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

Published: 01 July 2006 Publication History

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.

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Published In

cover image Cluster Computing
Cluster Computing  Volume 9, Issue 3
July 2006
145 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2006

Author Tags

  1. Distributed computing
  2. Heterogeneous computing
  3. Performance metrics
  4. Resource management system
  5. Scheduling

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  • (2011)Game-theoretic static load balancing for distributed systemsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2010.11.01671:4(537-555)Online publication date: 1-Apr-2011
  • (2011)A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/gridCluster Computing10.1007/s10586-010-0145-414:2(199-210)Online publication date: 1-Jun-2011
  • (2008)SOAGProceedings of the 2008 9th IEEE/ACM International Conference on Grid Computing10.1109/GRID.2008.4662820(346-351)Online publication date: 29-Sep-2008
  • (2008)A hybrid Branch-and-Bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systemsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2007.05.01168:4(410-426)Online publication date: 1-Apr-2008
  • (2007)Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environmentJournal of Parallel and Distributed Computing10.1016/j.jpdc.2006.06.00567:2(154-169)Online publication date: 1-Feb-2007
  • (2005)Resource Allocation for Periodic Applications in a Shipboard EnvironmentProceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 0210.1109/IPDPS.2005.382Online publication date: 4-Apr-2005
  • (2005)Dynamic Mapping in Energy Constrained Heterogeneous Computing SystemsProceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 0110.1109/IPDPS.2005.177Online publication date: 4-Apr-2005

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