在边缘计算中优化资源利用，通过调频与负载匹配提升能效
Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems
- 用微基准测试动态调节工作负载和CPU频率
- 发现性能与功耗的最优平衡点，提升资源利用率
- 适合关注边缘系统能效优化的研究者和工程师
边缘计算已成为关键技术，具有低延迟、增强数据安全性和减少对中心云依赖等优势，对实时数据处理或高安全要求的应用至关重要。然而，边缘集群中的设备常处于低利用率状态，主要因缺乏全局性能分析机制，难以根据工作负载动态调整系统配置。由于边缘环境涉及CPU频率、功耗与应用性能间的复杂相互作用，深入理解这些关系至关重要。本文通过改变工作负载大小和CPU频率，在单个处理节点上使用合成微基准测试，评估其功耗与性能特性。结果表明，通过识别最优配置，可在保障性能的同时实现边缘资源的高效利用，显著提升计算效率与节能效果。
原文摘要 · Abstract (English)
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.
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