A QoS performance measure framework for distributed heterogeneous networks

  • Authors:
  • Jong-Kook Kim;Debra A. Hensgen;Taylor Kidd;Howard Jay Siegel;David St. John;Cynthia Irvine;Tim Levin;N. Wayne Porter;Viktor K. Prasanna;Richard F. Freund

  • Affiliations:
  • Purdue University, ECE School, West Lafayette, IN;Naval Postgraduate School, CS and ECE Departments, Monterey, CA;Naval Postgraduate School, CS and ECE Departments, Monterey, CA;Purdue University, ECE School, West Lafayette, IN;Anteon Corporation, Monterey, CA;Naval Postgraduate School, CS and ECE Departments, Monterey, CA;Anteon Corporation, Monterey, CA;Naval Postgraduate School, CS and ECE Departments, Monterey, CA;University of Southern California, Department of EE-Systems, Los Angeles, CA;NOEMIX Inc., San Diego, CA

  • Venue:
  • EURO-PDP'00 Proceedings of the 8th Euromicro conference on Parallel and distributed processing
  • Year:
  • 2000

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Abstract

In a distributed heterogeneous computing environment, users' tasks are allocated resources to simultaneously satisfy, to varying degrees, the tasks' different, and possibly conflicting, quality of service (QoS) requirements. When the total demand placed on system resources by the tasks, for a given interval of time, exceeds the resources available, some tasks will receive degraded service or no service at all. One part of a measure to quantify the success of a resource management system (RMS) in such a distributed environment is the collective value of the tasks completed during an interval of time, as perceived by the user, application, or policy maker. The Flexible Integrated System Capability (FISC) ratio introduced here is a measure for quantifying this collective value. The FISC ratio is a multi-dimensional measure, and may include priorities, versions of a task or data, deadlines, situational mode, security, application- and domainspecific QoS, and dependencies. In addition to being used for evaluating and comparing RMSs, the FISC ratio can be incorporated as part of the objective function in a system's scheduling heuristics.