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

TL;DR

This paper evaluates CPU performance and power consumption in edge computing nodes using microbenchmarking to identify optimal configurations for efficiency and energy savings.

Contribution

It provides a comprehensive analysis of CPU performance and power trade-offs in edge nodes, aiding dynamic system configuration decisions.

Findings

Optimal CPU frequency settings improve energy efficiency.

Workload size significantly impacts power and performance.

Profiling mechanisms can enhance resource utilization in edge systems.

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.