UC-31 An Empirical Study of Thermal Attacks on Edge Platforms

Cloud-edge systems are vulnerable to thermal attacks as the increased energy consumption may remain undetected, while occurring alongside normal, CPU-intensive applications. The purpose of our research is to study thermal effects on modern edge systems. We also analyze how performance is affected from the increased heat and identify preventative measures. We speculate that due to the technology being a recent innovation, research on cloud-edge devices and thermal attacks is scarce. Other research focuses on server systems rather than edge platforms. In our paper, we use a Raspberry Pi 4 and a CPU-intensive application to represent thermal attacks on cloud-edge systems. We performed several experiments with the Raspberry Pi 4 and used stress-ng, a benchmarking tool available on Linux distributions, to simulate the attacks. The resulting effects displayed drastic increases in the temperature and power consumption. The key impact of our research is to highlight the following risks and mitigation plans: the vulnerability of cloud-edge systems from thermal attacks, the capability for the attacks to go unnoticed, to further the understanding of edge devices as well as the prevention of these attacks.Advisors(s): Dr. Kun SuoTopic(s): Securit

An Empirical Study of Artificial Intelligence Performance on Edge Devices

Artificial intelligence (AI) workloads have changed the computing paradigm from cloud services to mobile applications. However, there lacks an in-depth analysis of their advantages, limitations, performance and resource consumptions in an edge environment. In this work, we perform a comprehensive study of representative AI workloads on edge computing. We first conduct a summary of modern edge hardware and popular AI workloads. Then we quantitatively evaluate the AI applications in realistic edge environments based on Raspberry Pi, Nvidia TX2, etc. Our experiments show that performance variation and difference in resource footprint limit availability of certain types of workload. Our results could help user select the appropriate AI models or edge hardwares for their workloads and guide the optimization of existing AI scenarios