The Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed dynamic computing environments for end-users. In this paper, we study the Cloud computing paradigm from various aspects, such as definitions, distinct features, and enabling technologies. This paper brings an introductional review on the Cloud computing and provides the state-of-the-art of Cloud computing technologies.

/pdf/cloud-computing-a-perspective-study-gfravmqzzn.pdf

Cloud Computing: a Perspective Study

Agent Based Modelling and Simulation tools: A review of the state-of-art software

Industrial Agents explains how multi-agent systems improve collaborative networks to offer dynamic service changes, customization, improved quality and reliability, and flexible infrastructure. Learn how these platforms can offer distributed intelligent management and control functions with communication, cooperation and synchronization capabilities, and also provide for the behavior specifications of the smart components of the system. The book offers not only an introduction to industrial agents, but also clarifies and positions the vision, on-going efforts, example applications, assessment and roadmap applicable to multiple industries. This edited work is guided and co-authored by leaders of the IEEE Technical Committee on Industrial Agents who represent both academic and industry perspectives and share the latest research along with their hands-on experiences prototyping and deploying industrial agents in industrial scenarios.Learn how new scientific approaches and technologies aggregate resources such next generation intelligent systems, manual workplaces and information and material flow systemGain insight from experts presenting the latest academic and industry research on multi-agent systemsExplore multiple case studies and example applications showing industrial agents in a variety of scenariosUnderstand implementations across the enterprise, from low-level control systems to autonomous and collaborative management units

Industrial Agents: Emerging Applications of Software Agents in Industry

This chapter provides a review and examples of approaches to model scaling when constructing large agent-based models. A comparison is made between an aggregate ‘super-individual’ approach, as run on a single processor machine, and two different approaches to parallelisation of agent models run on multi-core hardware. Super-individuals provide a straightforward solution without much alteration of the model formulation and result in large improvements in model efficiency (speed and memory use). However, there are significant challenges to using a super-individual approach when relating super-individuals to individuals in time and space. Parallel computing approaches accept the requirement for large amounts of memory or CPU and attempt to solve the problem by distributing the calculation over many computational units. This requires some modification of the model software and algorithms to distribute the model components across multiple computational cores. This can be achieved in a number of different ways, two of which we illustrate further for the case of spatial models, an ‘agent-parallel’ and an ‘environment-parallel’ approach. However, the success of such approaches may also be affected by the complexity of the model (such as multiple agent types and agent interactions), as we illustrate by adding a predator to our example simulation. Between these two parallelisation approaches to the case study, the environment-parallel version of the model, written in C++ instead of Java, proved more efficient and successful at handling parallel processing of complex agent interactions. In conclusion, we use our experiences of creating large agent-based simulations to provide some general guidelines for best practice in agent-based model scaling.

Large Scale Agent-Based Modelling: A Review and Guidelines for Model Scaling

Enhancing a model-based engineering approach for distributed manufacturing automation systems with characteristics and design patterns

Large, experimental multi-agent system (MAS) simulations are highly demanding tasks, both computationally and developmentally. Agent toolkits provide reliable templates for the design of even the largest MAS simulations, without offering a solution to computational limitations. Conversely, distributed simulation architectures offer performance benefits, but the introduction of parallel logic can complicate the design process significantly. The motivations of distribution are not limited to this question of processing power. True interoperation of sequential agent-simulation platforms would allow agents designed using different toolkits to transparently interact in common abstract domains. This paper discusses the design and implementation of a system capable of harnessing the computational power of a distributed simulation infrastructure with the design efficiency of an agent toolkit. The system permits integration, through a higher-level architecture (HLA) federation, of multiple instances of the Java-based lightweight agent-simulation toolkit RePast. This paper defines abstractly the engineering process necessary in creating such middleware, and reports on the experience in the specific case of the RePast toolkit. The paper also presents performance results that illustrate that significant speedup can be achieved through the integration of RePast with HLA. Copyright © 2008 John Wiley & Sons, Ltd.

