Mixed-Criticality Scheduling Upon Permitted Failure Probability and Dynamic Priority

Abstract

Many safety-critical real-time systems are considered certified when they meet failure probability requirements with respect to the maximum permitted incidences of failure per hour. In this article, the mixed-criticality task model with multiple worst case execution time (WCET) estimations is extended to incorporate such system-level certification restrictions. A new parameter is added to each task, characterizing the distribution of WCET estimations-the likelihood of all jobs of a task finishing their executions within the less pessimistic WCET estimates. Efficient algorithms are derived for scheduling mixed-criticality systems represented using this model for both uniprocessor and multiprocessor platforms for independent tasks. Furthermore, a 0/1 covariance matrix is introduced to represent the failure dependency between tasks. An efficient algorithm is proposed to schedule such failure-dependent tasks. Experimental analyses show our new model and algorithm outperform current state-of-the-art mixed-criticality scheduling algorithms.

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

“…The tax risk status of each enterprise can be divided into two categories, one is the working probability, and the other is the failure probability of enterprise tax work indicators [19]. From this, the probability of enterprise tax risk status is obtained, and the formula is as follows:…”