FEARLESS: A Federated Reinforcement Learning Orchestrator for Serverless Edge Swarms
Christos Sad, Dimosthenis Masouros, Kostas Siozios · IEEE Embedded Systems Letters · 2024
The rise of edge computing, characterized by swarms of edge devices, marks a significant shift in cloud-edge computing landscapes, moving data processing closer to the source of data generation. However, this paradigm introduces complexities in orchestration, as traditional centralized methods become inadequate for effectively managing distributed, dynamic edge environments. In this letter, we introduce FEARLESS, a distributed orchestration framework tailored for swarms of edge devices. FEARLESS employs a vertical federated reinforcement learning approach to efficiently orchestrate function invocation requests in serverless swarms. Experimental results demonstrate that FEARLESS significantly reduces the quality-of-service violations of the scheduled tasks by up to 57%, compared to a centralized “least-CPU-utilization” and a “local-execution” approach, while it also achieves approximately up to 20% average total energy reduction.