Date of Award

2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Computer and Information Science

First Advisor

Insup Lee

Second Advisor

Linh T. Phan

Abstract

Timing guarantee is critical to ensure the correctness of embedded software systems that

interact with the physical environment. As modern embedded real-time systems evolves,

they face three challenges: resource constraints, mixed-criticality, and multiprocessors. This

dissertation focuses on resource-efficient scheduling techniques for mixed-criticality systems

on multiprocessor platforms.

While Mixed-Criticality (MC) scheduling has been extensively studied on uniprocessor plat-

forms, the problem on multiprocessor platforms has been largely open. Multiprocessor al-

gorithms are broadly classified into two categories: global and partitioned. Global schedul-

ing approaches use a global run-queue and migrate tasks among processors for improved

schedulability. Partitioned scheduling approaches use per processor run-queues and can

reduce preemption/migration overheads in real implementation. Existing global scheduling

schemes for MC systems have suffered from low schedulability. Our goal in the first work is

to improve the schedulability of MC scheduling algorithms. Inspired by the fluid scheduling

model in a regular (non-MC) domain, we have developed the MC-Fluid scheduling algo-

rithm that executes a task with criticality-dependent rates. We have evaluated MC-Fluid in

terms of the processor speedup factor: MC-Fluid is a multiprocessor MC scheduling algo-

rithm with a speed factor of 4/3, which is known to be optimal. In other words, MC-Fluid

can schedule any feasible mixed-criticality task system if each processor is sped up by a

factor of 4/3.

Although MC-Fluid is speedup-optimal, it is not directly implementable on multiprocessor

platforms of real processors due to the fractional processor assumption where multiple task

can be executed on one processor at the same time. In the second work, we have considered

the characteristic of a real processor (executing only one task at a time) and have developed

the MC-Discrete scheduling algorithm for regular (non-fluid) scheduling platforms. We have

shown that MC-Discrete is also speedup-optimal.

While our previous two works consider global scheduling approaches, our last work con-

siders partitioned scheduling approaches, which are widely used in practice because of low

implementation overheads. In addition to partitioned scheduling, the work consid-

ers the limitation of conventional MC scheduling algorithms that drops all low-criticality

tasks when violating a certain threshold of actual execution times. In practice, the system

designer wants to execute the tasks as much as possible. To address the issue, we have de-

veloped the MC-ADAPT scheduling framework under uniprocessor platforms to drop as few

low-criticality tasks as possible. Extending the framework with partitioned multiprocessor

platforms, we further reduce the dropping of low-criticality tasks by allowing migration of

low-criticality tasks at the moment of a criticality switch. We have evaluated the quality

of task dropping solution in terms of speedup factor. In existing work, the speedup factor

has been used to evaluate MC scheduling algorithms in terms of schedulability under the

worst-case scheduling scenario. In this work, we apply the speedup factor to evaluate MC

scheduling algorithms in terms of the quality of their task dropping solution under various

MC scheduling scenarios. We have derived that MC-ADAPT has a speedup factor of 1.618

for task dropping solution.

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