Energy Efficiency in the ICT - Profiling Power Consumption in Desktop Computer Systems

Energy awareness in the ICT has become an important issue. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. The aim of this work was collecting and analysing power consumption data of general-purpose computer systems, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. We selected two desktop systems of different generations as test machines. Meanwhile, we developed 11 usage scenarios, and conducted several test runs of them, collecting power consumption data by means of a power meter. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, which corresponds to a percentage increase that can reach up to 20% on recent and more powerful systems. We determined that software and its usage patterns impact consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resource

Cloud-based or On-device: An Empirical Study of Mobile Deep Inference

Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values. Because of computational complexity and size constraints, these trained models are often hosted in the cloud. To utilize these cloud-based models, mobile apps will have to send input data over the network. While cloud-based deep learning can provide reasonable response time for mobile apps, it restricts the use case scenarios, e.g. mobile apps need to have network access. With mobile specific deep learning optimizations, it is now possible to employ on-device inference. However, because mobile hardware, such as GPU and memory size, can be very limited when compared to its desktop counterpart, it is important to understand the feasibility of this new on-device deep learning inference architecture. In this paper, we empirically evaluate the inference performance of three Convolutional Neural Networks (CNNs) using a benchmark Android application we developed. Our measurement and analysis suggest that on-device inference can cost up to two orders of magnitude greater response time and energy when compared to cloud-based inference, and that loading model and computing probability are two performance bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering (IC2E) conference 201

Power consumption of the Raspberry Pi: a comparative analysis

Over the past few decades, human beings have increasingly adopted different types of personal computers including desktop computers, laptops, tablets and smart phones. More recently, there has been the emergence of the Raspberry Pi and since its release in 2012, this new type of computer has undergone rapid growth in adoption to even become the fastest selling British computer. The Raspberry Pi has often been referred as a computer designed to change the world since it is capable to do most things that a desktop computer can do. The growing concern is that all these computers utilize power in order to operate thereby turning ICT into a power drainer. The diverse functionalities present in modern computers including communication and Web browsing, among others, were found to be important components that affect the power consumption of such devices. As such, this paper investigates how power consumption of the Raspberry Pi is affected by the key functionalities that could be performed by end-users on the platform. Moreover, this relationship is compared against other types of common personal computers before recommending on techniques and practices that could reduce the power consumption of this emerging type of computer

Chasing Carbon: The Elusive Environmental Footprint of Computing

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems

Monitoring and Fault Location Sensor Network for Underground Distribution Lines

One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.Ministerio de Economía y Competitividad, Government of Spain project Sistema Inteligente Inalámbrico para Análisis y Monitorización de Líneas de Tensión Subterráneas en Smart Grids (SIIAM) TEC2013-40767-RMinisterio de Educación, Cultura y Deporte, Government of Spain, for the funding of the scholarship Formación de Profesorado Universitario 2016 (FPU 2016

Demand response implementation into residential sector

In the current financial climate, focus on energy saving within the home has intensified by the desire to reduce costs. Western Australian residential electricity prices are expected to increase in 2016 and 2017. Between 2015 and 2017 the cost of supplying electricity is predicted to increase annually by 7% (Australian Energy Market Commission 2014, 57 -63). Fossil fuel savings, lowering average carbon emissions, as well as a permanent fall in electricity prices, are all significant incentives for the residential sector to look at different methods to reduce its power consumption. In Australia, the residential sector contributes about 25% of the total energy consumption but can incorporate up to 45% of Peak Demand. Pricing techniques and enabling technologies offer various possibilities for lowering Peak Demand by encouraging consumers to participate actively in power Demand Response. Our power networks are designed to meet Peak Demand to avoid equipment failure and service disruptions; this provides excellent opportunities for energy savings. Reducing Peak Demand will benefit consumers and suppliers by reducing power system costs. Suitable Pricing techniques can be applied in the residential sector, which could lead to consumer savings on electricity bills. Due to its complexity, the introduction and integration of pricing schemes into the different Energy Markets entails a comprehensive approach, including consideration of the functional energy performance, economic and environmental aspects, from conceptual design through to design realization. This report defines some enabling technologies such as smart meters, appliances, and tools which provide an opportunity for consumers to respond at short notice to a variety of signals. For example electricity price, by changing their energy consumption. The pricing techniques are divided into several basic pricing schemes and the effectiveness of each programme in Demand Response implementation into the household sector will be explored. The pricing tariffs are systematically examined, and proper cost analysis is performed to determine the practicality of implementation. Existing pricing schemes and pilot studies, smart appliances and meters, in-home displays and smart energy measuring devices are first introduced to estimate the suitability of the introduction of pricing schemes into the residential sector. Multiple scenarios with comparable pricing tariffs is recommended for a comprehensive evaluation of Demand Response implementation in the residential area and the selection of the optimal pricing technique. The proposed general pricing schemes are also applied to solving a real problem. Namely, the introduction of pricing schemes in two typical residential households in the suburbs of Thornlie and Ferndale in Perth, Western Australia to verify consumer shift in energy consumption behaviour