# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 4 Practical Evaluation of Transport-Layer Options
Following this approach, we were able to obtain results using realistic values for the transmission rates in this kind of control transactions, making it easier to perceive traffic variations and its evolution during the tests, and performing better comparisons with real scenarios. In addition to the previous points, we need to take into account that we are dealing with intermittently available units. Their most prominent source for their disruptions is the battery lifetime: every UAV in the aerial network must have a rechargeable battery to power it. To simulate this functioning, we model the battery lifetime using a uniform random variable with upper limit 21 min and lower limit 19 min, U (19,21), for a 20 min average lifetime. These have been identified as regular operational values of commercial off-the-shelf SUAV units (e.g., [41]). Since there are no additional battery drain sources that may reduce its battery lifetime because the nodes are static, its position will not affect its battery consumption as demonstrated in [42], which makes a uniform variable an appropriate choice for modeling the battery lifetime, keeping the SUAV consumption values stable throughout the simulations. In addition, every simulation starts with a battery offset for each SUAV, to take into consideration that in a real-case scenario, SUAVs could have been running for a period of time when they are deployed. Moreover, since we are using a battery, aircraft must be replaced when its battery has been completely depleted. This replacement might be instantaneous in an ideal scenario since a battery-monitoring VIM could use a specialized algorithm to plan ahead and schedule a replacement when the other aircraft starts is near its battery lifetime limit point. However, there could be many problems in the process (bad weather, planning errors, etc.). Hence, we opted for an exponential random variable with average 2 min, E (2), to model their corresponding time replacements to compensate for those uncertainties. With both models, we can simulate a battery depletion-replacement under more realistic conditions. In our reference scenario, we assume that every SUAV has an analogous aircraft in the GCS whose battery is completely recharged when the SUAV flying has been completely discharged. We divided out tests into three different sets. In the first set, we aim at understanding how the exchange of orchestration information is affected by the intermittent availability of the SUAVs building the aerial ad-hoc network, through the analysis of its throughput. In a second set, we analyze how this information is affected by the presence of background data traffic among other nodes of the network (i.e., data-plane traffic corresponding to the network services deployed over the aerial network of SUAVs). In the first two sets, we assume the use of TCP to support the exchange of management and orchestration information between the GCS and each SUAV node. In a third set, we explore how UDP would behave in the previous scenarios and discuss the potential advantages that might be obtained when using UDP to deliver orchestration information in our reference scenario. The TCP variant used for all the tests has been TCP NewReno [43], which provides a well-known reference congestion control mechanism. Finally, We chose OLSR [35] because it is a well-known and recognized standard in MANET environments, due to its simplicity and efficiency, it is supported by NS3 and also has open-source implementations (which will be used in our future work). Moreover, in a previous work [5] we demonstrated that OLSR is an appropriate protocol to support data dissemination over multi-hop flying ad-hoc networks where the position of the flying nodes is relatively static (that is,changes in the network topology are mainly caused by the replacement of nodes due to battery constraints). In following subsections, we describe in more detail all the performed tests, as well as the observed results for each one of them.