Options
Synchronisation for Dynamic Load Balancing of Decentralised Conservative Distributed Simulation
File(s)
File | Description | Size | Format | |
---|---|---|---|---|
Bragard_2014_synchronisation.pdf | 532.07 KB |
Date Issued
21 May 2014
Date Available
05T08:48:27Z October 2015
Abstract
Synchronisation mechanisms are essential in distributed simulation. Some systems rely on central units to control the simulation but central units are known to be bottlenecks [10]. If we want to avoid using a central unit to optimise the simulation speed, we lose the capacity to act on the simulation at a global scale. Being able to act on the entire simulation is an important feature which allows to dynamically load-balance a distributed simulation. While some local partitioning algorithms exist [12], their lack of global view reduces their efficiency. Running a global partitioning algorithm without central unit requires a synchronisation of all logical processes (LPs) at the same step.We introduce in this paper two algorithms allowing to synchronise logical processes in a distributed simulation without any central unit. The first algorithm requires the knowledge of some topological properties of the network while the second algorithm works without any requirement. The algorithms are detailed and compared against each other. An evaluation shows the benefits of using a global dynamic load-balancing for distributed simulations.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Lero
Type of Material
Conference Publication
Publisher
Association for Computing Machinery
Copyright (Published Version)
2014 ACM
Language
English
Status of Item
Peer reviewed
Description
Proceedings of the 2nd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Denver, Colorado, USA, 18 - 21 May, 2014
This item is made available under a Creative Commons License
Owning collection
Scopus© citations
5
Acquisition Date
Jan 28, 2023
Jan 28, 2023
Views
1975
Last Month
22
22
Acquisition Date
Jan 28, 2023
Jan 28, 2023
Downloads
281
Last Month
82
82
Acquisition Date
Jan 28, 2023
Jan 28, 2023