A new upper bound of the completion time of the background task in a foreground-background system

被引:0
|
作者
Asham A.D. [1 ,2 ]
机构
[1] Egyptian Academy for Engineering and Advanced Technology, Cairo Governorate
来源
关键词
Completion time; Fixed priority; Foreground-background; Real-time; Response-time; Upper bound;
D O I
10.1504/IJES.2020.108868
中图分类号
学科分类号
摘要
A foreground-background scheduling system is a simple real-time pre-emptive scheduler, which is commonly used in uniprocessor embedded systems. In this system, there is a single background task of the lowest priority and multiple foreground tasks have higher priorities. Foreground tasks may have different levels of priorities. Foreground tasks are allowed to pre-empt the background task. The background task takes a longer time to complete its execution because of the frequent interruptions caused by the foreground tasks. The completion time of the background task is calculated using the utilisation of the processor by the foreground tasks. In this paper, a new upper bound formula of the completion time of the background task is derived. The proposed formula gives a closer upper bound to the exact completion time compared to the existing bounds in the case of few foreground tasks and even it gives the exact time in certain cases for the heavily utilised systems. In addition, the proposed upper bound is not a recursive formula like that of the existing response time analysis. Copyright © 2020 Inderscience Enterprises Ltd.
引用
收藏
页码:188 / 199
页数:11
相关论文
共 50 条
  • [41] A MAPK/GK/1/∞ Queueing System with Generalized Foreground-Background Processor Sharing Discipline
    C. D'Apice
    M. L. Cristofano
    A. V. Pechinkin
    Automation and Remote Control, 2004, 65 : 1961 - 1967
  • [42] A MAPk/Gk/1/∞ queueing system with generalized foreground-background processor sharing discipline
    D'Apice, C
    Cristofano, ML
    Pechinkin, AV
    AUTOMATION AND REMOTE CONTROL, 2004, 65 (12) : 1961 - 1967
  • [43] Foreground-background separation and deblurring super-resolution method☆
    Liu, Xuebin
    Chen, Yuang
    Zhao, Chongji
    Yang, Jie
    Deng, Huan
    OPTICS AND LASERS IN ENGINEERING, 2025, 184
  • [44] Foreground-Background Parallel Compression With Residual Encoding for Surveillance Video
    Wu, Lirong
    Huang, Kejie
    Shen, Haibin
    Gao, Lianli
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 31 (07) : 2711 - 2724
  • [45] Real-time foreground-background segmentation using adaptive support vector machine algorithm
    Hao, Zhifeng
    Wen, Wen
    Liu, Zhou
    Yang, Xiaowei
    ARTIFICIAL NEURAL NETWORKS - ICANN 2007, PT 2, PROCEEDINGS, 2007, 4669 : 603 - +
  • [46] An Adaptive Foreground-Background Separation Method for Effective Binarization of Document Images
    Das, Bishwadeep
    Bhowmik, Showmik
    Saha, Aniruddha
    Sarkar, Ram
    PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2016), 2018, 614 : 515 - 524
  • [47] Unsupervised video rain streaks removal with deep foreground-background modeling
    Zhuang, Jun-Hao
    Luo, Yi-Si
    Zhao, Xi-Le
    Jiang, Tai-Xiang
    Chang, Yi
    Liu, Jun
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2024, 436
  • [48] Unsupervised Ensemble Semantic Segmentation for Foreground-Background Separation on Satellite Image
    Tarry, Jaelen
    Dong, Xishuang
    Li, Xiangfang
    Qian, Lijun
    Chance, Leah
    Morrone, Philip
    18TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, ICSC 2024, 2024, : 212 - 217
  • [49] Improved Foreground-Background Segmentation using Dempster-Shafer Fusion
    Moro, Alessandro
    Mumolo, Enzo
    Nolich, Massimiliano
    Terabayashi, Kenji
    Umeda, Kazunori
    2013 8TH INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS (ISPA), 2013, : 72 - 77
  • [50] Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs
    Heess, Nicolas
    Le Roux, Nicolas
    Winn, John
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT II, 2011, 6792 : 9 - +