Reliable Object Storage to Support Atomic Actions.

Abstract

To preserve the consistency of on-line, long-lived, distributed data in the presence of concurrency and in the event of hardware failures, it is necessary to ensure atomicity and data resiliency in applications. The programming language Argus is designed to support such applications. This thesis investigates the mechanism needed to support the notion of data resiliency present in Argus. Data resiliency means that the probability is very high that the crash of a node or storage device in a distributed system does not cause the loss of vital data. Data resiliency requires the use of stable storage devices, memory devices that survive failure to a high probability. This thesis is not concerned with how to implement stable storage devices, but rather with how to organize the use of stable storage. The thesis presents a new organization of stable storage called the hybrid log that provides fast writing of information to stable storage and reasonably fast recovery of information from stable storage. In the context of this scheme, various algorithms are developed for writing objects to the log, recovering objects from the log, and housekeeping the log.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1983
Accession Number
ADA136484

Entities

People

  • B. M. Oki

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Computer Science
  • Computers
  • Data Storage Systems
  • Database Management Systems
  • Databases
  • Department Of Defense
  • Failure Mode And Effect Analysis
  • Fish
  • Information Processing
  • Information Systems
  • Language
  • Magnetic Disks
  • Memory Devices
  • Military Research
  • Programming Languages

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Mathematical Modeling and Probability Theory.
  • Systems Analysis and Design