Abstract-The ever-increasing need to diversify the Internet has recently revived the interest in network virtualization. Network virtualization carries a significant benefit to Service Providers, as it enables the deployment of network services within customized virtual networks (VN) that offer performance and reliability guarantees. VN embedding across multiple substrate providers creates the need for a layer of indirection which is fulfilled by VN Providers. VN Providers are expected to have very limited knowledge of the physical infrastructure, since substrate providers do not disclose their network topology and resource information. This entails significant implications on resource discovery and assignment.In this paper, we study the challenging problem of multidomain VN embedding with limited information disclosure (LID). In this context, we discuss the visibility of VN Providers on substrate network resources and question the suitability of topologies for VN request specifications. Our main contributions are as follows: (i) we present a traffic matrix based VN embedding framework that enables VN request partitioning under LID, and (ii) we conduct a feasibility study on VN embedding with LID compared to a "best-case" scenario where all information is available to VN Providers.

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Abstract-Network Function Virtualization (NFV) decouples network functions (NF) 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: (i) traffic scaling caused by certain NFs (e.g., caches, WAN optimizers) and (ii) 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 multiprovider NSE problem. We decompose NSE into (i) NF-graph partitioning performed by a centralized coordinator and (ii) 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.

Network Functions Virtualization is focused on\ud migrating traditional hardware-based network functions to\ud software-based appliances running on standard high volume\ud severs. There are a variety of challenges facing early adopters of\ud Network Function Virtualizations; key among them are resource\ud and service mapping, to support virtual network function orchestration.\ud Service providers need efficient and effective mapping\ud capabilities to optimally deploy network services. This paper\ud describes TeNOR, a micro-service based network function virtualisation\ud orchestrator capable of effectively addressing resource\ud and network service mapping. The functional architecture and\ud data models of TeNOR are described, as well as two proposed\ud approaches to address the resource mapping problem. Key\ud evaluation results are discussed and an assessment of the mapping\ud approaches is performed in terms of the service acceptance ratio\ud and scalability of the proposed approaches

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