doi:10.1016/j.peva.2004.10.015
Copyright © 2004 Elsevier B.V. All rights reserved.
The impact of predictive inaccuracies on execution scheduling
References and further reading may be available for this article. To view references and further reading you must
purchase this article.
Stephen A. Jarvis
,
, Ligang He, Daniel P. Spooner and Graham R. Nudd
High Performance Systems Group, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
Available online 7 December 2004.
Abstract
This paper investigates the underlying impact of predictive inaccuracies on execution scheduling, with particular reference to execution time predictions. This study is conducted from two perspectives: from that of job selection and from that of resource allocation, both of which are fundamental components in execution scheduling. A new performance metric, termed the degree of misperception, is introduced to express the probability that the predicted execution times of jobs display different ordering characteristics from their real execution times due to inaccurate prediction. Specific formulae are developed to calculate the degree of misperception in both job selection and resource allocation scenarios. The parameters which influence the degree of misperception are also extensively investigated. The results presented in this paper are of significant benefit to scheduling approaches that take into account predictive data; the results are also of importance to the application of these scheduling techniques to real-world high-performance systems.
Keywords: Performance prediction; Execution time; Scheduling; Job selection; Resource allocation; Performance evaluation
Fig. 1. (a) The predicted execution times of jobs J1 and J2 do not overlap and (b) the corresponding coordinate area from which the predictive errors y1 and y2 can be assigned values.
Fig. 2. (a) The predicted execution times of jobs J1 and J2 overlap, but the lower limit of the predicted execution time for J2 does not cover x1 and (b) the corresponding coordinate area of predicted errors of J1 and J2 (y1 and y2) in which the misperception occurs (the area is a triangle).
Fig. 3. (a) The predicted execution times of jobs J1 and J2 overlap and the lower limit of the predicted execution time for J2 covers x1 and (b) the corresponding coordinate area of predicted errors of J1 and J2 (y1 and y2) in which the misperception occurs (the area is a trapezoid).
Fig. 4. Impact of the parameters a and b on
: (a) the impact of the range size of predicted errors and (b) the impact of the range location of predicted errors.
Fig. 5. The impact of actual execution times on the degree of misperception: (a) the impact of the range size of actual execution times and (b) the impact of the range location of actual execution times.
Fig. 6. Impact of the parameters a and b on
: (a) the impact of the range size of predicted errors and (b) the impact of the range location of predicted errors.
Fig. 7. The impact of computer weight (heterogeneity) on
: (a) the impact of the size of computer weights and (b) the impact of the weight difference between computers.
Table 1.
The range of job execution times in the three job streams

Table 2.
Default values for the experimental parameters


Corresponding author. Tel.: +44 2476 524258; fax: +44 2476 573024.