Improving IoT Analytics through Selective Edge Execution
This work addresses performance and resource limitations for IoT applications, but it is incremental as it builds on existing edge computing concepts with a specific optimization method.
The paper tackles the problem of resource-intensive machine learning analytics on IoT devices by proposing a selective edge execution algorithm that predicts when to offload tasks to cloudlets, resulting in improved performance and adaptation to service requirements.
A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes network resources. However, due to power and computing limitations, the devices often cannot support such resource-intensive routines and fail to accurately execute the analytics. In this work, we propose to improve the performance of analytics by leveraging edge infrastructure. We devise an algorithm that enables the IoT devices to execute their routines locally; and then outsource them to cloudlet servers, only if they predict they will gain a significant performance improvement. It uses an approximate dual subgradient method, making minimal assumptions about the statistical properties of the system's parameters. Our analysis demonstrates that our proposed algorithm can intelligently leverage the cloudlet, adapting to the service requirements.