TY - GEN
T1 - DCNSim
T2 - 2013 ACM International Conference on Computing Frontiers, CF 2013
AU - Hu, Nongda
AU - Fu, Binzhang
AU - Sui, Xiufeng
AU - Li, Long
AU - Li, Tao
AU - Zhang, Lixin
PY - 2013
Y1 - 2013
N2 - Within today's large-scale data centers, the inter-node communication is often the major bottleneck. This fact recently blooms the data center network (DCN) research. Since building a real data center is cost prohibitive, most of DCN studies rely on simulations. Unfortunately, state-of-the-art network simulators have limited support for real world applications, which prevents researchers from first-hand investigation. To address this issue, we developed a unified and cross-layer simulation framework, namely the DCNSim. By leveraging the two widely deployed simulators, DCNSim introduces computer architecture solutions into DCN research. With DCNSim, one could run packet-level network simulation driven by commercial applications while varying computer and network parameters, such as CPU frequency, memory access latency, network topology and protocols. With extensive validations, we show that DCNSim could accurately capture performance trends caused by changing computer and network parameters. Finally, we argue that future DCN researches should consider computer architecture factors via several case studies.
AB - Within today's large-scale data centers, the inter-node communication is often the major bottleneck. This fact recently blooms the data center network (DCN) research. Since building a real data center is cost prohibitive, most of DCN studies rely on simulations. Unfortunately, state-of-the-art network simulators have limited support for real world applications, which prevents researchers from first-hand investigation. To address this issue, we developed a unified and cross-layer simulation framework, namely the DCNSim. By leveraging the two widely deployed simulators, DCNSim introduces computer architecture solutions into DCN research. With DCNSim, one could run packet-level network simulation driven by commercial applications while varying computer and network parameters, such as CPU frequency, memory access latency, network topology and protocols. With extensive validations, we show that DCNSim could accurately capture performance trends caused by changing computer and network parameters. Finally, we argue that future DCN researches should consider computer architecture factors via several case studies.
KW - Data center network
KW - Execution-driven simulation
KW - Full-system simulation
KW - Parallel discrete-event simulation
UR - http://www.scopus.com/inward/record.url?scp=84879516601&partnerID=8YFLogxK
U2 - 10.1145/2482767.2482792
DO - 10.1145/2482767.2482792
M3 - Conference contribution
AN - SCOPUS:84879516601
SN - 9781450320535
T3 - Proceedings of the ACM International Conference on Computing Frontiers, CF 2013
BT - Proceedings of the ACM International Conference on Computing Frontiers, CF 2013
Y2 - 14 May 2013 through 16 May 2013
ER -