<bold>A Pervasive Internet Approach to Fine-Grain Power-Aware Computing</bold>

被引:0
|
作者
Abukmail, A [1 ]
Helal, A
机构
[1] Univ Florida, Pervas Comp Lab, Comp & Informat Sci & Engn, Gainesville, FL 32611 USA
[2] Tsuyama Natl Coll Technol, Tsuyama, Okayama 7088509, Japan
关键词
power management; computation outsourcing; pervasive computing; smart spaces;
D O I
10.1109/SAINT.2006.5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We present a novel approach to conserve power in networked mobile devices. Our approach exploits communication within a pervasive smart space as an opportunity to save power as opposed to the classic view of communication as a drain on resources. We outsource intensive computations to the network whenever a pervasive connection to the Internet exists and when it pays off to do so. At compile-time our approach generates two versions of the program being compiled, a client version and a server version, each containing the necessary code to handle the run-time decision of executing code locally on the mobile device or remotely to the server based on power efficiency. We utilize a technique from Real-Time systems to help the compiler generate highly accurate code by calculating the number of loop iterations for each candidate section of code. This approach has the advantage of analyzing applications at a finer granularity than other similar methodologies. This is because the candidate code sections are CPU blocks represented mostly by loops. Our experimental results performed on Intel's XScale architecture and the Wi-Fi wireless technology show significant savings in power consumption by the mobile device.
引用
收藏
页码:109 / +
页数:3
相关论文
共 50 条
  • [41] A GPU Architecture Aware Fine-Grain Pruning Technique for Deep Neural Networks
    Choi, Kyusik
    Yang, Hoeseok
    EURO-PAR 2021: PARALLEL PROCESSING, 2021, 12820 : 217 - 231
  • [42] A power-aware middleware for location & context aware mobile apps with cloud computing interaction
    Perez-Torres, Rafael
    Torres-Huitzil, Cesar
    PROCEEDINGS OF THE 2012 WORLD CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGIES, 2012, : 691 - 696
  • [43] A Low-Power FPGA Based on Autonomous Fine-Grain Power Gating
    Ishihara, Shota
    Hariyama, Masanori
    Kameyama, Michitaka
    IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2011, 19 (08) : 1394 - 1406
  • [44] Generating Fine-Grain Multithreaded Applications Using a Multigrain Approach
    Arteaga, Jaime
    Zuckerman, Stephane
    Gao, Guang R.
    ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, 2017, 14 (04)
  • [45] Microwave sintering: A new approach to fine-grain tungsten - I
    Jain, M
    Skandan, G
    Martin, K
    Cho, K
    Klotz, B
    Dowding, R
    Kapoor, D
    Agrawal, D
    Cheng, JP
    INTERNATIONAL JOURNAL OF POWDER METALLURGY, 2006, 42 (02): : 45 - 50
  • [46] Optional and responsive fine-grain locking in Internet-based collaborative systems
    Sun, CZ
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2002, 13 (09) : 994 - 1008
  • [47] Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing
    Deng, Li
    Li, Yang
    Yao, Li
    Jin, Yu
    Gu, Jinguang
    MOBILE INFORMATION SYSTEMS, 2016, 2016
  • [48] Power-Aware Computing on GPGPU Systems Using ML Classification Techniques
    Al-Obaidy, Furat
    Mohammadi, Farah
    2022 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 22), 2022, : 1487 - 1491
  • [49] Developing power-aware scheduling mechanisms for computing systems virtualized by Xen
    Ren, Shenyuan
    He, Ligang
    Zhu, Huanzhou
    Gu, Zhuoer
    Song, Wei
    Shang, Jiandong
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2017, 29 (03):
  • [50] Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection
    Ghasemzadeh, Hassan
    Amini, Navid
    Saeedi, Ramyar
    Sarrafzadeh, Majid
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2015, 14 (04) : 800 - 812