Synchronised Range Queries in Distributed Simulations of Multi-agent Systems

Suryanarayanan, Vinoth; Craenen, Bart; Theodoropoulos, Georgios

doi:10.1109/ds-rt.2010.18

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The tuple‐space is partitioned by following a hierarchical schema based on the spheres of influence so as to avoid bottlenecks in managing shared data. The same approach is exploited in where range query operations are allowed on shared data.…”

Section: Related Workmentioning

confidence: 99%

Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

Cicirelli

Giordano

Nigro

2014

Concurrency and Computation

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SummaryMulti‐agent systems have been proven very effective for the modelling and simulation (M&S) of complex systems like those related to biology, engineering, social sciences and so forth. The intrinsic spatial character of many such systems leads to the definition of a situated agent. A situated agent owns spatial coordinates and acts and interacts with its peers in a hosting territory. In the context of parallel/distributed simulation of situated agent models, the territory represents a huge shared variable that requires careful handling. Frequent access by agents to territory information easily becomes a bottleneck degrading system performance and scalability. This paper proposes an original approach to modelling and distributed simulation of large‐scale situated multi‐agent systems. Time management is exploited for resolving conflicts and achieving data consistency while accessing the environment. The approach allows a simplification of the M&S tasks by making the modeller unaware of distribution concerns while ensuring the achievement of good scalability and performance during the distributed simulation. Practical aspects of the approach are demonstrated through some modelling examples based on Tileworld. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

Cicirelli

Giordano

Nigro

2014

Concurrency and Computation

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“…Many of the interest management approaches surveyed in this article can and have been applied in both DVEs and distributed simulations. Interest management approaches specifically targetting the latter have also been developed (e.g., Suryanarayanan et al [2010]), but these are considered outside the scope of this survey.…”

Section: Introductionmentioning

confidence: 99%

Interest management for distributed virtual environments

Liu

Theodoropoulos

2014

ACM Comput. Surv.

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The past two decades have witnessed an explosion in the deployment of large-scale distributed simulations and distributed virtual environments in different domains, including military and academic simulation systems, social media, and commercial applications such as massively multiplayer online games. As these systems become larger, more data intensive, and more latency sensitive, the optimisation of the flow of data, a paradigm referred to as interest management, has become increasingly critical to address the scalability requirements and enable their successful deployment. Numerous interest management schemes have been proposed for different application scenarios. This article provides a comprehensive survey of the state of the art in the design of interest management algorithms and systems. The scope of the survey includes current and historical projects providing a taxonomy of the existing schemes and summarising their key features. Identifying the primary requirements of interest management, the article discusses the trade-offs involved in the design of existing approaches.

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“…The aim of the strategy is to reduce the service latency (query response time) and execution time of the PDES-MAS system. The design and experimental results presented in this chapter are reported in [137].…”

Section: Chapter 5 Range Queries In the Presence Of State Migrationmentioning

confidence: 99%

“…1 contents and definitions in this section are taken from [137] 11 range query(min val, max val) = {v ∈ V read(v)(if min val ≤ read(v) ≤ max val)}.…”

Section: Query Mechanismsmentioning

confidence: 99%

Synchronised Range Queries

Suryanarayanan

Minson

Theodoropoulus

2009

2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications

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Computer simulations have been used more than ever before to embark on developing and understanding complex systems such as Multi-Agent Systems (MAS). MAS are complex, non-deterministic, data-centric behaviour and nature. Simulations play a key role for the designer of an agent based system to experiment and study the impact of different architectures, environment and agent behaviour. As MAS are increasingly used to solve larger and more complex problems, scalability becomes an important issue for the successful deployment. An emerging viable solution is adopting distributed simulation techniques in executing MAS models in parallel. One approach is to distribute the shared state (or environment) of a simulation model across available computing resources. Based on this approach, PDES-MAS (Parallel and Discrete Event Simulations for Multi-Agent System) framework is designed to execute large scale models such as MAS. It adopts PDES techniques to distribute and run parallel simulation of Multi-Agent Systems (MAS). Several challenges arise on executing MAS models on a distributed environment, of which one issue that requires focus is data access. Accessing data efficiently in a latency-sensitive and large scale network overlay is a vital requirement for the scalability of the system. Following PDES paradigm, it is also very important that events (accessing shared state) in a parallel and discrete event simulation system are processed in a non decreasing (logical) time stamp order. So, this thesis presents a notion of logical time synchronised range queries to access data and in particular within the PDES-MAS framework. To localise data access, this thesis also provides mechanisms to distribute shared state in an adaptive manner such that the distribution reflects access patterns of simulating nodes. The algorithms are evaluated within the implementation of PDES-MAS framework using various agent based simulation traces. ACKNOWLEDGEMENTS

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Synchronised Range Queries in Distributed Simulations of Multi-agent Systems

Cited by 9 publications

References 23 publications

Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

Interest management for distributed virtual environments

Synchronised Range Queries

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