An NFV-Based Energy Scheduling Algorithm for a 5G Enabled Fleet of Programmable Unmanned Aerial Vehicles

Abstract

The fifth generation of mobile networks (5G) is expected to provide diverse and stringent improvements such as greater connectivity, bandwidth, throughput, availability, improved coverage, and lower latency. Considering this, drones or Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) devices are perfect examples of existing technology that can take advantage of the capabilities provided by 5G technology. In particular, UAVs are expected to be an important component of 5G networks implementations and support different communication requirements and applications. UAVs working together with 5G can potentially facilitate the deployment of standalone or complementary communications infrastructures, and, due to its rapid deployment, these solutions are suitable candidates to provide network services in emergency scenarios, natural disasters, and search and rescue missions. An important consideration in the deployment of a programmable drone fleet is to guarantee the reliability and performance of the services through consistent monitoring, control, and management scheme. In this regard, the Network Functions Virtualization (NFV) paradigm, a key technology within the 5G ecosystem, can be used to perform automation, management, and orchestration tasks. In addition, to ensure the coordination and reliability in the communications systems, considering that the UAVs have a finite lifetime and that eventually they must be replaced, a scheduling scheme is needed to guarantee the availability of services and efficient resource utilization. To this end, in this paper is presented an UAV scheduling scheme which leverages the potential offered by NFV. The proposed strategy, based on a brute-force search combinatorial algorithm, allows obtaining the optimal scheduling of UAVs in time, in order to efficiently deploy network services. Simulation results validate the performance of the proposed strategy, by providing the number of drones needed to meet certain levels of service availability. Furthermore, the strategy allows knowing the sequence of replacement of UAVs to ensure the optimal resource utilization.

NFV/SDN Enabled Architecture for Efficient Adaptive Management of Renewable and Non-Renewable Energy

“…In the proposal, the schedulable and non-schedulable services corresponds to NCS and CS, respectively. In addition, the time-shifting capability of workloads in energy management, have already been validated in previous work [32], [33], and the evaluation results demonstrate that this strategy allows for an efficient use of available energy (100% energy utilization in some cases) and the processing of services that under normal conditions (without strategy, i.e., T k s bw = T k s fw = 0) would be rejected.…”

“…Future work must include the following aspects: (i) analysis of the architecture, considering that an interesting point to be addressed is the modeling and sizing of the battery units, to guarantee the execution of CS; (ii) deployment of fast and scalable algorithmic solutions, taking as a baseline the metrics, procedures, and results obtained for the optimal solution; (iii) application of the concepts and strategies presented in the proposal to specific scenarios, e.g., managing battery power in a communications system supported by drones, as shown in [33]; and (iv) analysis in other areas including, regulation, standardization, economics, and market, considering that this paper is focused strictly on technical aspects.…”

Softwarization of UAV Networks: A Survey of Applications and Future Trends

“…An optimal UAV scheduling scheme is proposed in [262]. This scheme leverages the potential offered by NFV and 5G capabilities to be able to ensure energy-efficient management of resources and for the provision of network services.…”

Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks

“…Unfortunately, this action is not as trivial as it may seem at first: not only exists several limitations regarding the computing, storage and networking resources involved in the migration, but also its transition should be anticipated with enough time to ensure a seamless migration procedure, only possible when a VIM is able to monitor the battery lifetime of the aircraft. This VIM could use algorithms to indicate when the aircraft should leave/enter a recharging station such as the one seen in work [23] or [24], allowing it to also predict when the services would need migration before replacing the unit. However, at the time this paper is being written, most VIM implementations do not consider battery lifetime as a limited resource, making VNF migration in these environments suboptimal.…”

“…Develop a VIM able to consider battery lifetime for scheduling VNF migration in advance to increase NFV management and orchestration performance using planning algorithms [23].…”