accelerator and the remote monitoring of a large particle physics experiment, are briefly discussed.E

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