Tackling the Memory Balancing Problem for Large-Scale Network Simulation

A key obstacle to large-scale network simulation over PC clusters is the memory balancing problem where a memory-overloaded machine can slow down an entire simulation due to disk I/O overhead. Memory balancing is complicated by (i) the difficulty of estimating the peak memory consumption of a group of nodes during network partitioning--a consequence of per-node peak memory not being synchronized--and (ii) trade-off with CPU balancing whose cost metric depends on total--as opposed to maximum--number of messages processed over time. We investigate memory balancing for large-scale network simulation which admits solutions for memory estimation and balancing not availed to small-scale or discrete-event simulation in general. First, we advance a measurement methodology for accurate and efficient memory estimation, and we establish a trade-off between memory and CPU balancing under maximum and total cost metrics. Second, we show that joint memory-CPU balancing can overcome the performance t...

• Type: Conference
• Year: 2008
• Where: MASCOTS
• Authors: Hyojeong Kim, Kihong Park
• Keywords: Large-scale Network Simulation, MASCOTS 2008, Memory Balancing, Modeling And Simulation, Network Simulation