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.

翻译：边缘计算已成为一项关键技术，具有低延迟、增强数据安全性和减少对集中式云基础设施依赖等显著优势。这些优势对于需要实时数据处理或严格安全措施的应用至关重要。尽管存在这些优点，边缘集群内运行的边缘设备通常未得到充分利用。这种低效主要源于缺乏一种整体性能剖析机制，该机制能够帮助针对给定工作负载动态调整所需的系统配置。由于边缘计算环境涉及CPU频率、功耗和应用性能之间复杂的相互作用，深入理解这些相关性至关重要。通过揭示这些关系，可以做出明智决策，从而同时提高计算效率和节约能源。为弥补这一空白，本文通过改变工作负载大小和CPU频率，使用合成微基准测试评估了边缘集群内单个处理节点的功耗和性能特征。结果表明，在兼顾性能和功耗的情况下，优化措施能够实现边缘资源的高效利用。