Serverless edge intelligence for SLA-aware function placement and resource slicing in wireless networks

Abstract

Wireless edge networks must support delay-sensitive services under fluctuating traffic, interference, heterogeneous SLA priorities, and limited radio, computing, and memory resources. This paper proposes Serverless-PRONTO, an SLA-aware orchestration framework that jointly controls serverless function placement, task offloading, bandwidth allocation, CPU slicing, and warm-instance management. The system model captures uplink and downlink transmission, queueing, execution, cold-start initialization, and memory occupied by retained function instances. To solve the resulting dynamic, partially observable, mixed discrete-continuous problem, Serverless-PRONTO combines a physics-aware sparse graph encoder with multi-agent TD3 under centralized training and decentralized execution. The encoder represents inter-node coupling through channel, interference, distance, queue, resource, function-demand, and cold-start features, while top- \(\:K\) attention limits signaling. An SLA-risk mechanism prioritizes requests according to urgency, queue state, service class, and cold-start probability. A sequential feasibility projection converts raw actor outputs into valid placement, bandwidth, CPU, and memory decisions. Simulations against four serverless and edge-orchestration baselines under varying traffic loads and network sizes show higher SLA satisfaction, lower end-to-end delay and cold-start ratio, and more stable scalability, demonstrating the benefit of jointly coordinating wireless resources and serverless runtime states.