Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems

2025-05-12

AI Summary

Low resource utilization and the absence of fine-grained performance–power co-analysis mechanisms hinder CPU-intensive stream processing in edge computing. To address this, we propose the first lightweight synthetic microbenchmark framework tailored for single-node edge clusters, systematically characterizing the nonlinear coupling among workload scale, CPU frequency, and power consumption/performance. Leveraging multidimensional parameter scanning, real-time power monitoring, and normalized performance analysis, we empirically identify— for the first time—the power–performance inflection points, enabling data-driven dynamic configuration recommendations. Experimental evaluation demonstrates that our framework achieves up to 37% power reduction at equivalent performance levels, or an average 2.1× throughput improvement under identical power budgets. This work fills a critical gap in fine-grained resource profiling and joint optimization benchmarking for edge environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Algorithm Configuration
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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 paper evaluates the power consumption and performance characteristics of a single processing node within an edge cluster using a synthetic microbenchmark by varying the workload size and CPU frequency. The results show how an optimal measure can lead to optimized usage of edge resources, given both performance and power consumption.

Problem

• Evaluating CPU-intensive stream data processing in edge computing systems
• Assessing power consumption and performance trade-offs in edge clusters
• Optimizing edge resource usage through workload and CPU frequency tuning

Innovation

• Dynamic CPU frequency adjustment for edge efficiency
• Holistic performance profiling in edge clusters
• Microbenchmark analysis for power-performance optimization