A flexible multi-dimensional QoS performance measure framework for distributed heterogeneous systems

  • 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:

  • Electrical and Computer Engineering School, Purdue University, W. Lafayette, USA 47907-1285;Department of Computer Science, Naval Postgraduate School, Monterey, USA 93943;Department of Computer Science, Naval Postgraduate School, Monterey, USA 93943;Department of Electrical and Computer Engineering and Department of Computer Science, Colorado State University, Ft. Collins, USA 80523-1373;Department of Computer Science, Naval Postgraduate School, Monterey, USA 93943;Department of Computer Science, Naval Postgraduate School, Monterey, USA 93943;Anteon Corporation, Monterey, USA 93940;Department of Computer Science, Naval Postgraduate School, Monterey, USA 93943;Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, USA 90089;GridIQ, USA 92064-3453

  • Venue:
  • Cluster Computing
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

When users' tasks in a distributed heterogeneous computing environment (e.g., cluster of heterogeneous computers) are allocated resources, the total demand placed on some system resources by the tasks, for a given interval of time, may exceed the availability of those resources. In such a case, some tasks may receive degraded service or be dropped from the system. 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) measure presented here is a measure for quantifying this collective value. The FISC measure is a flexible multi-dimensional measure such that any task attribute can be inserted and may include priorities, versions of a task or data, deadlines, situational mode, security, application- and domain-specific QoS, and task dependencies. For an environment where it is important to investigate how well data communication requests are satisfied, the data communication request satisfied can be the basis of the FISC measure instead of tasks completed. The motivation behind the FISC measure is to determine the performance of resource management schemes if tasks have multiple attributes that needs to be satisfied. The goal of this measure is to compare the results of different resource management heuristics that are trying to achieve the same performance objective but with different approaches.