Fog computing was designed to support the specific needs of latency-critical applications such as augmented reality, and IoT applications which produce massive volumes of data that are impractical to send to faraway cloud data centers for analysis. However this also created new opportunities for a wider range of applications which in turn impose their own requirements on future fog computing platforms. This article presents a study of a representative set of 30 fog computing applications and the requirements that a general-purpose fog computing platform should support.

The Cloud already represents an important part of the global energy consumption, and this consumption keeps increasing. Many solutions have been investigated to increase its energy efficiency and to reduce its environmental impact. However, with the introduction of new requirements, notably in terms of latency, an architecture complementary to the Cloud is emerging: the Fog. The Fog computing paradigm represents a distributed architecture closer to the end-user. Its necessity and feasibility keep being demonstrated in recent works. However, its impact on energy consumption is often neglected and the integration of renewable energy has not been considered yet. The goal of this work is to exhibit an energy-efficient Fog architecture considering the integration of renewable energy. We explore three resource allocation algorithms and three consolidation policies. Our simulation results, based on real traces, show that the intrinsic low computing capability of the nodes in a Fog context makes it harder to exploit renewable energy. In addition, the share of the consumption from the communication network between the computing resources increases in this context, and the communication devices are even harder to power through renewable sources.

Several countries have deployed, or have started the deployment of a smart metering infrastructure in order to enable the Smart Grid. This infrastructure aims to provide new services to grid users and grid operators relying on several communication technologies. One of the goals of this infrastructure is to improve energy consumption, for instance by increasing the awareness of the users, or by enforcing energy management policies. Yet, this infrastructure also consumes energy. The objective of this work is to accurately characterize the energy consumption of each part of the smart metering infrastructure, at a nation-wide scale. We also explore several consumption scenarios highlighting the impact of legacy technologies on the energy consumption of the smart metering infrastructure.

One of the main target of smart grids consists in deploying efficient demand-side management strategies. Several communication technologies are used to transfer monitoring data and control commands from and to smart meters with different quality of service depending on the technology. The quality of service provided by cyber-physical networks can significantly impact the performance of demand-side management strategies. But, this quality of service is rarely considered in research works on smart grid management, leading to over-estimating the advantages of smart grid technology. Therefore, the goal of our work is to quantify the performance degradation on demand-side management due to the quality of service provided by three communication technologies used for smart gridsnamely PLC, Wi-Fi and Ethernet. This quantitative analysis relies on a residential grid congestion management case study and a coherent co-simulation environment. Two simple energy management policies (one centralized, the other decentralized) are considered. In addition, we present a sensitivity analysis over several parameters to highlight the limitations of communication technologies in this context of power congestion management.