Mapping subtasks with multiple versions on an ad hoc grid

  • Authors:

  • S. Shivle;P. Sugavanam;H. J. Siegel;A. A. Maciejewski;T. Banka;K. Chindam;S. Dussinger;A. Kutruff;P. Penumarthy;P. Pichumani;P. Satyasekaran;D. Sendek;J. Smith;J. Sousa;J. Sridharan;J. Velazco

  • Affiliations:

  • Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA and Colorado State University, Department of Computer Science, Fort Collins, CO 80523, ...;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;HP Technologies, Fort Collins, CO 80528-9544, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;IBM Corporation, Boulder, CO 80301, USA;HP Technologies, Fort Collins, CO 80528-9544, USA;Colorado State University, Department of Electrical and Computer Engineering, Fort Collins CO 80523-1373, USA;Stelex, San Juan, Puerto Rico

  • Venue:
  • Parallel Computing - Heterogeneous computing
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

An ad hoc grid is a heterogeneous computing system composed of mobile devices. Each computing resource is constrained in battery energy. The problem being studied is to assign statically computing resources to the subtasks of an application that has an execution time constraint, when the resources are oversubscribed. All subtasks must be executed; to accommodate this in an oversubscribed environment, each subtask has two versions: the primary or full version, and the secondary or degraded version. The secondary version utilizes only 10% of the resources that the primary version requires, and produces only 10% of the data output for the subsequent children subtasks. Thus, the degraded version (secondary version) represents a reduced capability of lesser overall value, while consuming fewer resources. The goal is to assign resources so that the application meets an execution time constraint and the battery energy constraint while minimizing the number of degraded versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented, evaluated, and compared.