A deadlock resolution strategy based on spiking neural P systems

Deadlock resolution is a classic problem faced by operating and database systems. It not only affects the utilization of system resources, but also may bring unpredictable consequences to the events corresponding to deadlock processes. Spiking neural P systems (SN P systems) are neural networks built upon membrane computing models inspired by neuron communication via excitatory and inhibitory spikes. We explore the application of SN P systems to the optimal revocation of deadlock processes in order to overcome deficiencies in traditional deadlock resolution methods such as exhaustive method and time cost method. Our system determines the optimal order of revocation by calculating the time cost associated with the deadlock release process. Compared to the exhaustive method and time cost method, our system significantly reduces the time complexity of the deadlock revocation process, improving deadlock resolution efficiency and offering more opportunities for parallelism.

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