Optimal Size of Job Pool for Initiating a Scheduling Event

Abstract

In today's military with its dwindling resources, making the best use of computers, particularly to support real time commercial off the shelf (COTS) applications, is becoming critical for success. Resource Management Systems (RMS) strive to address this issue. The RMS's job scheduler is needed to ensure good quality of service (QoS) to all applications. This research uses discrete event simulation experiments to investigate the cost tradeoff between improving system performance through grouping incoming jobs to create better schedules, versus both: (1) the time spent waiting for the group to accumulate, and (2) the additional cost of computing schedules involving more jobs. A MaxMin O(MN(2)) greedy scheduling algorithm attempting to minimize the total time in system was used in these experiments. We analyzed the data generated from numerous experiments that used typical input parameters. As a result of this effort, we conclude that job grouping should be used when the utilization factor for the system is near 1.0, or precisely when the mean arrival rate is comparable to the total mean service rate of the processors. At this utilization rate, the group size should be equal to the number of machines in the system. However, when the utilization factor is significantly different from 1.0, each job should be scheduled as it arrives.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1999
Accession Number
ADA370839

Entities

People

  • James M. Breitinger

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Databases
  • Information Science
  • Information Systems
  • Mathematical Models
  • Monte Carlo Method
  • Network Science
  • Operating Systems
  • Programming Languages
  • Random Variables
  • Regression Analysis
  • Resource Management
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Economics
  • Operations Research