Date of Award

2024

Type

Thesis

Major

Master of Science

Degree Type

Applied Computer Science

Department

TSYS School of Computer Science

First Advisor

Yi Zhou

Second Advisor

Rania Hodhod

Third Advisor

Lixin Wang

Abstract

As artificial intelligence (AI) applications become more common on the edge of networks, like Raspberry Pi servers, it is crucial to optimize their energy use. This research project investigates how AI algorithms affect energy efficiency and resource usage on Raspberry Pi servers. Two models were created: one predicts resource usage, and the other predicts power consumption of AI algorithms on Raspberry Pi. Several factors are considered like CPU and memory use, algorithm speed, dataset size, and types of algorithms and datasets. Using regression-based methods, we model how these factors affect energy use. By converting categorical factors into numerical ones, we develop models that describe the relationship between factors and energy use on Raspberry Pi. This research contributes practical tools that empower developers to assess the energy impact of AI deployments on edge servers, offering unique insights that are not readily available through solely profiling-based approaches. Our work facilitates scheduling AI applications on edge servers for energy efficiency without compromising performance.

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