Performance Studies of Dynamic Load Balancing in Distributed Systems

Abstract

Distributed systems are often characterized by uneven loads on hosts and other resources. In this thesis, the problems concerning dynamic load balancing in loosely-coupled distributed systems are studied using trace-driven simulation, implementation, and measurement. Information about job CPU and input/output demands is collected from three production systems and used as input to a simulator that includes a representative central processing units scheduling policy and considers the message exchange and job transfer costs explicitly. A prototype load balancer is implemented in the Berkeley UNIX and Sun/UNIX environments, and the results of a large number of measurement experiments performed on six workstations are presented. The quality of two families of load indices, one based on resource queue length, the other on resource utilization, is evaluated in the context of dynamic load balancing. The performances of seven algorithms using different load information exchange and job placement strategies are compared. The factors that affect load balancing performance, and the impacts of load balancing on individuals hosts and on each type of job are also quantitatively investigated.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1987
Accession Number
ADA197130

Entities

People

  • Songnian Zhou

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Application Software
  • Central Processing Units
  • Computer Networks
  • Computer Programming
  • Computer Science
  • Computers
  • Dynamic Loads
  • Information Exchange
  • Local Area Networks
  • Measurement
  • Operating Systems
  • Parallel Computing
  • Probability
  • Processing Equipment
  • Servers (Computer Hardware)
  • Simulators
  • Workload

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.