Fog networks offer computing resources of varying capacities at different distances from end users. A Fog Node (FN) closer to the network edge may have less powerful computing resources compared to the cloud, but the processing of computational tasks in FN limits long-distance transmission. How should the tasks be distributed between fog and cloud nodes? We formulate a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem minimizing task transmission- and processing-related energy with delay constraints to answer this question. It is transformed with Successive Convex Approximation (SCA) and decomposed using the primal and dual decomposition techniques. Two practical algorithms called Energy-EFFicient Resource Allocation (EEFFRA) and Low-Complexity (LC)-EEFFRA are proposed and their effectiveness is tested for various network and traffic scenarios. Using EEFFRA/LC-EEFFRA can significantly decrease the number of computational requests with unmet delay requirements when compared with baseline solutions (from 48% to 24% for 10 MB requests). Utilizing Dynamic Voltage and Frequency Scaling (DVFS) minimizes energy consumption (by one-third) while satisfying delay requirements.

Fog computing can be used to offload computationally intensive tasks from battery powered Internet of Things (IoT) devices. Although it reduces energy required for computations in an IoT device, it uses energy for communications with the fog. This paper analyzes when usage of fog computing is more energy efficient than local computing. Detailed energy consumption models are built in both scenarios with the focus set on the relation between energy consumption and distortion introduced by a Power Amplifier (PA). Numerical results show that task offloading to a fog is the most energy efficient for short, wideband links.

Traffic variation heavily impacts energy savings achieved by putting Line Cards (LCs) into Sleep Mode (SM) in core Internet Protocol (IP)-over-Wavelength Division Multiplexing (WDM) networks. We propose a simple traffic model covering temporal and spatial traffic variation and investigate the impact of (controlled) traffic variations on energy savings achieved with the Energy Watermark Algorithm (EWA) as well as on devices lifetime in realistic network scenarios. The results indicate that spatial variation of traffic is no obstacle in saving energy in the networks. We point out the need for consistent routing schemes between the reference network (designed with a sophisticated Mixed-Integer Linear Programming (MILP) formulation in our case) and the solutions computed dynamically during network operation (with the lifetime-unaware EWA in our case).

This paper targets the lifetime-aware management of a set of optical line amplifiers (OLAs) in an optical network exploiting sleep mode (SM) in order to save energy. We first present a simple model to predict the OLA lifetime. We then provide different mixed-integer linear programming (MILP) formulations, which jointly consider energy saving and lifetime. The proposed MILP formulations are then solved on different realistic scenarios, by taking into account the spatial and temporal variations of traffic demands. Results show that our lifetime-aware approach outperforms classical energy saving ILP formulations, which instead tend to notably decrease the OLA lifetime. More important, the proposed approaches can achieve a good lifetime performance without consuming significantly more energy than purely energy-aware strategies.

The problem of reducing power consumption in backbone networks by adopting sleep modes has been extensively studied in the literature. In contrast to previous work, we forecast power consumption and traffic in two operator networks (France Telecom (FT) and Telefo´nica Investigacio´n y Desarrollo (TID)) for the year 2020, and consider both Single Line Rates (SLRs) and Mixed Line Rates (MLRs) deployments. Given these realistic scenarios, the set of devices in sleep mode is computed autonomously on each network node by a simple power-aware algorithm using only local information as input and requiring no rerouting. Results show that the savings obtained by adopting sleep modes are significant, even if power efficiency (W/Gbps) is increased. Moreover, we show that networks using MLR always consume less energy than SLR-based networks when the power-aware algorithm is applied to the considered scenarios.