The \(g\)-good-neighbor conditional diagnosability of the crossed cubes under the PMC and MM* model
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Publication:1725692
DOI10.1016/j.tcs.2018.06.046zbMath1411.68023OpenAlexW2884743138MaRDI QIDQ1725692
Publication date: 14 February 2019
Published in: Theoretical Computer Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.tcs.2018.06.046
interconnection networksPMC modelcrossed cube\(g\)-good-neighbor conditional diagnosabilityMM* model
Network design and communication in computer systems (68M10) Graph theory (including graph drawing) in computer science (68R10) Reliability, testing and fault tolerance of networks and computer systems (68M15)
Related Items (8)
Diagnosability and hybrid diagnosability of some classes of graphs under the BPMC model ⋮ Diagnosability for two families of composition networks ⋮ Restricted connectivity and good-neighbor diagnosability of split-star networks ⋮ \(r\)-component diagnosability of hypercubes under the PMC model ⋮ The \(t/k\)-diagnosability of \(m\)-ary \(n\)-cube networks ⋮ Fault diagnosability of regular networks under the hybrid PMC model ⋮ The diagnosability of wheel networks with the condition: 2-extra ⋮ Hamiltonian Cycle Embeddings in Faulty Hypercubes Under the Forbidden Faulty Set Model
Cites Work
- Embedding a mesh of trees in the crossed cube
- The \(g\)-good-neighbor conditional diagnosability of hypercube under PMC model
- The \(g\)-good-neighbor conditional diagnosability of star graphs under the PMC and MM* model
- Conditional diagnosability of bubble-sort star graphs
- The relationship between extra connectivity and conditional diagnosability of regular graphs under the PMC model
- Embedding binary trees into crossed cubes
- The Conditional Diagnosability of k-Ary n-Cubes under the Comparison Diagnosis Model
- Strong Diagnosability and Conditional Diagnosability of Multiprocessor Systems and Folded Hypercubes
- On self-diagnosable multiprocessor systems: diagnosis by the comparison approach
- Optimization of Reduced Dependencies for Synchronous Sequential Machines
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