Distributed simulation of large-scale and detailed models

D’Angelo, Gabriele; Bracuto, Michele

doi:10.1504/ijspm.2009.028625

Cited by 24 publications

(28 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It offers the possibility to implement both sequential and parallel/distributed simulations, using the ARTÌS simulation middleware [2] and GAIA adaptive framework [7,15]. Specifically for this simulation study, some new features have been developed and integrated in the tool, to efficiently analyze the simulation results and compute the metrics of interest.…”

Section: Pascasmentioning

confidence: 99%

Multiplayer Online Games over scale-free networks: a viable solution?

Ferretti

D’Angelo

2012

Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques

View full text Add to dashboard Cite

In this paper we discuss the viability of deploying Multiplayer Online Games (MOGs) over scale-free networks. We employ a general peer-to-peer overlay network; nodes have a number of neighbors which follows a power law distribution, p k ∼ k −α , the usual degree distribution that characterizes scale-free nets. Game events generated by nodes during the game evolution are disseminated through the network, based on some (push) gossip protocols run over the created overlay. We experiment with different gossip protocols. Results demonstrate that the employed gossip protocol may greatly influence the ability of disseminating the game data through the scale-free network. In particular, when gossip is performed using a small dissemination probability, a nonnegligible percentage of the network is not able to receive the message. This implies that not all players might be able to perceive the game event. Hence, parameters of gossip protocols must be properly tuned to guarantee a full network coverage. Concurrently, it is shown that, due to their low diameter, the use of scale-free networks allows to disseminate game events in very few steps. This could ensure a high level of responsiveness on the dissemination of game events, which is the main objective to pursue when dealing with MOGs.

show abstract

Section: Pascasmentioning

confidence: 99%

Multiplayer Online Games over scale-free networks: a viable solution?

Ferretti

D’Angelo

2012

Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques

View full text Add to dashboard Cite

show abstract

“…For example, in [34] is proposed a dynamic partitioning algorithm for optimistic distributed simulation. In our vision, the reduction of the communication cost and the aspects of load-balancing in the parallel or distributed architecture can be seen as different aspects of the same problem and therefore a joint approach is necessary [19].…”

Section: Generic Adaptive Interaction Architecture (Gaia)mentioning

confidence: 99%

“…Following the GAIA paradigm, each entity in the simulation can be migrated within the execution architecture, with the aim to cluster (migrate) the highly interacting entities in the same execution units, and therefore reduce the communication overhead (see Section 3.2.1). This version of GAIA is a very simplified form of the mechanism presented in [19]. In brief, the proposed mechanism continuously audits the communication pattern of each simulated entity, and determines if a better allocation is possible.…”

Section: Generic Adaptive Interaction Architecture (Gaia)mentioning

confidence: 99%

“…It is obvious that an unconstrained clustering mechanism would cluster all the SMEs in the same execution unit, that is the worst case for load balancing. Complex load-balancing schemes have been proposed and implemented in GAIA [19], while in the actual version of PaScaS a quite simple mechanism, that maintains a constant number of SMEs in each execution unit [5], has been used with the aim to reduce at the bare minimum the overhead introduced by the mechanism.…”

Section: The Clustering Mechanismmentioning

confidence: 99%

“…In these terms, for the reasons described in the first part of this paper, the scale free networks represent a new challenging environment to verify its applicability. As only an overall description of the mechanism has been given, due to space limitations, more details can be found in [8,5,6] and [19].…”

Section: The Clustering Mechanismmentioning

confidence: 99%

See 2 more Smart Citations

Simulation of scale-free networks

D’Angelo

Ferretti

2009

Proceedings of the Second International ICST Conference on Simulation Tools and Techniques

View full text Add to dashboard Cite

In this paper, we present a new simulation tool for scale-free networks composed of a high number of nodes. The tool, based on discrete-event simulation, enables the definition of scale-free networks composed of heterogeneous nodes and complex application-level protocols. To satisfy the performance and scalability requirements, the simulator supports both sequential (i.e. monolithic) and parallel/distributed (i.e. PADS) approaches. Furthermore, appropriate mechanisms for the communication overhead-reduction are implemented. To demonstrate the efficiency of the tool, we experiment with gossip protocols on top of scale-free networks generated by our simulator. Results of the simulations demonstrate the feasibility of our approach. The proposed tool is able to generate and manage large scale-free networks composed of thousands of nodes interacting following real-world dissemination protocols.

show abstract

Distributed re‐arrangement scheme for balancing computational load and minimizing communication delays in HLA‐based simulations

Grande

Boukerche

Ramadan

2011

Concurrency and Computation

View full text Add to dashboard Cite

SUMMARYBecause of the availability of shared resources, substantial efforts have been applied to the development of large‐scale distributed simulations, and performance has become an essential aspect that can be impaired by heterogeneity and availability of resources, dynamic, unpredictable load imbalances, and communication delays. In order to manage and keep such distributed simulations consistent, the high level architecture (HLA) standard has been designed; however, it does not provide any solution that directly solves simulation performance issues. Many balancing approaches have been proposed in order to offer a suboptimal balancing solution, but they are limited to certain simulation aspects, are specific to determined applications, or are unaware of the HLA‐based simulation characteristics. In light of considering both computational and communication aspects for HLA‐based simulations, a centralized hierarchical balancing scheme was proposed. This scheme presents several drawbacks that make it susceptible to bottlenecks, overheads, global synchronization, and single point of failure. Therefore, a scheme based on a distributed algorithm to re‐arrange the computational and communication load is proposed. Experiments have been performed to evaluate the effectiveness of the distributed scheme when compared with the scheme based on a centralized redistribution algorithm. The results showed that the distributed balancing technique could provide similar performance gain or even improve it for some specific cases. Copyright © 2011 John Wiley & Sons, Ltd.

show abstract

Distributed simulation of large-scale and detailed models

Cited by 24 publications

References 33 publications

Multiplayer Online Games over scale-free networks: a viable solution?

Multiplayer Online Games over scale-free networks: a viable solution?

Simulation of scale-free networks

Distributed re‐arrangement scheme for balancing computational load and minimizing communication delays in HLA‐based simulations

Contact Info

Product

Resources

About