Engaging applications with diverse SLO requirements has become indispensable for production-scale LLM serving systems. However, existing systems rely on iteration-level scheduling, which enforces inflexible, unified execution across multi-SLO workloads, significantly constraining the serving efficiency.

Serverless computing is becoming prevalent to develop resource-demanding and delay-sensitive edge native applications across the edge and cloud. The unique pricing mechanism of serverless computing brings new opportunities to reduce the cost of edge native applications, by orchestrating function fusion and placement across the edge and cloud. However, function fusion potentially increases the latency of the serverless workflow. To navigate this performance-cost tradeoff, we present an online predictive function orchestration framework which leverages predictions to dynamically optimize the function fusion and placement. Preliminary evaluation results verify the efficacy of the proposed framework.