A Model for Determining Task Set Schedulability in the Presence of System Effects

Abstract

This research developed a parameterized model that accounts for system overhead and determines when an Ada runtime environment can no longer successfully execute a given Ada task set and still meet all deadlines. The Ada Compiler Evaluation Capability benchmark was used to characterize an actual runtime environment. Using that data, a generic model of a preemptive, rate monotonic priority based runtime system was developed which accounts for overhead due to clock updates, context switching, task suspension, and synchronization. Validation was based on the Hartstone benchmark. First, the benchmark was executed using, the actual runtime environment. Then, those results were compared with the execution of the benchmark using the model. In all cases, except one, the model predicted the point where the task set would fail. A runtime system optimization omitted from model caused the single failure. Experiments conducted using the model allowed the demonstration of the following results. System overhead can be modeled within the existing framework of rate monotonic scheduling theory. Runtime optimizations can be extremely sensitive to phase relationships between task periods and workloads and can render a schedulable task set unschedulable. Requirements of the task set and the performance of the runtime system must be considered simultaneously.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1992
Accession Number
ADA258915

Entities

People

  • Rusty O. Baldwin

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Compilers
  • Computer Programming
  • Computer Programs
  • Computers
  • Control Systems
  • Descriptive Analytics
  • Embedded Systems
  • Engineering
  • Failure Mode And Effect Analysis
  • Operating Systems
  • Personal Computers
  • Scheduling (Production)
  • Simulations
  • Software Development
  • Test And Evaluation
  • Workload

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  • Computer science
  • Engineering

Readers

  • Parallel and Distributed Computing.
  • Regression Analysis.

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  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks