Deakin University
Browse
ren-contentionaware-inpress-2019.pdf (620.77 kB)

Contention-aware prediction for performance impact of task co-running in multicore computers

Download (620.77 kB)
journal contribution
posted on 2019-01-01, 00:00 authored by S Ren, L He, J Li, Z Chen, P Jiang, Chang-Tsun LiChang-Tsun Li
© 2019, The Author(s). In this paper, we investigate the influential factors that impact on the performance when the tasks are co-running on a multicore computers. Further, we propose the machine learning-based prediction framework to predict the performance of the co-running tasks. In particular, two prediction frameworks are developed for two types of task in our model: repetitive tasks (i.e., the tasks that arrive at the system repetitively) and new tasks (i.e., the task that are submitted to the system the first time). The difference between which is that we have the historical running information of the repetitive tasks while we do not have the prior knowledge about new tasks. Given the limited information of the new tasks, an online prediction framework is developed to predict the performance of co-running new tasks by sampling the performance events on the fly for a short period and then feeding the sampled results to the prediction framework. We conducted extensive experiments with the SPEC2006 benchmark suite to compare the effectiveness of different machine learning methods considered in this paper. The results show that our prediction model can achieve the accuracy of 99.38% and 87.18% for repetitive tasks and new tasks, respectively.

History

Journal

Wireless networks

Pagination

1 - 8

Publisher

Springer

Location

Berlin, Germany

ISSN

1022-0038

eISSN

1572-8196

Language

eng

Notes

In Press

Publication classification

C1 Refereed article in a scholarly journal

Usage metrics

    Research Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC