Load Balancing Mechanism for Edge-Cloud-Based Priorities Containers

Load Balancing Mechanism for Edge-Cloud-Based Priorities Containers

Автор: Wael Hadeed, Dhuha Basheer Abdullah

Журнал: International Journal of Wireless and Microwave Technologies @ijwmt

Статья в выпуске: 5 Vol.12, 2022 года.

Бесплатный доступ

Considering edge devices have limited resources, it's critical to keep track of their current resource usage and device resource allocation algorithms that send containers to edge and cloud nodes according to their priority. To minimize the strain on edge devices and enable the running of mission-critical applications, edge containers may need to be transferred to a cloud platform. In this paper, we suggested a mechanism for prioritizing container balance between the edge and cloud while attempting to assign delay-sensitive containers to edge nodes. We assess the performance of Docker container management systems on resource-constrained computers and offer ways for reducing administration and migration overhead based on the workload type, to bring load balance to the systems. The proposed algorithm gives flexibility to get the best possible ways to achieve load balancing. The Tensorflow’s object discovery API was used, accessed with Flask, Python's micro-web framework. Docker container management technology was used in the implementation of this application.

Еще

Container, Docker, Edge computing, Load Balance, Resource Management

Короткий адрес: https://sciup.org/15019184

IDR: 15019184   |   DOI: 10.5815/ijwmt.2022.05.01

Список литературы Load Balancing Mechanism for Edge-Cloud-Based Priorities Containers

