Network slicing has become a fundamental property for next-generation networks, especially because an inherent part of 5G standardisation is the ability for service providers to migrate some or all of their network services to a virtual network infrastructure, thereby reducing both capital and operational costs. With network function virtualisation (NFV), network functions (NFs) such as firewalls, traffic load balancers, content filters, and intrusion detection systems (IDS) are either instantiated on virtual machines (VMs) or lightweight containers, often chained together to create a service function chain (SFC). In this work, we review the state-of-the-art NFV and SFC implementation frameworks and present a taxonomy of the current proposals. Our taxonomy comprises three major categories based on the primary objectives of each of the surveyed frameworks: (1) resource allocation and service orchestration, (2) performance tuning, and (3) resilience and fault recovery. We also identify some key open research challenges that require further exploration by the research community to achieve scalable, resilient, and high-performance NFV/SFC deployments in next-generation networks.
As the adoption of softwarized network functions (NFs) keeps growing, we evaluate the performance benefits of SDN-aware data-plane implementations when compared to diverse acceleration and process-based NFV frameworks. Typical network functions have been implemented using four alternative frameworks scenarios, an SDN-aware software switch (dataplane), a virtual machine (VM), a Data-Plane Development Kit (DPDK) NF, and a containerized NF. Results from our experiments show that the data-plane NF implementation yields much higher bandwidth and packets per second (pps) rates. The bandwidth obtained is 14% more than the user-space scenario while retaining CPU utilization. The DPDK NFs in our evaluation can process packets at a much higher rate for 64B packets, on a single CPU core, which is 7 times higher than the containerized NF implementations, also tied to a single core. Our results also show the performance gains from deploying virtual network functions on heterogeneous frameworks.
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