  For a network slice that spans multiple technology and/or administrative
domains, these domains must ensure that the slice's End-to-End (E2E) Service
Level Agreement (SLA) is met. Thus, the E2E SLA should be decomposed to partial
SLAs, assigned to each of these domains. Assuming a two level management
architecture consisting of an E2E service orchestrator and local domain
controllers, we consider that the former is only aware of historical data of
the local controllers' responses to previous slice requests, and captures this
knowledge in a risk model per domain. In this study, we propose the use of
Neural Network (NN) based risk models, using such historical data, to decompose
the E2E SLA. Specifically, we introduce models that incorporate monotonicity,
applicable even in cases involving small datasets. An empirical study on a
synthetic multidomain dataset demonstrates the efficiency of our approach.
