Performance Unveiled: Comparing Lightweight Devices Testbed and Virtual Machines for Edge Computing

作者：Faiza Akram, Andrew Zheng, April Guo-Yue, Cooper Medved, Claire E. Johnson, Asad Waqar Malik, Samee U. Khan · 年份：2025 · DOI：10.18260/1-2--54186 · 被引用次数：6 · 研究领域：Cloud Computing and Resource Management、IoT and Edge/Fog Computing

Technological innovations are accelerating across fields like engineering, IT, environmental science, and agriculture, the convergence of education & research has emerged as a vital and concerning issue.Although the research in areas such as edge computing holds a lot of potential for real-world applications, its integration into engineering education remains marginalized due to lack of curriculum alignment, lack of resources for faculty training, and industry-academia disconnect.This study bridges the gap by investigating the suitability of hands-on experimentation with edge computing frameworks to enhance learning outcomes for engineering students.To create an engaging and inclusive workplace environment for students with cultural diversity, undergraduate students from different backgrounds were selected as a part of NSF funded collaborative program, iEDGE.With different engaging sites, students gained firsthand experience of practical implementation.For one of the sites we designed experiments with Apache Storm, a distributed stream processing framework on virtual machines and Raspberry Pi testbeds, and tested it under varying workloads.These experiments highlighted the performance impact on resource-constrained devices, highlighting the importance of hardware and software optimization in real-world IoT applications.We aim to encourage critical thinking and problemsolving capabilities by incorporating this research experience into engineering education.Our goal is to enabl...