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.
| Original language | English |
|---|---|
| Pages (from-to) | 127-139 |
| Number of pages | 13 |
| Journal | Performance Evaluation |
| Volume | 60 |
| Issue number | 1-4 |
| DOIs | |
| Publication status | Published - May 2005 |
Bibliographical note
Funding Information:This work is sponsored in part by grants from the NASA AMES Research Center (administrated by USARDSG, Contract No. N68171-01-C-9012), the EPSRC (Contract No. GR/R47424/01) and the EPSRC e-Science Core Programme (Contract No. GR/S03058/01).
Keywords
- Execution time
- Job selection
- Performance evaluation
- Performance prediction
- Resource allocation
- Scheduling
ASJC Scopus subject areas
- Software
- Modelling and Simulation
- Hardware and Architecture
- Computer Networks and Communications