Control-Theoretic Formulation of Operating Systems Resource Management Policies.

Abstract

In this thesis we propose the following general control-theoretic approach to the formulation of resource management policies for operating systems. (1) In order to develop a resource management policy, model the corresponding program behavior as a stochastic process. (2) Using identification techniques and empirical data, identify a suitable model structure for the process and estimate typical values of model parameters. (3) Based on the model, formulate a prediction strategy for the stochastic process, and hence a resource management policy. The policy so obtained is dynamic in the sense that it varies the allocation of the system resource to a user job depending upon the recent past behavior of the job. It, thus, provides the run time optimization not possible with the queueing theory approach. Also, notice that the individuality of the job is fully exploited. The key step in the approach is the formulation of the stochastic process model in such a way that the allocation problem reduces to a prediction problem. We exemplify this approach by formulating control-theoretic policies for CPU scheduling and page replacement. Policies for allocation of other shared resources (e.g., disks) can be, similarly, formulated. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1978
Accession Number
ADA110392

Entities

People

  • Rajendra Kumar Jain

Organizations

  • Harvard University

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computer Programs
  • Computers
  • Control Systems
  • Data Mining
  • Data Science
  • Databases
  • Information Processing
  • Information Science
  • Knowledge Management
  • Mathematical Filters
  • Network Science
  • Operating Systems
  • Probabilistic Models
  • Random Variables
  • Stationary Processes
  • Stochastic Processes
  • Surveys

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Parallel and Distributed Computing.
  • Systems Analysis and Design