Efficient Edge Computing: A Survey of High-Throughput Concurrent Processing Strategies for Graph Data


  • Weirong Xiu
  • Md Gapar Md Johar
  • Mohammed Hazim Alkawaz
  • Chen Bian




Edge computing, Graph data, High throughput, Concurrent processing


This paper reviews the strategies for high-throughput concurrent processing of graph data in edge computing environments. As information technology rapidly advances, particularly in areas such as the Internet of Things (IoT), smart cities, and autonomous driving, the need for real-time and efficient data processing continues to grow. Edge computing is a distributed computing paradigm that can process data at or near its origin, thereby reducing network latency, improving application performance, and reducing the load on central data centers. The discussion includes the application of edge computing in graph data processing, highlighting parallel computing models such as the Bulk Synchronous Parallel (BSP) model and other parallel optimization techniques like data partitioning and task scheduling. The paper also addresses specific challenges of parallel processing in edge computing, such as resource constraints, data communication delays, and strategies for security and privacy protection. Despite the significant potential edge computing has demonstrated in processing graph data, numerous challenges remain. Future research directions involve the development of new resource optimization algorithms, low-latency communication protocols, and technologies to enhance data security, aiming to achieve more efficient and secure graph data processing. This paper provides clear directions and a foundation for the efficient implementation of graph data processing in edge computing environments, positioning edge computing to play an increasingly significant role in real-time data analysis and intelligent applications.


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How to Cite

Xiu, W., Johar, M. G. M., Alkawaz, M. H., & Bian, C. (2024). Efficient Edge Computing: A Survey of High-Throughput Concurrent Processing Strategies for Graph Data. Journal of Computing and Electronic Information Management, 12(3), 101-106. https://doi.org/10.54097/

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