Grid-based large-scale Web3D collaborative virtual environment.
ABSTRACT This paper presents a grid-based large-scale web3D collaborative virtual environment that has the capability of scaling across multiple geographically dispersed resources. The architecture consists of distributed mobile agents working cooperatively in supporting and managing the web3D collaborative virtual environments. The mobile agents' tasks include managing persistency and consistency of the virtual worlds, maintaining reliability and efficiency of user interactions, ensuring security and integrity of data and systems. The mobile agents are autonomous and have the ability of migrating among hosts to maximize resource utilizations. Grid technologies allow the mobile agents to execute and communicate securely in multiple administrative domains. Grid-based scheduling components and polices are integrated to provide intelligent resource optimizations. Furthermore, a better load-balancing can be achieved by utilizing additional or more accurate information like data-user proximity and hosts' workload. The result will be a more scalable and robust architecture for supporting large-scale web3D collaborative virtual environment.
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ABSTRACT: The Web3D technologies make it possible to create Collaborative Virtual Environment (CVE) in the popular Internet, thus promoting the popularity of CVE scientific applications to broader areas and making it easily accessible to more online users. However, the scalability of a Large-scale CVE (LCVE) is still limited by the constraints in processing power and network speed of each participating host. In this paper, we propose an Intelligent Mobile Agent Framework for LCVE in heterogeneous internet environment. In our approach, the system functions are decomposed into independent and fine grained tasks. The tasks are modeled as intelligent mobile agents which are not bound to any fixed nodes as the traditional CVE architectures do. As the intelligent mobile agents can autonomously migrate or clone at any suitable participating host dynamically at run-time, the system workloads can be distributed more pervasively to avoid potential bottleneck. This improves the scalability of LCVE. Our experiments results have demonstrated the scalability of our proposed approach.IJVR. 01/2008; 7:63-72.
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ABSTRACT: Over the last years, a high demand for scenario-specific visualizations of 3D urban models has evolved. At the same time, established service specifications do not yet provide the means to define 3D map products and to deliver them in suitable formats, since they are focused on traditional 2D map products. In this paper, we present an approach for the definition of a 3D urban model view service. This approach consists of a three-step process, in which original geodata is integrated, filtered and then transformed into various scene graph formats such as X3D. We were able to maintain a high degree of compatibility with existing services and specifications such as Styled Layer Descriptors and the Web Map Service interface. The paper concludes with the experiences gathered from implementing and using this approach and provides an outlook as to how the lessons learned can be used in application and standardization.Fraunhofer IGD. 01/2009;
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ABSTRACT: Enterprise distributed real-time and embedded (DRE) publish/subscribe (pub/sub) systems manage resources and data that are vital to users. Cloud com- puting—where computing resources are provisioned elastically and leased as a service—is an increasingly popular deployment paradigm. Enterprise DRE pub/- sub systems can leverage cloud computing provisioning services to execute needed functionality when on-site computing resources are not available. Although cloud computing provides flexible on-demand computing and networking resources, enterprise DRE pub/sub systems often cannot accurately characterize their be- havior a priori for the variety of resource configurations cloud computing sup- plies (e.g., CPU and network bandwidth), which makes it hard for DRE systems to leverage conventional cloud computing platforms. This paper provides two contributions to the study of how autonomic configu- ration of DRE pub/sub middleware can provision and use on-demand cloud re- sources effectively. We first describe how supervised machine learning can con- figure DRE pub/sub middleware services and transport protocols autonomically to support end-to-end quality-of-service (QoS) requirements based on cloud com- puting resources. We then present results that empirically validate how comput- ing and networking resources affect enterprise DRE pub/sub system QoS. These results show how supervised machine learning can configure DRE pub/sub mid- dleware adaptively in < 10 μsec with bounded time complexity to support key QoS reliability and latency requirements.Middleware 2010 - ACM/IFIP/USENIX 11th International Middleware Conference, Bangalore, India, November 29 - December 3, 2010. Proceedings; 01/2010