  • F. A. Salaht, F. Desprez, and A. Lebre, “An Overview of Service Placement Problem in Fog and Edge Computing,” ACM Comput. Surv., vol. 53, no. 3, 2020, doi: 10.1145/3391196.
  • M. Planeta, J. Bierbaum, L. S. D. Antony, T. Hoefler, and H. Härtig, “MigrOS: Transparent live-migration support for containerised RDMA applications,” 2021 USENIX Annu. Tech. Conf., pp. 47–63, 2021.
  • S. P. Adithela, S. Marru, M. Christie, and M. Pierce, “Django content management system evaluation and integration with apache airavata,” ACM Int. Conf. Proceeding Ser., no. 2, pp. 3–6, 2018, doi: 10.1145/3219104.3229272.
  • S. Maheshwari, S. Choudhury, I. Seskar, and D. Raychaudhuri, “Traffic-Aware Dynamic Container Migration for Real-Time Support in Mobile Edge Clouds,” Int. Symp. Adv. Networks Telecommun. Syst. ANTS, vol. 2018-Decem, no. December, 2018, doi: 10.1109/ANTS.2018.8710163.
  • A. Machen, S. Wang, K. K. Leung, B. J. Ko, and T. Salonidis, “Live Service Migration in Mobile Edge Clouds,” IEEE Wirel. Commun., vol. 25, no. 1, pp. 140–147, 2018, doi: 10.1109/MWC.2017.1700011.
  • S. Guo, K. Zhang, B. Gong, W. He, and X. Qiu, “A delay-sensitive resource allocation algorithm for container cluster in edge computing environment,” Comput. Commun., vol. 170, no. January, pp. 144–150, 2021, doi: 10.1016/j.comcom.2021.01.020.
  • D. R. Vasconcelos, R. M. C. Andrade, V. Severino, and J. N. De Souza, “Cloud, Fog, or Mist in IoT? That is the qestion,” ACM Trans. Internet Technol., vol. 19, no. 2, 2019, doi: 10.1145/3309709.
  • T. Navigation and E. Technologies, “P Ort E Fficiency and T Rade,” no. November, pp. 48–56, 2017.
  • Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, “Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing,” Proc. IEEE, pp. 1–25, 2019, doi: 10.1109/JPROC.2019.2918951.
  • T. Rausch, A. Rashed, and S. Dustdar, “Optimized container scheduling for data-intensive serverless edge computing,” Futur. Gener. Comput. Syst., vol. 114, pp. 259–271, 2021, doi: 10.1016/j.future.2020.07.017.
  • O. Smimite and K. Afdel, “Containers Placement and Migration on Cloud System,” Int. J. Comput. Appl., vol. 176, no. 35, pp. 9–18, 2020, doi: 10.5120/ijca2020920493.
  • S. Ketu and P. K. Mishra, “Cloud, Fog and Mist Computing in IoT: An Indication of Emerging Opportunities,” IETE Tech. Rev. (Institution Electron. Telecommun. Eng. India), vol. 0, no. 0, pp. 1–12, 2021, doi: 10.1080/02564602.2021.1898482.
  • S. Maheshwari, P. Netalkar, and D. Raychaudhuri, “DISCO : Distributed Control Plane Architecture for Resource Sharing in Heterogeneous Mobile Edge Cloud Scenarios,” pp. 519–529, 2020.
  • J. Barthélemy, N. Verstaevel, H. Forehead, and P. Perez, “Edge-computing video analytics for real-time traffic monitoring in a smart city,” Sensors (Switzerland), vol. 19, no. 9, 2019, doi: 10.3390/s19092048.
  • K. Govindaraj and A. Artemenko, “Container Live Migration for Latency Critical Industrial Applications on Edge Computing,” IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, vol. 2018-Septe, no. iii, pp. 83–90, 2018, doi: 10.1109/ETFA.2018.8502659.
  • A. Elgazar and K. Harras, “Teddybear: Enabling efficient seamless container migration in user-owned edge platforms,” Proc. Int. Conf. Cloud Comput. Technol. Sci. CloudCom, vol. 2019-Decem, no. Section 2, pp. 70–77, 2019, doi: 10.1109/CloudCom.2019.00022.
  • Z. Benomar, F. Longo, G. Merlino, and A. Puliafito, “Cloud-based Enabling Mechanisms for Container Deployment and Migration at the Network Edge,” ACM Trans. Internet Technol., vol. 20, no. 3, 2020, doi: 10.1145/3380955.
  • S. A. Bello et al., “Cloud computing in construction industry: Use cases, benefits and challenges,” Autom. Constr., vol. 122, p. 103441, 2021, doi: 10.1016/j.autcon.2020.103441.
  • S. B. Melhem, A. Agarwal, N. Goel, and M. Zaman, “A markov-based prediction model for host load detection in live VM migration,” Proc. - 2017 IEEE 5th Int. Conf. Futur. Internet Things Cloud, FiCloud 2017, vol. 2017-Janua, pp. 32–38, 2017, doi: 10.1109/FiCloud.2017.37.
  • S. V. N. Kotikalapudi, “Comparing Live Migration between Linux Containers and Kernel Virtual Machine : Investigation study in terms of parameters,” no. February, p. 42, 2017, [Online]. Available: www.bth.se.
  • A. Dhumal and D. Janakiram, “C-Balancer: A System for Container Profiling and Scheduling,” pp. 1–10, 2020, [Online]. Available: http://arxiv.org/abs/2009.08912.
  • L. Deshpande and K. Liu, “Edge computing embedded platform with container migration,” 2017 IEEE SmartWorld Ubiquitous Intell. Comput. Adv. Trust. Comput. Scalable Comput. Commun. Cloud Big Data Comput. Internet People Smart City Innov. SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - , pp. 1–6, 2018, doi: 10.1109/UIC-ATC.2017.8397578.
  • O. Oleghe, “Container Placement and Migration in Edge Computing: Concept and Scheduling Models,” IEEE Access, vol. 9, pp. 68028–68043, 2021, doi: 10.1109/ACCESS.2021.3077550.
  • A. Barbalace, M. L. Karaoui, W. Wang, T. Xing, P. Olivier, and B. Ravindran, “Edge computing: The case for heterogeneous-ISA container migration,” VEE 2020 - Proc. 16th ACM SIGPLAN/SIGOPS Int. Conf. Virtual Exec. Environ., pp. 73–87, 2020, doi: 10.1145/3381052.3381321.
  • A. Machen, S. Wang, K. K. Leung, B. J. Ko, and T. Salonidis, “Live Service Migration in Mobile Edge Clouds,” IEEE Wirel. Commun., vol. 25, no. 1, pp. 140–147, 2018, doi: 10.1109/MWC.2017.1700011.
  • S. Maheshwari, P. Netalkar, and D. Raychaudhuri, “DISCO : Distributed Control Plane Architecture for Resource Sharing in Heterogeneous Mobile Edge Cloud Scenarios,” pp. 519–529, 2020.
  • Saxena, Shailesh, Mohammad Zubair Khan, and Ravendra Singh. "Green computing: an era of energy saving computing of cloud resources." Int. J. Math. Sci. Comput.(IJMSC) 7.2 (2021): 42-48.
  • Alakberov, Rashid G. "Clustering Method of Mobile Cloud Computing According to Technical Characteristics of Cloudlets." International Journal of Computer Network & Information Security 14.3 (2022).
  • Kaur, Amanpreet, et al. "Load balancing optimization based on deep learning approach in cloud environment." International Journal of Information Technology and Computer Science 12.3 (2020): 8-18.
  • Hadeed, Wael W., and Dhuha Basheer Abdullah. "Real-Time Based Big Data and E-Learning: A Survey and Open Research Issues." AL-Rafidain Journal of Computer Sciences and Mathematics 15.2 (2021): 225-243.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp