Network function (NF) virtualization decouples NFs from the underlying middlebox hardware and promotes their deployment on virtualized network infrastructures. This essentially paves the way for the migration of NFs into clouds (i.e., NF-as-a-Service), achieving a drastic reduction of middlebox investment and operational costs for enterprises. In this context, service chains (expressing middlebox policies in the enterprise network) should be mapped onto datacenter networks, ensuring correctness, resource efficiency, as well as compliance with the provider's policy. The network service embedding (NSE) problem is further exacerbated by two challenging aspects: 1) traffic scaling caused by certain NFs (e.g., caches and WAN optimizers) and 2) NF location dependencies. Traffic scaling requires resource reservations different from the ones specified in the service chain, whereas NF location dependencies, in conjunction with the limited geographic footprint of NF providers (NFPs), raise the need for NSE across multiple NFPs. In this paper, we present a holistic solution to the multi-provider NSE problem. We decompose NSE into: 1) NF-graph partitioning performed by a centralized coordinator and 2) NF-subgraph mapping onto datacenter networks. We present linear programming formulations to derive near-optimal solutions for both problems. We address the challenging aspect of traffic scaling by introducing a new service model that supports demand transformations. We also define topology abstractions for NF-graph partitioning. Furthermore, we discuss the steps required to embed service chains across multiple NFPs, using our NSE orchestrator (Nestor). We perform an evaluation study of multi-provider NSE with emphasis on NF-graph partitioning optimizations tailored to the client and NFPs. Our evaluation results further uncover significant savings in terms of service cost and resource consumption due to the demand transformations. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works..