## 5 Proof-of-Concept Results
### The P4 Algorithm
There are numerous ways this could be performed, such as through manual human planning, machine learning algorithms, or game theory. There is a significant amount of work in the field of SDN tackling this issue of controller placement and clustering, and adapting these advancements to the space DTN environment would aid a SDDTN network in its ability to scale. Although the proof-of-concept P4 DTN implementation was able to demonstrate novel capabilities, several components should be improved before deploying to the field. This was largely due to the issues we faced with the variable-width fields of BP, however the ideas we presented and the different implementations attempted are not exhaustive. Therefore, we believe it is still possible to use P4 to implement hardware that is able to more rigorously handle BP. This could be possible through the use of other P4 targets, modified algorithms, through improvements of the Tofino target and provided externs, or through improvements in the P4 language itself. Alternative tools to programming or creating hardware should also be considered. Details of BP that were not handled in this proof-of-concept translator include a general codec for SDNV and CBOR fields, BPv7 cyclic redundancy checks (CRC) for each block, accounting for both standard and CBHE encoded BPv6 bundles, handling different orderings of extension blocks, and determining what a proper mapping of data between BPv6 and BPv7 should look like. Important aspects of DTN and BP that were outside the scope of this work include handling other transport layers such as TCP and LTP, handling Bundle Protocol Security (BPSec), and improving bundle routing through learning of contacts, EIDs, IP addresses, and MAC addresses. Additionally, there is exciting work to be done in the far future if a fully featured DTN node was able to be implemented in P4. For example, previous work has demonstrated it is possible to use P4 to implement a router that can use machine learning to learn network behavior at both the individual node level and at the SDN controller level [39]. Successful application of these router machine learning concepts to a SDDTN node could enable a cognitive DTN (CDTN) node that is able to sense, react, and dynamically respond to changes in the network that lead to significantly improved routing that may not be possible with traditional routing algorithms. A CDTN and other cognitive forms of network communications in the space DTN environment are desirable technology for NASA and its mission to improve space network scalability [40]. ## 7 Conclusions

A SDDTN architecture is proposed as a solution to the scalability issue that space DTN networks currently face. Current space DTN networks rely on CGR and proper distribution of contact plans to ensure that DTN nodes are able to route bundles in the challenged space networking environment. The SDDTN architecture aims to solve the issue of contact plan distribution in this highly dynamic environment with intermittent links by dividing the network into smaller clusters. Benefits of this clustering approach that SDDTN provides includes more reliable contact plan distribution, reduced CGR computation runtime, and the ability to accumulate information from individual nodes within a cluster to make more optimal decisions. The BP translator proof of concept showed one way that SDN hardware, P4, and the BP headers are able to coexist with one another. This served as both a test for what P4 may be able to contribute to DTN network programmability and also as a potential real-world application in aiding interoperability between DTN nodes using different versions of BP. We showed that a switch can be used as more than just a bundle relay device, but as a component in the DTN network that provides additional routing and protocol translation capabilities.