The Comparison Approach to Multiprocessor Fault Diagnosis
IEEE Transactions on Computers
A Generalized Theory for System Level Diagnosis
IEEE Transactions on Computers
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
A Group-Theoretic Model for Symmetric Interconnection Networks
IEEE Transactions on Computers
Dynamic Testing Strategy for Distributed Systems
IEEE Transactions on Computers
Distributed Diagnosis Algorithms for Regular Interconnected Structures
IEEE Transactions on Computers
IEEE Transactions on Computers
Better Adaptive Diagnosis of Hypercubes
IEEE Transactions on Computers
Optimal Adaptive Fault Diagnosis of Hypercubes
SWAT '00 Proceedings of the 7th Scandinavian Workshop on Algorithm Theory
On Adaptive Fault Diagnosis for Multiprocessor Systems
ISAAC '01 Proceedings of the 12th International Symposium on Algorithms and Computation
A partitioning method for efficient system-level diagnosis
Journal of Systems and Software
Reducing the Number of Sequential Diagnosis Iterations in Hypercubes
IEEE Transactions on Computers
A (4n-9)/3 diagnosis algorithm on n-dimensional cube network
Information Sciences: an International Journal
Three-round adaptive diagnosis in binary n-cubes
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
Adaptive system-level diagnosis for hypercube multiprocessors using a comparison model
Information Sciences: an International Journal
Hi-index | 14.99 |
System-level diagnosis is an important technique for fault detection and location in multiprocessor computing systems. Efficient diagnosis is highly desirable for sustaining the original system power. Moreover, effective diagnosis is particularly important for a multiprocessor system with high scalability but low connectivity. Most of the existing results are not applicable in practice because of the high diagnosis cost and limited diagnosability. Over-d fault diagnosis, where d is the diagnosability, has only been addressed using a probabilistic method in the literature. Aiming at these two issues, we propose a hierarchical adaptive system-level diagnosis approach for hypercube systems using a divide-and-conquer strategy. We first propose a conceptual algorithm HADA to formulate a rigorous analysis. Then we present its practical variant IHADA. In HADA and IHADA, the over-d fault problem is inherently tackled through a deterministic method. Three measures for diagnosis cost (diagnosis time, number of tests, and number of test links) are analyzed for the proposed algorithms. It is proved that the diagnosis cost required by our approach is lower than in previous diagnosis algorithms. It is shown that the diagnosis cost for the proposed algorithms depends on the number and location of faulty units in the system and the cost is extremely low when only a small number of faulty units exist. It is also shown that our algorithms are characterized by lower costs than a pessimistic diagnosis algorithm which trades lower diagnosis cost for a lower degree of accuracy. Experimental results on the nCUBE are provided to substantiate the practicality of the proposed approach.