OTL: On-demand thread stack allocation scheme for real-time sensor operating systems

被引:0
|
作者
Yi, Sangho [1 ]
Lee, Seungwoo [1 ]
Cho, Yookun [1 ]
Hong, Jiman [2 ]
机构
[1] Seoul Natl Univ, Sch Comp Sci & Engn, Syst Software Res Lab, Seoul, South Korea
[2] Soongsil Univ, Sch Comp, Seoul, South Korea
关键词
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In wireless sensor networks, each sensor node has severe resource constraints in terms of energy, computing device, and memory space. Especially, the memory space of the platform hardware is much smaller than that of the other computing systems. In this paper, we propose a OTL, which is an on-demand thread stack allocation scheme for MMU-less real-time sensor operating systems. The OTL enables to adaptively adjust the stack size by allocating stack frame based on the amount of each function's stack usage. The amount of the function's stack usage is checked at compile-time, and the adaptive adjustment of the stack occurs at run-time. Our experimental results show that the OTL significantly minimizes the spatial overhead of the threads' stacks with tolerable time overhead compared with fixed stack allocation mechanism of the existing sensor operating systems.
引用
收藏
页码:905 / +
页数:2
相关论文
共 50 条
  • [31] RTID: On-demand real-time data processing for IoT network
    Rahman, Muhammad Saifur
    Das, Rohit Kumar
    MATERIALS TODAY-PROCEEDINGS, 2022, 62 : 4721 - 4725
  • [32] On-demand forecasting of stock prices using a real-time predictor
    Wang, YF
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2003, 15 (04) : 1033 - 1037
  • [33] Roda: A Flexible Framework for Real-Time On-demand Data Aggregation
    Xu, Jiawei
    Zhu, Weidong
    Qian, Shiyou
    Xue, Guangtao
    Cao, Jian
    Zhu, Yanmin
    Zhu, Zongyao
    Zhu, Junwei
    ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2020, PT II, 2020, 12453 : 587 - 602
  • [34] Knowledge Sharing Live Streams: Real-time and On-demand Engagement
    Gravina Fonseca, Leonardo Mariano
    Junqueira Barbosa, Simone Diniz
    ICEIS: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 2, 2021, : 441 - 450
  • [35] Real-Time Gas Quality Data for On-Demand Production of Biogas
    Bierer, Benedikt
    Naegele, Hans-Joachim
    Ortiz Perez, Alvaro
    Woellenstein, Juergen
    Kress, Philipp
    Lemmer, Andreas
    Palzer, Stefan
    CHEMICAL ENGINEERING & TECHNOLOGY, 2018, 41 (04) : 696 - 701
  • [36] Scheduling real-time requests in on-demand data broadcast environments
    Lee, Victor C. S.
    Wu, Xiao
    Ng, Joseph Kee-Yin
    REAL-TIME SYSTEMS, 2006, 34 (02) : 83 - 99
  • [37] WHATS REAL IN REAL-TIME OPERATING-SYSTEMS
    CHILD, J
    COMPUTER DESIGN, 1992, 31 (06): : 107 - +
  • [38] Real-Time On-Demand Design of Circuit-Analog Plasmonic Stack Metamaterials by Divide-and-Conquer Deep Learning
    Xiong, Jiankai
    Shen, Jiaqing
    Gao, Yuan
    Chen, Yingshi
    Ou, Jun-Yu
    Liu, Qing Huo
    Zhu, Jinfeng
    LASER & PHOTONICS REVIEWS, 2023, 17 (03)
  • [39] G-Quadruplex-Functionalized Gold Nanoparticles for a Real-Time Biomolecule Sensor with On-Demand Tunable Properties
    Chuaychob, Surachada
    Fujita, Masahiro
    Maeda, Mizuo
    LANGMUIR, 2022, 38 (16) : 4870 - 4878
  • [40] Real-time On-Demand Multi-Hop Audio Streaming with Low-Resource Sensor Motes
    Pham, Congduc
    Cousin, Philippe
    Carer, Arnaud
    2014 IEEE 39TH CONFERENCE ON LOCAL COMPUTER NETWORKS WORKSHOPS (LCN WORKSHOPS), 2014, : 539 - 543