82 research outputs found

    Star Structure Connectivity of Folded hypercubes and Augmented cubes

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    The connectivity is an important parameter to evaluate the robustness of a network. As a generalization, structure connectivity and substructure connectivity of graphs were proposed. For connected graphs GG and HH, the HH-structure connectivity κ(G;H)\kappa(G; H) (resp. HH-substructure connectivity κs(G;H)\kappa^{s}(G; H)) of GG is the minimum cardinality of a set of subgraphs FF of GG that each is isomorphic to HH (resp. to a connected subgraph of HH) so that G−FG-F is disconnected or the singleton. As popular variants of hypercubes, the nn-dimensional folded hypercubes FQnFQ_{n} and augmented cubes AQnAQ_{n} are attractive interconnected network prototypes for multiple processor systems. In this paper, we obtain that κ(FQn;K1,m)=κs(FQn;K1,m)=⌈n+12⌉\kappa(FQ_{n};K_{1,m})=\kappa^{s}(FQ_{n};K_{1,m})=\lceil\frac{n+1}{2}\rceil for 2⩽m⩽n−12\leqslant m\leqslant n-1, n⩾7n\geqslant 7, and κ(AQn;K1,m)=κs(AQn;K1,m)=⌈n−12⌉\kappa(AQ_{n};K_{1,m})=\kappa^{s}(AQ_{n};K_{1,m})=\lceil\frac{n-1}{2}\rceil for 4⩽m⩽3n−1544\leqslant m\leqslant \frac{3n-15}{4}

    The star-structure connectivity and star-substructure connectivity of hypercubes and folded hypercubes

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    As a generalization of vertex connectivity, for connected graphs GG and TT, the TT-structure connectivity κ(G,T)\kappa(G, T) (resp. TT-substructure connectivity κs(G,T)\kappa^{s}(G, T)) of GG is the minimum cardinality of a set of subgraphs FF of GG that each is isomorphic to TT (resp. to a connected subgraph of TT) so that G−FG-F is disconnected. For nn-dimensional hypercube QnQ_{n}, Lin et al. [6] showed κ(Qn,K1,1)=κs(Qn,K1,1)=n−1\kappa(Q_{n},K_{1,1})=\kappa^{s}(Q_{n},K_{1,1})=n-1 and κ(Qn,K1,r)=κs(Qn,K1,r)=⌈n2⌉\kappa(Q_{n},K_{1,r})=\kappa^{s}(Q_{n},K_{1,r})=\lceil\frac{n}{2}\rceil for 2≤r≤32\leq r\leq 3 and n≥3n\geq 3. Sabir et al. [11] obtained that κ(Qn,K1,4)=κs(Qn,K1,4)=⌈n2⌉\kappa(Q_{n},K_{1,4})=\kappa^{s}(Q_{n},K_{1,4})=\lceil\frac{n}{2}\rceil for n≥6n\geq 6, and for nn-dimensional folded hypercube FQnFQ_{n}, κ(FQn,K1,1)=κs(FQn,K1,1)=n\kappa(FQ_{n},K_{1,1})=\kappa^{s}(FQ_{n},K_{1,1})=n, κ(FQn,K1,r)=κs(FQn,K1,r)=⌈n+12⌉\kappa(FQ_{n},K_{1,r})=\kappa^{s}(FQ_{n},K_{1,r})=\lceil\frac{n+1}{2}\rceil with 2≤r≤32\leq r\leq 3 and n≥7n\geq 7. They proposed an open problem of determining K1,rK_{1,r}-structure connectivity of QnQ_n and FQnFQ_n for general rr. In this paper, we obtain that for each integer r≥2r\geq 2, κ(Qn;K1,r)=κs(Qn;K1,r)=⌈n2⌉\kappa(Q_{n};K_{1,r})=\kappa^{s}(Q_{n};K_{1,r})=\lceil\frac{n}{2}\rceil and κ(FQn;K1,r)=κs(FQn;K1,r)=⌈n+12⌉\kappa(FQ_{n};K_{1,r})=\kappa^{s}(FQ_{n};K_{1,r})= \lceil\frac{n+1}{2}\rceil for all integers nn larger than rr in quare scale. For 4≤r≤64\leq r\leq 6, we separately confirm the above result holds for QnQ_n in the remaining cases

    The structure connectivity of Data Center Networks

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    Last decade, numerous giant data center networks are built to provide increasingly fashionable web applications. For two integers m≥0m\geq 0 and n≥2n\geq 2, the mm-dimensional DCell network with nn-port switches Dm,nD_{m,n} and nn-dimensional BCDC network BnB_{n} have been proposed. Connectivity is a basic parameter to measure fault-tolerance of networks. As generalizations of connectivity, structure (substructure) connectivity was recently proposed. Let GG and HH be two connected graphs. Let F\mathcal{F} be a set whose elements are subgraphs of GG, and every member of F\mathcal{F} is isomorphic to HH (resp. a connected subgraph of HH). Then HH-structure connectivity κ(G;H)\kappa(G; H) (resp. HH-substructure connectivity κs(G;H)\kappa^{s}(G; H)) of GG is the size of a smallest set of F\mathcal{F} such that the rest of GG is disconnected or the singleton when removing F\mathcal{F}. Then it is meaningful to calculate the structure connectivity of data center networks on some common structures, such as star K1,tK_{1,t}, path PkP_k, cycle CkC_k, complete graph KsK_s and so on. In this paper, we obtain that κ(Dm,n;K1,t)=κs(Dm,n;K1,t)=⌈n−11+t⌉+m\kappa (D_{m,n}; K_{1,t})=\kappa^s (D_{m,n}; K_{1,t})=\lceil \frac{n-1}{1+t}\rceil+m for 1≤t≤m+n−21\leq t\leq m+n-2 and κ(Dm,n;Ks)=⌈n−1s⌉+m\kappa (D_{m,n}; K_s)= \lceil\frac{n-1}{s}\rceil+m for 3≤s≤n−13\leq s\leq n-1 by analyzing the structural properties of Dm,nD_{m,n}. We also compute κ(Bn;H)\kappa(B_n; H) and κs(Bn;H)\kappa^s(B_n; H) for H∈{K1,t,Pk,Ck∣1≤t≤2n−3,6≤k≤2n−1}H\in \{K_{1,t}, P_{k}, C_{k}|1\leq t\leq 2n-3, 6\leq k\leq 2n-1 \} and n≥5n\geq 5 by using gg-extra connectivity of BnB_n

    Computational methods and software systems for dynamics and control of large space structures

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    Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers

    A Local Diagnosis Algorithm for Hypercube-like Networks under the BGM Diagnosis Model

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    System diagnosis is process of identifying faulty nodes in a system. An efficient diagnosis is crucial for a multiprocessor system. The BGM diagnosis model is a modification of the PMC diagnosis model, which is a test-based diagnosis. In this paper, we present a specific structure and propose an algorithm for diagnosing a node in a system under the BGM model. We also give a polynomial-time algorithm that a node in a hypercube-like network can be diagnosed correctly in three test rounds under the BGM diagnosis model

    Genetic neural networks on MIMD computers

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