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The production and demand profiles mismatch limits the network hosting capacity, which still can be enhanced by implementing a load management.
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We have implemented a load balancer for Sun/UNIX environments.
We have developed and implemented a load balancing scheme for particle-in-cell (PIC) codes which use the GCPIC algorithm on MIMD computers.
Due to the vastly different processing times of PSC operations when using different methods, we allow the master process: (a) to make a choice between sequential versus parallel job processing and (b) implement a load balancing scheme for the slaves.
In the future, we plan to implement a load-dependent mapper by post-processing the output of the gain-based mapper.
Dhillon et al. [47] suggest to implement a load-dependent access scheme wherein uncoordinated strategy is for light load and coordinated strategy for heavy load.
In order to minimize the number of server and prepare computational resources Ajiro [4] implemented a load-balancing, least loaded algorithm and compared it with classical FFD (First Fit Decreasing) problem.
In this section, those layouts are tested in a dynamic system-level simulator implementing a highly loaded and extremely unbalanced indoor scenario, where traffic sharing must be carried out.
To overcome this challenge, we implemented a scalable load balancing strategy suitable for multi-stepped execution based on the hierarchical load balancing framework (Zheng 2005; Mei et al. 2011) in the Charm++ runtime.
By having the mapper list randomly ordered, we implement a simple load balancing mechanism.
Such an example can be found when implementing a global voltage analysis algorithm (load flow (Morton 2003)) which runs over a group of assets, and updates the assets' voltage at each timestep.
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