Optimal Policy for Database Batch Operations: Backup, Checkpointing, and Batch Update.

Abstract

The purpose of this paper is to present a general model for determining the optimal frequency of batch operations. Specifically, optimal backup, checkpointing, and batch updating policies are derived. Our approach exploits inventory parallels, by seeking the optimal number of items--rather than a time interval--to trigger a batch. The Renewal Reward Theorem is used to find the average long run costs for backup, recovery, and item storage, per unit time, which is then minimized to find the optimal backup policy. This approach allows us to make far less restrictive assumptions about the updata arrival process than did previous models, as well as to include storage costs for the updates. The optimal checkpointing and batch updating policies are shown to be special cases of this optimal backup policy. The derivation of previous results as special cases of this model, and an example, demonstrate the generality of the methodology we develop. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1976
Accession Number
ADA037233

Entities

People

  • Guy M. Lohman
  • John A. Muckstadt

Organizations

  • Cornell University College of Engineering

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Databases
  • Dead Time
  • Distribution Functions
  • Engineering
  • Frequency
  • Industrial Engineering
  • Instructions
  • Intervals
  • Inventory
  • Maintenance
  • New York
  • Numbers
  • Operations Research
  • Probability
  • Random Variables
  • Square Roots
  • Time Intervals

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Science.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms