An adaptive load management mechanism for distributed simulation of multi-agent systems

Oguara, Ton; Chen, D.; Theodoropoulos, Georgios; Logan, Brian; Lees, Michael

doi:10.1109/distra.2005.9

Cited by 25 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several approaches of achieving distribution within PDES-MAS system are reported in [79]. A competitive optimisation algorithm to SSV migration for PDES-MAS framework is reported in [105]. SSV migration is achieved with migrating SSVs closer towards the accessing agents.…”

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

confidence: 99%

“…One such adaptive load management mechanism proposed within PDES-MAS system is reported in [105], which uses access patterns of LPs to migrate a portion of shared state closer to the accessing LPs. It uses several threshold parameters to avoid thrashing the system with migration.…”

Section: Load Balancing In Pdesmentioning

confidence: 99%

“…It is not possible to determine ways to achieve dynamism (discussed in detail in chapter 5), of which this framework has a fixed tree of CLPs where shared state of the simulation is distributed among them. More comprehensive and adaptive approaches are discussed in [105]. The idea is that shared state of the simulation in the CLP tree should reflect the SoI of the ALP interacting with them.…”

Section: Distributing Shared Statementioning

confidence: 99%

See 2 more Smart Citations

Synchronised Range Queries

Suryanarayanan

Minson

Theodoropoulus

2009

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

View full text Add to dashboard Cite

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

show abstract

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

confidence: 99%

Section: Load Balancing In Pdesmentioning

confidence: 99%

Section: Distributing Shared Statementioning

confidence: 99%

See 1 more Smart Citation

Synchronised Range Queries

Suryanarayanan

Minson

Theodoropoulus

2009

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

View full text Add to dashboard Cite

show abstract

“…Size of virtual space 10,000 × 10,000 Number of agents in total 10,20,30,50,70,100,200, 300, 400, 500, 600 Speed of MovingAgent Random number, 1-100 Detection radius of StaticAgent Random number, 0-50…”

Section: Parameter Valuementioning

confidence: 99%

“…The "think" steps are not allowed to overlap because of the assumption that a typical agent only has one Central Processing Unit (CPU) and cannot think at the same time; this model can perceive the external environment while simulating the thinking of the agent. The Scalable Agents Simulation System (SASSY) [4] and Parallel Discrete Event Simulation for Multi-Agent System (PDES-MAS) [30] are the main hybrid simulation platforms using this original Traditional agent Behavior Model (original TBM). The System for Parallel Agent Discrete Event Simulator (SPADES) [31] framework improves this model by establishing a "sense-think-act" delay model (which can be shorted as delayed TBM).…”

Section: Traditional Agent Behavior Modelmentioning

confidence: 99%

A Lookahead Behavior Model for Multi-Agent Hybrid Simulation

Yang

Peng

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

Abstract:In the military field, multi-agent simulation (MAS) plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running efficiency is of great concern for researchers. Current solutions mainly use hybrid simulation to gain fewer updates and synchronizations, where some important continuous models are maintained implicitly to keep the system dynamics, and partial resynchronization (PR) is chosen as the preferable state update mechanism. However, problems, such as resynchronization interval selection and cyclic dependency, remain unsolved in PR, which easily lead to low update efficiency and infinite looping of the state update process. To address these problems, this paper proposes a lookahead behavior model (LBM) to implement a PR-based hybrid simulation. In LBM, a minimal safe time window is used to predict the interactions between implicit models, upon which the resynchronization interval can be efficiently determined. Moreover, the LBM gives an estimated state value in the lookahead process so as to break the state-dependent cycle. The simulation results show that, compared with traditional mechanisms, LBM requires fewer updates and synchronizations.

show abstract