/pdf/distributing-repast-agent-based-simulations-with-hla-39mqk03osk.pdf

Distributing RePast agent-based simulations with HLA

The developmentof many complex siumulation applications requires collaborrrtive effortfrom reseachers with different domain knowledge and expertise,possibly at different locations. These simulations systems often require huge computing resources and data set, which may be geo- graphically distribyted, In ordr to support collaborative model development and to cater for the increasing comoplex- ity of such systems, it is necessary to harness distributed resources over the Internet. the emergence of Grid tech nologies provide exciting new opportunities for large scale distributed simulation, enabling collaboration and the use of distributed computing resources, while also facilitating access to geographically distributed data sets. This paper discusses the research challenges that must be addresses before these opportunities can be exploited and presents HLA-GRID_REPast, a system for executing large scale distributed simulations of agent based systems over the Grid.

/pdf/large-scale-distributed-simulation-on-the-grid-4pql3lt8w9.pdf

Large Scale Distributed Simulation on the Grid

The efficient simulation of multi-agent systems presents particular challenges which are not addressed by current parallel discrete event simulation (PDES) models and techniques. While the modelling and simulation of agents, at least at a coarse grain, is relatively straightforward, it is harder to apply PDES approaches to the simulation of the agents' environment. In conventional PDES approaches a system is modelled as a set of logical processes (LPs). Each LP maintains its own portion of the state of the simulation and interacts with a small number of other LPs. The interaction between the LPs is assumed to be known in advance and does not change during the simulation. In contrast, the environment of a MAS is read and updated by agent and environment LPs in ways which depend on the evolution of the simulation. As a result, MAS simulations typically have a large shared state which is not associated with any particular agent or environment LP. In [1] we proposed a new approach to the distributed simulation of MAS in which the shared state is maintained by a tree of additional logical processes called Communication Logical Processes (CLP). In this paper we refine this model by giving precise definitions of a set of operations which allow agent and environment LPs to interact with the shared state and briefly outline how these operations could be implemented by a CLP.

Distributed simulation of MAS

Decentralised, event-driven distributed simulation is particularly suitable for modelling systems with inherent asynchronous parallelism, such as agent-based systems. However the efficient simulation of multi-agent systems presents particular challenges which are not addressed by standard parallel discrete event simulation (PDES) models and techniques. PDES approaches based on the logical process paradigm assume a fixed decomposition into processes, each of which maintains its own portion of the state of the simulation. The interaction between the processes is fixed in advance and does not change during the simulation. In contrast, simulations of MAS typically have a large shared state, the agents' environment, which is only loosely associated with any particular process. In this paper, we present a model of the shared state of a distributed MAS simulation of situated agents. We consider the problems of efficient sensing, parallel actions and action conflicts, and present preliminary work on an approach to the simulation of the environment which addresses these issues.

/pdf/modelling-environments-for-distributed-simulation-387h7geoq0.pdf

Modelling environments for distributed simulation

The efficient simulation of multi-agent systems presents particular challenges which are not addressed by current parallel discrete event simulation (PDES) models and techniques. While the modelling and simulation of agents, at least at a coarse grain, is relatively straightforward, it is harder to apply PDES approaches to the simulation of the agents’ environment. In conventional PDES approaches a system is modelled as a set of logical processes (LPs). Each LP maintains its own portion of the state of the simulation and interacts with a small number of other LPs. The interaction between the LPs is assumed to be known in advance and does not change during the simulation. In contrast, the environment of a MAS is read and updated by agent and environment LPs in ways which depend on the evolution of the simulation. As a result, MAS simulations typically have a large shared state which is not associated with any particular agent or environment LP. In [1] we proposed a new approach to the distributed simulation of MAS in which the shared state is maintained by a tree of additional logical processes called Communication Logical Processes (CLP). In this paper we refine this model by giving precise definitions of a set of operations which allow agent and environment LPs to interact with the shared state and briefly outline how these operations could be implemented by a CLP.

/pdf/distributed-simulation-of-mas-5czlyzm3oh.pdf

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Distributing RePast agent-based simulations with HLA

Large Scale Distributed Simulation on the Grid

Distributed simulation of MAS

Modelling environments for distributed simulation

Distributed simulation of MAS