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Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns that drive the urgent creation of new solutions for energy management. In this regard, by leveraging the massive connectivity provided by emerging 5G communications, this paper proposes a long-term sustainable Demand-Response (DR) architecture for the efficient management of available energy consumption for Internet of Things (IoT) infrastructures. The proposal uses Network Functions Virtualization (NFV) and Software Defined Networking (SDN) technologies as enablers and promotes the primary use of energy from renewable sources. Associated with architecture, this paper presents a novel consumption model conditioned on availability and in which the consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as prioritization of the energy supply and workload scheduling using time-shifting capabilities. The complexity of the proposal is analyzed in order to present an appropriate architectural framework. The energy management solution is modeled as an Integer Linear Programming (ILP) and, to verify the improvements in energy utilization, an algorithmic solution and its evaluation are presented. Finally, open research problems and application scenarios are discussed.

Abstract-The power grid has become a critical infrastructure, which modern society cannot do without. It has always been a challenge to keep power supply and demand in balance; the more so with the recent rise of intermittent renewable energy sources. Demand response schemes are one of the counter measures, traditionally employed with large industrial plants. This paper suggests to consider data centres as candidates for demand response as they are large energy consumers and as they are able to adapt their power profile sufficiently well. To unlock this potential, we suggest a system of contracts that regulate collaboration and economic incentives between the data centre and its energy supplier (GreenSDA) as well as between the data centre and its customers (GreenSLA). Several presented use cases serve to validate the suitability of data centers for demand response schemes.

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.