Runtime estimation using the case-based reasoning approach for scheduling in a grid environment
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Future Generation Computer Systems
| Hi-index | 0.00 |
Long term load prediction can assist task scheduling and load balancing greatly in distributed environment such as computational grid. Due to the dynamic property of grid environment, fixed-parameter prediction model can not exert its forecast capability completely. In this paper we first observe and analyze parameters’ impact on prediction accuracy for our previous long term load prediction hybrid model (HModel) in detail. And then, a parameter-level adaptive method based on previous analysis is proposed in order to make HModel adapt to the time-varying characteristics of load in computational grid. The results of the experiments demonstrate that our adaptive hybrid model (AHModel) outperforms the widely used autoregressive (AR) model in long term load prediction significantly, and it also achieves obvious reduction in prediction mean square error comparing with HModel which uses fixed parameter value.