# A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities
## 5 Discussion: Research Trends and Future Challenges  
According to the performed survey, automatic routing and bootstrapping for in-band control in SDN is still an ongoing research field. Different proposals tackle both features using different techniques. Several proposals provide comprehensive implementations and evaluation, even with a publicly available code. Regarding failure recovery, some proposals exist, but just a few guarantee low recovery times with reduced overhead. From our point of view, the area could still be improved, particularly if combined with high-performance routing and automatic bootstrapping and if they are implemented in real testbeds (or at least emulated and deployed with SDN popular platforms in industry environments, like ONOS). Finally, distributed control planes still require additional research efforts. Even if several works are mentioned in this survey, most of them belong to a few authors. Regarding research trends and future challenges, we envision potential research topics, including  
* Considering IoT and Low-power and Lossy Networks (LLNs) as a key use case for leveraging in-band control, since network devices in these scenarios are usually constrained and cannot fully deploy out-of-band SDN control. * Testing in-band approaches in big telecommunication networks (even if emulated) to check the feasibility of these approaches. * Leveraging in-band as an East/Westbound Interface (EWBI) protocol to grant an effective and reliable communication among distributed controllers (which are the most common deployment option). This aspect is barely covered in the current literature [106]. * Evaluating potential security risks of in-band control and methods to tackle them, as just a few works mention it. * Measuring Key Performance Indicators (KPIs) to compare in-band, out-of-band and hybrid-band approaches, as well as their implementation and compatibility with diverse SDN frameworks. Currently, few works tried to provide a holistic overview about it [7; 107; 108]. * Improving in-band connection routing with Artificial Intelligence (AI)/Machine Learning (ML). In computer science, AI is also known as machine intelligence. ML is a category of AI based on natural intelligence that can learn from data, make decisions, identify patterns, and perform various actions with less human intervention. ML can potentially be used to solve many problems in networking, including design, implementation, performance, and verification [109]. ML can be applied to improve in-band connectivity in many situations, such as finding the path with the highest QoS [69], finding disjoint paths for MPTCP [63; 68], or improving network security by detecting a malicious node [65]. The use of ML for routing optimisation in SDN can be found in this survey [109]. A good example is the proposal by [PERSON] [110]. They present a new routing algorithm that they implemented in a real SDN network with better results than other routing algorithms, such as OSPF and Least Loaded (LL). [PERSON] et al [111] provides another example of using ML on a WAN SDN for load balancing optimisation, which could also be used for in-band backup links. ML should also be used to improve network resilience by providing some alternatives or backup paths for the in-band links. Finally, we would like to highlight the importance of designing a full-fledged in-band protocol that covers all four aspects surveyed in this article and comprehensively integrates with an open source SDN controller like ONOS, ODL, or even TeraflowSDN [112], which was recently proposed as a SDN controller closely aligned with industry requirements and is a part of ETSI's Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) ecosystems. Providing such a holistic design would facilitate the actual usage of automatic in-band control approaches. In the meantime, while partial solutions are published, industry might still manually deploy in-band control in SDN.