Edge Computing Performance Amplification

Edge Computing Performance Amplification

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)  
 
© 2023 by IJRES Journal
Volume-10 Issue-3
Year of Publication : 2023
Authors : Vivek Basavegowda Ramu
DOI : 10.14445/23497157/IJRES-V10I3P111

How to Cite?

Vivek Basavegowda Ramu, "Edge Computing Performance Amplification," International Journal of Recent Engineering Science, vol. 10, no. 3, pp. 69-76, 2023. Crossref, https://doi.org/10.14445/23497157/IJRES-V10I3P111

Abstract
Edge computing can be defined as an emerging technology that uses cloud computing to leverage edge data centers to process, store and analyze data close to the source. Traditional cloud computing architectures are not designed for latency-critical applications such as AI (Artificial Intelligence) and IoT (Internet of Things) because they rely on low data volumes generated by applications running near highly-populated areas. When volume grows beyond 50 miles from the population center, networks experience higher latency and packet loss rates which impacts application performance. Since everyone's life is equipped with more and more IoT devices by the day, decisions should be made in a split second in edge computing. It is really crucial to perform at an optimum level; some devices, especially medical wearables, deal with patient life, and any delay in decision-making will result in disaster. Similarly, modern-day autonomous self-driving vehicles where late decisions that can endup in accidents and really, there is no room for errors. This paper provides a new approach to improve the performance of edge computing by having two identical computing systems in which one system will act as primary and another as reserved or secondary. This system will be available in the local environment of the IoT device and not in the cloud. The secondary system will be reserved for mission-critical requests. Whenever the primary system breaches the latency threshold for the response, the request will be rerouted to the secondary system. Both systems will sync data in the background and can also serve as backup computing systems in case of any failure of one of the systems. Traditional edge computing systems will have a singular computing system on the device and low capability to process data or user requests since it still relies on transferring data to the cloud to compute and make decisions. With multiple high-capacity computing systems on the device and automatic rerouting and balancing of the requests, edge computing performance will be more reliable. It will also ensure high availability, low latency and a highly dependable edge computing architecture. This method is scalable; based on the volume and complexity, the architecture can be extended to additional sub-systems to add more computing power and to handle additional requests on demand. The proposed method of multiple computing systems at the local environment or device will result in a highly responsive system, provide the much needed support to process data or user requests in a fraction of a second, and result in life-saving decisions. This solution also opens the door to multiple possibilities like tagging multiple IoT devices to the same computing system, the ability to include AI (Artificial Intelligence)/ML (Machine Learning) models and processing locally, and learning from previous decisions to enhance future computing, self troubleshooting and healing process etc which will further advance the existing technology.

Keywords
Edge computing, Performance testing, Performance engineering, Cloud computing, Edge computing infrastructure, IoT.

Reference
[1] Mahadev Satyanarayanan, “The Emergence of Edge Computing,” Computer, vol. 50, no. 1, pp. 30-39, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[2] MSV Janakiram, Vapor I0 Wants to Turn Every Cell Phone Tower Into an Edge Location, Forbes, 2017. [Online]. Available:
https://www.forbes.com/sites/janakirammsv/2017/06/26/vapor-io-wants-to-turn-every-cell-phone-tower-into-an-edge-location/
[3] MSV Janakiram, Edge Computing - Redefining the Enterprise Infrastructure, Forbes, 2017. [Online]. Available: https://www.forbes.com/sites/janakirammsv/2017/02/07/edge-computing-redefining-the-enterprise-infrastructure/
[4] Gartner, Gartner's top 10 Strategic Technology Trends for 2019: Intelligent Edge, 2018. [Online]. Available: https://www.gartner.com/smarterwithgartner/gartners-top-10-strategic-technology-trends-for-2019/
[5] Mahadev Satyanarayanan et al., “The Case for VM-Based Cloudlets in Mobile Computing,” IEEE Pervasive Computing, vol. 8, no. 4, pp. 14-23, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] S. Li, L. Da Xu, and S. Zhao, “The Fog Computing Framework for Scalable Distributed Edge Computing,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 1505-1515, 2018.
[7] J. Zhang, K. Li, and K. Ota, “Edge Computing in the AIoT Era: A Survey,” IEEE Access, vol. 7, pp. 164230-164252, 2019.
[8] S. Yi et al., “Towards Smart City: A Survey on the Big Data Analytics for Context-Aware Urban Computing,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 1971-2009, 2020.
[9] Z. Shi et al., “Industrial Edge Computing In Industry 4.0: A Survey,” IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5647-5663, 2021.
[10] M. M. Fouad, M. Khedr, and S. S. El-Rahman, “Fog-Based Containerization for Efficient Management of IoT Applications,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 6, pp. 8825-8842, 2021.
[11] S. Vishnupriya, “Edge Computing Based IoT for Smart Cities,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 1, pp. 16-21, 2020.
[CrossRef] [Publisher Link]
[12] Yuyi Mao et al., “A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322-2358, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] S. Dey, N. Chaki, and I. Saha, “A Survey on Security Issues in Fog/Edge Computing,” Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 3, pp. 4003-4020, 2022.
[14] Edge Computing Market Size, Share & Trends Analysis Report by Component (Hardware, Software, Services, Edge-managed Platforms), By Application, By Industry Vertical, By Region, and Segment Forecasts, 2022 – 2030, Grand View Research, 2022. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/edge-computing-market
[15] Jianli Pan, and James McElhannon, “Future Edge Cloud and Edge Computing for Internet of Things Applications,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 439-449, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[16] S. Aiswarya et al., “Latency Reduction in Medical IoT Using Fuzzy Systems by Enabling Optimized Fog Computing,” SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 12, pp. 156-166, 2022.
[CrossRef] [Publisher Link]
[17] Blesson Varghese et al., “Challenges and Opportunities in Edge Computing,” IEEE International Conference on Smart Cloud(SmartCloud), pp. 20-26, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Gopika Premsankar, Mario Di Francesco, and Tarik Taleb, “Edge Computing for the Internet of Things: A Case Study,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1275-1284, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Weisong Shi et al., “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Wei Yu et al., “A Survey on the Edge Computing for the Internet of Things,” IEEE Access, vol. 6, pp. 6900-6919, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Najmul Hassan, Kok-Lim Alvin Yau, and Celimuge Wu, “Edge Computing in 5G: A Review,” IEEE Access, vol. 7, pp. 127276-127289, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Wazir Zada Khan et al., “Edge Computing: A Survey,” Future Generation Computer Systems, vol. 97, pp. 219-235, 2019.
[CrossRef] [Google Scholar] [Publisher Link]