SANDS: Specialized Active Networking for Distributed Simulation

Zabele, S.; Dorsch, M.; Ge, Zihui; Ji, Ping; Keaton, M.; Kurose, Jim; Shapiro, J.K.; Towsley, Don

doi:10.1109/dance.2002.1003507

Cited by 19 publications

(15 citation statements)

References 12 publications

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“…On the other hand, Active interest solution not only transmits user messages, but also computes and processes the packages. Zabele [4] [5] provides an active interest solution in which the router joins the VR actively and filters the messages cascading. This specific solution eliminates the drawback of the multicast technology and thus could be used in Inter-WAN Virtual environments.…”

Section: Related Workmentioning

confidence: 99%

Research of inter-domain data filtering method for distributed interactive simulation

Chen

Tan

2010

2010 International Conference on Audio, Language and Image Processing

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The distributed interactive simulation of multidomain is an important research area of the distributed virtual reality. However, frequent interaction, great quantity of messages and the discrepant need of each individual simulation domain resulted in numerous redundant messages. Filtering out the redundant data and relieving the transfer loads become a pressing necessity. We analysis the principle of data filtering of HLA standard and introduce a data filtering algorithm based on publish/subscribe. Utilized the data locality feature of distributed interaction simulation, this algorithm can be used in multi-domain either. The experiment shows that the method we introduced provides reliable data filtering performance with incurred incremental costs on purchasing information endurable by our system. In addition, the data filtering matching efficiency is well.

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Section: Related Workmentioning

confidence: 99%

Research of inter-domain data filtering method for distributed interactive simulation

Chen

Tan

2010

2010 International Conference on Audio, Language and Image Processing

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“…The few that have been implemented focus on a total solution to a problem such as video processing [11], distributed interest filtering [15], and network management [10]. Each of these executes as a complete AA.…”

Section: Related Workmentioning

confidence: 99%

Designing Service-Specific Execution Environments

et al. 2002

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Abstract. Past work on the active network architectural framework has focused on the NodeOS and Execution Environment (EE), which offer rather standard programming environments. As a result, Active Applications (AAs) must build the desired service from the ground up. Ideally, active applications could be built on higher-level active services that could themselves be "programmed". In this paper, we examine the issues involved in the design and implementation of higherlevel active network services. We describe the issues that arise when using AAs to implement these services and then present our experiences implementing one such service, namely concast, as an AA running on the ASP EE. The paper concludes with performance numbers obtained from an example audio-merge application that shows the viability of using AAs as specialized execution environments.

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“…¥ Number of hops. In certain signaling protocols such as RSVP and AFSP [18], a signaling sender must install state at multiple nodes between itself and the ultimate signaling destination. As the number of hops increases, the fraction of time that all nodes are in an inconsistent state will also likely increase.…”

Section: Hard-state (Hs) Approachmentioning

confidence: 99%

A comparison of hard-state and soft-state signaling protocols

Kurose

et al. 2003

Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications

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One of the key infrastructure components in all telecommunication networks, ranging from the telephone network, to VC-oriented data networks, to the Internet, is its signaling system. Two broad approaches towards signaling can be identified: so-called hard-state and soft-state approaches. Despite the fundamental importance of signaling, our understanding of these approaches -their pros and cons and the circumstances in which they might best be employed -is mostly anecdotal (and occasionally religious). In this paper, we compare and contrast a variety of signaling approaches ranging from a "pure" soft state, to soft-state approaches augmented with explicit state removal and/or reliable signaling, to a "pure" hard state approach. We develop an analytic model that allows us to quantify state inconsistency in single-and multiple-hop signaling scenarios, and the "cost" (both in terms of signaling overhead, and application-specific costs resulting from state inconsistency) associated with a given signaling approach and its parameters (e.g., state refresh and removal timers). Among the class of soft-state approaches, we find that a soft-state approach coupled with explicit removal substantially improves the degree of state consistency while introducing little additional signaling message overhead. The addition of reliable explicit setup/update/removal allows the soft-state approach to achieve comparable (and sometimes better) consistency than that of the hard-state approach.

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SANDS: Specialized Active Networking for Distributed Simulation

Cited by 19 publications

References 12 publications

Research of inter-domain data filtering method for distributed interactive simulation

Research of inter-domain data filtering method for distributed interactive simulation

Designing Service-Specific Execution Environments

A comparison of hard-state and soft-state signaling protocols

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