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[2505.07755] Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems
Summary
This article evaluates CPU-intensive stream data processing in edge computing systems, highlighting performance and power consumption optimization through benchmarking.
Why It Matters
As edge computing becomes increasingly vital for real-time data processing and security, understanding its performance dynamics is crucial. This research addresses inefficiencies in edge device utilization by providing insights into optimizing resource usage, which can enhance both performance and energy efficiency in various applications.
Key Takeaways
- Edge computing offers low latency and enhanced security for real-time applications.
- Underutilization of edge devices can be mitigated through effective performance profiling.
- Optimizing CPU frequency and workload size can improve both performance and power efficiency.
- Understanding the interplay between CPU performance and power consumption is essential for resource management.
- The research provides a framework for benchmarking that can inform future edge computing strategies.
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2505.07755 (cs) [Submitted on 12 May 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems Authors:Tomasz Szydlo, Viacheslaw Horbanov, Devki Nandan Jha, Shashikant Ilager, Aleksander Slominski, Rajiv Ranjan View a PDF of the paper titled Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems, by Tomasz Szydlo and 5 other authors View PDF HTML (experimental) Abstract:Edge computing has emerged as a pivotal technology, offering significant advantages such as low latency, enhanced data security, and reduced reliance on centralized cloud infrastructure. These benefits are crucial for applications requiring real-time data processing or strict security measures. Despite these advantages, edge devices operating within edge clusters are often underutilized. This inefficiency is mainly due to the absence of a holistic performance profiling mechanism which can help dynamically adjust the desired system configuration for a given workload. Since edge computing environments involve a complex interplay between CPU frequency, power consumption, and application performance, a deeper understanding of these correlations is essential. By uncovering these relationships, it becomes possible to make informed decisions that enhance both computational efficiency and energy savings. To address this gap, this pap...
