Abstract

Unlike typical computing systems, applications in real-time systems require strict timing guarantees. In the pursuit of providing guarantees, the complex dynamic behaviors of these systems are simplified using models to keep the analysis tractable. In order to guarantee safety, such models often involve pessimistic assumptions. While the amount of pessimism was reasonable for simple computing platforms, for modern platforms the pessimism involves ignoring features that improve performance such as cache usage, instruction pipelines, and more. In this work, we explore routing and scheduling problems in real-time systems, where the uncertainties in the operation are carefully accounted for by complex models and/or the routing and scheduling algorithms proposed. For real-time scheduling problems, we incorporate the execution time distribution into the task model to design a system that can meet the maximum permitted incidences of failure per hour. We also consider the case where no failure is permitted and all jobs in the system must be scheduled without violating their timing requirements, throughout their operation. It is achieved on a varying speed multiprocessor platform. For real-time routing problems, we consider graphs whose edge cost distribution is dynamic and the routed packets have deadlines to be met. We then extend this problem to the case where the initial (discrete) distribution of the edge costs is fully known. We propose a technique to safely incorporate a reinforcement learning strategy once the system deviates from its initial distribution. Finally, we focus on practical improvements to the popular and optimal earliest deadline first scheduling algorithm, upon a uniprocessor setting. Specifically, we develop techniques to quantify and utilize the idle times to handle uncertainties in the form of additional run-time workloads, arbitrary self-suspensions, and execution time estimate overruns.

Notes

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Graduation Date

2022

Semester

Summer

Advisor

Guo, Zhishan

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Computer Engineering

Identifier

CFE0009274; DP0026878

URL

https://purls.library.ucf.edu/go/DP0026878

Language

English

Release Date

August 2023

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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