  Mixed-Criticality (MC) systems consolidate multiple functionalities with
different criticalities onto a single hardware platform. Such systems improve
the overall resource utilization while guaranteeing resources to critical
tasks. In this paper, we focus on the problem of partitioned multiprocessor MC
scheduling, in particular the problem of designing efficient partitioning
strategies. We develop two new partitioning strategies based on the principle
of evenly distributing the difference between total high-critical utilization
and total low-critical utilization for the critical tasks among all processors.
By balancing this difference, we are able to reduce the pessimism in
uniprocessor MC schedulability tests that are applied on each processor, thus
improving overall schedulability. To evaluate the schedulability performance of
the proposed strategies, we compare them against existing partitioned
algorithms using extensive experiments. We show that the proposed strategies
are effective with both dynamic-priority Earliest Deadline First with Virtual
Deadlines (EDF-VD) and fixed-priority Adaptive Mixed-Criticality (AMC)
algorithms. Specifically, our results show that the proposed strategies improve
schedulability by as much as 28.1% and 36.2% for implicit and
constrained-deadline task systems respectively.
