## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.2 Deepnfv Framework  
DeepNFV is a lightweight NFV framework proposed by [PERSON] et al. [80], which was designed specifically for edge network deployments, with the aim of minimising the packet processing tasks at the core of the network by offloading to edge network functions. DeepNFV is built on the GNF framework [19], which uses lightweight docker containers to build network functions for the edge. The key components of the proposed framework are the deep learning models employed and the infrastructure layer, which handles the interaction between network links and devices.  
DeepNFV uses deep learning to enhance tasks such as the optimisation of QoS parameters, classification of traffic, and analysis of network links. Similar to the GNF framework, DeepNFV was built to support the idea of moving network processing elements as close to the data source as possible (edge computing). As a use case for the DeepNFV framework, network traffic analysis functionality was considered by the authors by generating basic images from network traffic. A traffic analysis-containerized network function was used for the analysis and classification of images using deep learning models.  
To demonstrate the traffic analysis use case, the DeepNFV framework starts by splitting the received traffic into discrete components, which are stored as PCAP files, and the second step involves the modification of the packet headers to trim the header length or remove unimportant fields from the packets. The modified PCAP files are _cleaned_ to remove duplicates before being converted into image data. The resulting images are processed by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Framework** & **Problem** & **Solution(s)** & **Processing** & **Testbed/PoC** \\ \hline ClickOS [12] & Middlebox optimization & Optimized packet processing and Zen-based VM optimization. DPDK-enabled vNFs, containerised NFs, kernel by-pass packet & Click & Xen-based hypervisor \\ OpenNetVM [13] & VNF performance optimization & processing and line-rate packet processing. & Containers & DPDK-enabled hosts and containers \\ NetBricks [18] & NF performance optimization & Zero-copy isolation and memory level isolation. & VM & Physical servers \\ GNF [19] & Edge vNF deployments & Lightweight vNFs and edge service chains & CN & Host with CNs \\  & & Optimization of modular SFCs, effective resource allocation, resource-aware placement, vNF fault tolerance and optimized run-time scheduler. & Containers & Commodity servers \\ DeepNFV [80] & NFV SFC performance & Deep learning for QoS and traffic optimization, lightweight containerised vNFs and Edge NF deployments. & Containers & GNF framework \\ NetFate [81] & Edge NF deployments & Active NF/VM migration and open PaaS platform. & VM & Commodity servers and virtual switches \\ HyperNF [82] & NFV SFC performance & Reduced I/O sync overheads, hypercall-based I/O in VM context and vNF scalability. & VM & VALE switches and VMs \\ PhantomSFC [83] & Resource optimization & Service/Control decoupling, reduced latency and improved throughput. & VMs & VM/DPDK \\ MVMP [84] & NFV SFC performance & Improved throughput, lightweight vNFs, vNF fault tolerance and vNF replication. & Containers & DPDK, Container, Virtual Switch \\ MicroNF [85] & NFV/SFC performance & Optimal NF placement, reduced inter-NF latency, vNF performance optimization and scalability and fair scheduling of vNFs. & Containers & Docker and VMs \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance tuning frameworks.  
the CNN model and sent to the next network function in the chain for further action(s). The ability of the framework to classify images and the performance of the network functions at the edge of the network were evaluated, and improved performance in terms of precision and efficiency was recorded.