An analytical model for hybrid checkpointing in time warp distributed simulation

1998 ◽  
Vol 9 (10) ◽  
pp. 947-951 ◽  
Author(s):  
H.M. Soliman ◽  
A.S. Elmaghraby
Keyword(s):  
Analytical Model ◽  
Distributed Simulation ◽  
Time Warp

LOAD BALANCING FOR PARALLEL PCS NETWORK SIMULATIONS

2000 ◽  
Vol 01 (03) ◽  
pp. 173-193 ◽  
Author(s):  
AZZEDINE BOUKERCHE
Keyword(s):  
Load Balancing ◽  
Distributed Simulation ◽  
Simulation Experiments ◽  
Time Warp ◽  
Conservative Scheme ◽  
Simulation Techniques ◽  
Parallel And Distributed Simulation ◽  
Pcs Networks ◽  
Network Simulations ◽  
Stage 1

Parallel and distributed simulation techniques have been investigated in a number of studies to decrease the execution times of PCS network simulations. In this paper, we consider distributed simulation of PCS models using a two-state PCS simulation testbed which makes use of a conservative scheme at Stage 1, and of Time Warp at Stage 2, and focus upon the load balancing issue. We investigate and study several load balancing schemes for TDMA systems. Extensive simulation experiments were conducted on a cluster of workstations using a real suburban area serviced by an FCA-based PCS networks. Our results indicate clearly that careful load balancing scheme is important in the success of the PCS simulation model.

scholarly journals Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 467
Author(s):  
Edwin Cortes ◽  
Luis Rabelo ◽  
Alfonso T. Sarmiento ◽  
Edgar Gutierrez
Keyword(s):  
Discrete Event Simulation ◽  
Research Study ◽  
Distributed Simulation ◽  
Discrete Event ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Cognitive Approach ◽  
Time Warp ◽  
Event Simulation ◽  
Synchronization Scheme

In this research study, we investigate the ability of deep learning neural networks to provide a mapping between features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to optimize speedup performance. We use deep belief networks (DBNs). DBNs, which due to their multiple layers with feature detectors at the lower layers and a supervised scheme at the higher layers, can provide nonlinear mappings. The mapping mechanism works by considering simulation constructs, hardware, and software intricacies such as simulation objects, concurrency, iterations, routines, and messaging rates with a particular importance level based on a cognitive approach. The result of the mapping is a synchronization scheme such as breathing time buckets, breathing time warp, and time warp to optimize speedup. The simulation-optimization technique outlined in this research study is unique. This new methodology could be realized within the current parallel and distributed simulation modeling systems to enhance performance.

ANALYTICAL MODEL OF SUPEREXCHANGE

1988 ◽  
Vol 49 (C8) ◽  
pp. C8-911-C8-912
Author(s):  
Yu. V. Rakitin ◽  
V. T. Kalinnikov
Keyword(s):  
Analytical Model
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