Abstract
The increasing worldwide energy demand, the CO2 emissions generated due to the production and use of energy, climate change, and the depletion of natural resources are important concerns that require new solutions for energy generation and management. In order to ensure energy sustainability, measures, including the use of renewable energy sources, the deployment of adaptive energy consumption schemes, and consumer participation, are currently envisioned as feasible alternatives. Accordingly, this paper presents the requirements and algorithmic solutions for efficient management of energy consumption, which aims to optimize the use of available energy, whether or not it is 100% renewable, by minimizing the waste of energy. The proposal works within a Demand-Response environment, uses Network Functions Virtualization as an enabling technology, and leverages the massive connectivity of the Internet of Things provided by modern communications technologies. The energy consumption optimization problem is formulated as an Integer Linear Program. It is optimally solved while using a brute-force search strategy, defined as OptTs, to detect all concerns that are related to the problem. Given the NP-hard nature of the problem and the non-polynomial complexity of OptTs, some heuristic solutions are analyzed. Subsequently, a heuristic strategy, described as FastTs based on a pre-partitioning method, is implemented. The simulation results validate our proposed energy management solution. Exact and heuristic strategies, when deployed in the Network Functions Virtualization domain, demonstrate improvements in the way that energy is consumed, thereby offering an increase in service processing. The evaluation results also show that FastTs produces high-quality solutions that are close to those of OptTs while executing 230×–5000× faster.
Highlights
In recent years, the increasing energy consumption and CO2 emissions caused by the deployment of new services and the proliferation of the Internet of Things (IoT) concept are becoming critical concerns driving the adoption of new solutions for energy production and management [1]
To enhance the running time for larger scenarios, a method that could be complemented to FASTTS is the parallel processing of partitions, so the total execution time would decrease proportionally as the number of cores increases
This paper studies and formulates an Network Functions Virtualization (NFV)-Enabled energy management model for renewable and non-renewable energy sources in order to analyze the requirements and concerns about service scheduling algorithms
Summary
The increasing energy consumption and CO2 emissions caused by the deployment of new services and the proliferation of the Internet of Things (IoT) concept are becoming critical concerns driving the adoption of new solutions for energy production and management [1]. An essential component for deploying DR schemes is the ICT infrastructure that is needed for both the data exchange (i.e., notifications and instructions) between the ES and the ECs and the execution of management mechanisms (e.g., workload scheduling) [5] In this regard, existing ICT solutions, such as cloud computing infrastructures and IoT technologies, can be used as key enablers for delivering feasible smart and programable management approaches for adaptive consumption of available supply. All energy consumers (i.e., devices, appliances, or, in general, IoT infrastructures) will be able to be managed, for example, in terms of their consumption This condition facilitates a true customer-side, the complete interaction between the ECs and the ES, the improvement in control, monitoring and management tasks, as well as the applicability to small scenarios, such as Home Energy Management Systems (HEMS) or environments as big as cities or countries. Aggregated power demanded Available power in the system Power from Non-Renewable sources Power from Renewable sources Service identifier, k ∈ {1, · · · , N}
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