TY - GEN
T1 - Systematic Topology Design for Large-Scale Networks
T2 - 38th IEEE Conference on Computer Communications, INFOCOM 2020
AU - Chang, Yijia
AU - Huang, Xi
AU - Deng, Longxiulin
AU - Shao, Ziyu
AU - Zhang, Junshan
N1 - Funding Information:
* This research was supported in part by the Nature Science Foundation of Shanghai under Grant 19ZR1433900, NSF under Grant CPS-1739344, ARO under grant W911NF-16-1-0448, and the DTRA under Grant HDTRA1-13-1-0029. (Corresponding author: Ziyu Shao)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - For modern large-scale networked systems, ranging from cloud to edge computing systems, the topology design has a significant impact on the system performance in terms of scalability, cost, latency, throughput, and fault-tolerance. These performance metrics may conflict with each other and design criteria often vary across different networks. To date, there has been little theoretic foundation on topology designs from a prescriptive perspective, indicating that the current status quo of the design process is more of an art than a science. In this paper, we advocate a novel unified framework to describe, generate, and analyze topology design in a systematic fashion. By reverse-engineering existing topology designs and developing a fine-grained decomposition method for topology design, we propose a general procedure that serves as a common language to describe topology design. By proposing general criteria for the procedure, we devise a top-down approach to generate topology models, based on which we can systematically construct and analyze new topologies. To validate our approach, we leverage concrete tools based on combinatorial design theory and propose a novel layered topology model. With quantitative performance analysis, we reveal the trade-offs among performance metrics and generate new topologies with various advantages for different large-scale networks.
AB - For modern large-scale networked systems, ranging from cloud to edge computing systems, the topology design has a significant impact on the system performance in terms of scalability, cost, latency, throughput, and fault-tolerance. These performance metrics may conflict with each other and design criteria often vary across different networks. To date, there has been little theoretic foundation on topology designs from a prescriptive perspective, indicating that the current status quo of the design process is more of an art than a science. In this paper, we advocate a novel unified framework to describe, generate, and analyze topology design in a systematic fashion. By reverse-engineering existing topology designs and developing a fine-grained decomposition method for topology design, we propose a general procedure that serves as a common language to describe topology design. By proposing general criteria for the procedure, we devise a top-down approach to generate topology models, based on which we can systematically construct and analyze new topologies. To validate our approach, we leverage concrete tools based on combinatorial design theory and propose a novel layered topology model. With quantitative performance analysis, we reveal the trade-offs among performance metrics and generate new topologies with various advantages for different large-scale networks.
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U2 - 10.1109/INFOCOM41043.2020.9155462
DO - 10.1109/INFOCOM41043.2020.9155462
M3 - Conference contribution
AN - SCOPUS:85090297102
T3 - Proceedings - IEEE INFOCOM
SP - 347
EP - 356
BT - INFOCOM 2020 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 July 2020 through 9 July 2020
ER -