A Framework for Seamless Offloading in IoT Applications using Edge and Cloud Computing

被引:0
|
作者
Welgama, Himesh [1 ]
Lee, Kevin [1 ]
Kua, Jonathan [1 ]
机构
[1] Deakin Univ, Sch Informat Technol, Geelong, Vic 3220, Australia
关键词
Edge; Cloud; Docker; IoT; Offloading; THINGS;
D O I
10.5220/0011107500003194
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Typical Internet of Things (IoT) deployments are resource-constrained, with limited computation and storage, high network latency, and low bandwidth. The introduction of Edge and Cloud computing provides a method of mitigating these shortfalls. This paper proposes a framework for structuring IoT applications to allow for seamless offloading (based on CPU load) of work from IoT nodes to Edge and Cloud computing resources. The proposed flexible framework utilises software to orchestrate multiple containerised IoT applications for optimal performance within available computational resources. Edge and Cloud servers co-operate autonomously to determine the appropriate resource allocation based on the requirements of running IoT applications in real-time. The result is a framework that is suited to perform with heterogeneous IoT hardware while improving overall computational performance, latency and bandwidth relative to IoT architectures that do not auto-scale. This framework is evaluated using an experimental setup with multiple IoT nodes, Edge nodes and Cloud computing resources. It demonstrates the approach is viable and results in a flexible and scalable IoT solution.
引用
收藏
页码:289 / 296
页数:8
相关论文
共 50 条
  • [1] Collaborative offloading decision policy framework in IoT using edge computing
    Shirke, Archana
    Chandane, M. M.
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2023,
  • [2] Task partitioning and offloading in IoT cloud-edge collaborative computing framework: a survey
    Chen, Haiming
    Qin, Wei
    Wang, Lei
    [J]. JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2022, 11 (01):
  • [3] Task partitioning and offloading in IoT cloud-edge collaborative computing framework: a survey
    Haiming Chen
    Wei Qin
    Lei Wang
    [J]. Journal of Cloud Computing, 11
  • [4] Mobile Offloading Framework: Solution for Optimizing Mobile Applications Using Cloud Computing
    Krawczyk, Henryk
    Nykiel, Michal
    Proficz, Jerzy
    [J]. COMPUTER NETWORKS, CN 2015, 2015, 522 : 293 - 305
  • [5] Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing
    Yousafzai, Abdullah
    Yaqoob, Ibrar
    Imran, Muhammad
    Gani, Abdullah
    Md Noor, Rafidah
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (05): : 4171 - 4182
  • [6] DisCO: A Distributed and Concurrent Offloading Framework for Mobile Edge Cloud Computing
    Kwangman, K. O.
    Son, Yunsik
    Kim, Soongohn
    Lee, Yangsun
    [J]. 2017 NINTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2017), 2017, : 763 - 766
  • [7] An adaptive offloading framework for Android applications in mobile edge computing
    Xing CHEN
    Shihong CHEN
    Yun MA
    Bichun LIU
    Ying ZHANG
    Gang HUANG
    [J]. Science China(Information Sciences), 2019, 62 (08) : 114 - 130
  • [8] An adaptive offloading framework for Android applications in mobile edge computing
    Xing Chen
    Shihong Chen
    Yun Ma
    Bichun Liu
    Ying Zhang
    Gang Huang
    [J]. Science China Information Sciences, 2019, 62
  • [9] An adaptive offloading framework for Android applications in mobile edge computing
    Chen, Xing
    Chen, Shihong
    Ma, Yun
    Liu, Bichun
    Zhang, Ying
    Huang, Gang
    [J]. SCIENCE CHINA-INFORMATION SCIENCES, 2019, 62 (08)
  • [10] Cloud edge computing in the IoT
    Fajjari, Ilhem
    Tobagi, Fouad
    Takahashi, Yutaka
    [J]. ANNALS OF TELECOMMUNICATIONS, 2018, 73 (7-8) : 413 - 414