A multi-objective learning framework for secure serverless scheduling with GNN and RL

Serverless computing promises elastic, fine-grained execution in the cloud, but production systems still rely on heuristic schedulers that are oblivious to workload structure, leading to suboptimal performance and unnecessary cost. The original Dandelion system introduced a pure-function execution architecture that separates computation from I/O to reduce overhead, but its scheduler remains heuristic and workload-agnostic. In this paper, we present Dandelion-Learn, a learning-enhanced scheduling extension to the Dandelion architecture that treats function orchestration as a machine learning problem. Dandelion-Learn combines three components: (i) a graph neural network (GNN) that predicts per-function runtime and resource usage, (ii) a reinforcement learning (RL) agent that makes online placement and scaling decisions, and (iii) a multi-objective optimizer that balances latency, energy, and cost. Across Azure Functions traces and synthetic DAG workloads evaluated under a single unified SimPy configuration, Dandelion-Learn achieves a p99 latency of 50.09 ms compared with 51.36 ms for FIFO (about 2.5% lower), while throughput remains essentially unchanged at about 82.67 jobs/s. Relative to FIFO, energy per job decreases from 41.15 to 21.82 (about 47.0%) and cost per job from 38.06 to 21.29 (about 44.1%), and the cold-start rate falls from 0.871 to 0.446. CHERI-style compartmentalization of the learning components is treated as an architectural design choice and is modeled at about 8% CPU overhead; it is not measured on CHERI hardware. Overall, these results indicate that learning-enhanced multi-objective scheduling can improve energy, cost, and cold-start behavior while remaining competitive on tail latency in controlled simulation.