Аннотации:
Resource-intensive applications have created a growing demand for efficient computation offloading in edge computing.
This project investigates schedulers for energy efficiency in edge computing, utilizing a PicoCluster 20H with 20 Jetson
Nano devices as the edge infrastructure. The system uses the RAPID and COSCO frameworks for container
orchestration and task scheduling while incorporating a custom workload based on real-time intermediate flow
estimation (RIFE) model. Five different schedulers, including algorithmic (ROS, MAD-MC, LR-MMT) and machine
learning (ML)-based (GOBI, GOSH) approaches, were implemented and evaluated.
The results demonstrate that ML-based schedulers, specifically GOBI and GOSH, achieve superior energy efficiency
compared to algorithmic methods, highlighting the potential of deep learning (DL) for optimizing resource allocation in
edge computing. We were also successful at setting up and executing custom workloads with the corresponding
schedulers on our cluster. Future work will focus on covering a larger selection of schedulers and novel methods of DL
applications in scheduling algorithms.