TY - JOUR
T1 - Enabling high fault-tolerant embedding capability of alternating group graphs
AU - Zhuang, Hongbin
AU - Li, Xiao Yan
AU - Wang, Dajin
AU - Lin, Cheng Kuan
AU - Zhao, Kun
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/9
Y1 - 2024/9
N2 - The Hamiltonian path/cycle serves as a robust tool for transmitting messages within parallel and distributed systems. However, the prevalent device-intensive nature of these systems often leads to the occurrence of faults. Tackling the critical challenge of tolerating numerous faults when constructing Hamiltonian paths and cycles in these systems is of utmost significance. The alternating group graph AGn is a suitable interconnection network for building parallel and distributed systems due to its outstanding topological properties. Many studies have been dedicated to the fault-tolerant embedding of Hamiltonian paths and cycles in AGn, but they all fail to reach the desired level of fault-tolerance. In this paper, we aim to enhance the edge fault-tolerant embedding capability of AGn by leveraging a newly emerged fault model, called the Partitioned Edge Fault (PEF) model. We prove the existence of Hamiltonian paths/cycles tolerating large-scale edge faults in AGn under the PEF model. Additionally, we propose a fault-tolerant Hamiltonian path embedding algorithm for AGn under the PEF model and validate its effectiveness through experiments. To gauge the significance of each missed edge, we conduct experimental calculations for the average path length of every edge along the Hamiltonian path produced by the proposed embedding algorithm. Furthermore, the fault-tolerance comparison and experimental analysis reveal that our results improve the fault-tolerant embedding capability of AGn from a linear level to an exponential one. To our knowledge, this is the first time that a Hamiltonian path/cycle embedding approach is proposed and actually implemented for AGn with large-scale edge faults.
AB - The Hamiltonian path/cycle serves as a robust tool for transmitting messages within parallel and distributed systems. However, the prevalent device-intensive nature of these systems often leads to the occurrence of faults. Tackling the critical challenge of tolerating numerous faults when constructing Hamiltonian paths and cycles in these systems is of utmost significance. The alternating group graph AGn is a suitable interconnection network for building parallel and distributed systems due to its outstanding topological properties. Many studies have been dedicated to the fault-tolerant embedding of Hamiltonian paths and cycles in AGn, but they all fail to reach the desired level of fault-tolerance. In this paper, we aim to enhance the edge fault-tolerant embedding capability of AGn by leveraging a newly emerged fault model, called the Partitioned Edge Fault (PEF) model. We prove the existence of Hamiltonian paths/cycles tolerating large-scale edge faults in AGn under the PEF model. Additionally, we propose a fault-tolerant Hamiltonian path embedding algorithm for AGn under the PEF model and validate its effectiveness through experiments. To gauge the significance of each missed edge, we conduct experimental calculations for the average path length of every edge along the Hamiltonian path produced by the proposed embedding algorithm. Furthermore, the fault-tolerance comparison and experimental analysis reveal that our results improve the fault-tolerant embedding capability of AGn from a linear level to an exponential one. To our knowledge, this is the first time that a Hamiltonian path/cycle embedding approach is proposed and actually implemented for AGn with large-scale edge faults.
KW - Alternating group graphs
KW - Embedding
KW - Fault-tolerance
KW - Interconnection networks
KW - Partitioned edge faults
UR - http://www.scopus.com/inward/record.url?scp=85190975121&partnerID=8YFLogxK
U2 - 10.1016/j.future.2024.04.019
DO - 10.1016/j.future.2024.04.019
M3 - Article
AN - SCOPUS:85190975121
SN - 0167-739X
VL - 158
SP - 110
EP - 121
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -