PC POWER MANAGEMENT BASED ON APPLICATION IDLE TIME AND POWER TRANSITIONING EVENT PROFILING

Green Information Technology (IT) has recently become one of the main focuses in research and practice. Its primary goal is to reduce or optimize power consumption, and to address the issue of large power wastage in many organizations. Computer power management is part of the green IT that can help to save computer power consumption. Consequently, it is helping organizations to increase profits and to reduce environmental impact. Thisresearch contributes to the green ITby proposing a mechanism to efficiently manage the computer power consumption through a computer power management application

Automatic Standby Power-Saving Power Strip

The Standby Power Saving Power Strip improves on common power strip functionality. The project reduces the power loss from most electrical devices’ standby power mode, while retaining the convenience of leaving the devices plugged-in. The surge protector automatically recognizes when the plugged-in devices enter standby power mode. Then, it shuts off power to that device and automatically provides power back to the plugged-in devices, when turned on

Power Consumption Analysis, Measurement, Management, and Issues:A State-of-the-Art Review of Smartphone Battery and Energy Usage

The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed

Energy aware approach for HPC systems

International audienceHigh‐performance computing (HPC) systems require energy during their full life cycle from design and production to transportation to usage and recycling/dismanteling. Because of increase of ecological and cost awareness, energy performance is now a primary focus. This chapter focuses on the usage aspect of HPC and how adapted and optimized software solutions could improve energy efficiency. It provides a detailed explanation of server power consumption, and discusses the application of HPC, phase detection, and phase identification. The chapter also suggests that having the load and memory access profiles is insufficient for an effective evaluation of the power consumed by an application. The available leverages in HPC systems are also shown in detail. The chapter proposes some solutions for modeling the power consumption of servers, which allows designing power prediction models for better decision making.These approaches allow the deployment and usage of a set of available green leverages, permitting energy reduction

Modeling virtualized application performance from hypervisor counters

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 61-64).Managing a virtualized datacenter has grown more challenging, as each virtual machine's service level agreement (SLA) must be satisfied, when the service levels are generally inaccessible to the hypervisor. To aid in VM consolidation and service level assurance, we develop a modeling technique that generates accurate models of service level. Using only hypervisor counters as inputs, we train models to predict application response times and predict SLA violations. To collect training data, we conduct a simulation phase which stresses the application across many workloads levels, and collects each response time. Simultaneously, hypervisor performance counters are collected. Afterwards, the data is synchronized and used as training data in ensemble-based genetic programming for symbolic regression. This modeling technique is quite efficient at dealing with high-dimensional datasets, and it also generates interpretable models. After training models for web servers and virtual desktops, we test generalization across different content. In our experiments, we found that our technique could distill small subsets of important hypervisor counters from over 700 counters. This was tested for both Apache web servers and Windows-based virtual desktop infrastructures. For the web servers, we accurately modeled the breakdown points and also the service levels. Our models could predict service levels with 90.5% accuracy on a test set. On a untrained scenario with completely different contending content, our models predict service levels with 70% accuracy, but predict SLA violation with 92.7% accuracy. For the virtual desktops, on test scenarios similar to training scenarios, model accuracy was 97.6%. Our main contribution is demonstrating that a completely data-driven approach to application performance modeling can be successful. In contrast to many other works, our models do not use workload level or response times as inputs to the models, but nevertheless predicts service level accurately. Our approach also lets the models determine which inputs are important to a particular model's performance, rather than hand choosing a few inputs to train on.by Lawrence L. Chan.M.Eng

A Smart Health Monitoring Technology

With the implementation of the Advanced Metering Infrastructure (AMI), comes the opportunity to gain valuable insights into an individual’s daily habits, patterns and routines. A vital part of the AMI is the smart meter. It enables the monitoring of a consumer’s electricity usage with a high degree of accuracy. Each device reports and records a consumer’s energy usage readings at regular intervals. This facilitates the identification of emerging abnormal behaviours and trends, which can provide operative monitoring for people living alone with various health conditions. Through profiling, the detection of sudden changes in behaviour is made possible, based on the daily activities a patient is expected to undertake during a 24-hour period. As such, this paper presents the development of a system which detects accurately the granular differences in energy usage which are the result of a change in an individual’s health state. Such a process provides accurate monitoring for people living with self-limiting conditions and enables an early intervention practice (EIP) when a patient’s condition is deteriorating. The results in this paper focus on one particular behavioural trend, the detection of sleep disturbances; which is related to various illnesses, such as depression and Alzheimer’s. The results demonstrate that it is possible to detect sleep pattern changes to an accuracy of 95.96% with 0.943 for sensitivity, 0.975 for specificity and an overall error of 0.040 when using the VPC Neural Network classifier. This type of behavioral detection can be used to provide a partial assessment of a patient’s wellbeing

Sensor-based early activity recognition inside buildings to support energy and comfort management systems

Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activities after only 10 sensor events with an accuracy of 81%. Furthermore, the correlation between activities can be used to predict the next activity with an accuracy of about 60%

gLCB: An Energy Aware Context Broker

Context Worldwide mobile device sales will reach 821 Million units in 2012 and will rise to 1.2 Billion in 2013. Inevitably the paradigm for access information and Internet services is increasingly migrating to mobile. Context-aware services can help to improve the user experience because they can adapt themselves to the users’ context but, despite the improvements in terms of hardware, the the main obstacle towards a widespread adoption of such services is the limited battery life. A context-aware service requires the installation of a Context-Broker Application, which generates a continuous flow of data from the smartphone and a constant usage of its equipped sensors: as a consequence the considerable increase of energy consumption becomes a problem. Aim The aim of this work is to propose gLCB an Energy Efficient Context-Aware middleware for Android OS, which is able to collect Context Information and to send it to a remote platform in an energy-efficient way. The gLCB application proposes a new energy-aware data collection based on user profiles. Methods We define policies based on battery consumption profiles, which are selected depending on modifications of the context information. Moreover, we have implemented an automatic consumption testing mechanism and a statistical treatment of results to provide a reliable validation of gLCB in terms of energy efficiency. Results Experimental results show that our middleware got the best trade-off between number of server uploads and battery lifetime with the policies computed automatically by the device. This means that the way in which software is written can impact the energy consumption of a mobile device and service adaptation can be based on the actual value of the battery charge

Energy consumption in non-domestic buildings based on empirical data

The electricity demand data for a variety of buildings throughout the UK has been made available for analysis. This consists of half hourly resolution data spanning several years for 48 schools (with a mixture of secondary, primary and specialised secondary) and two office buildings, allowing key trends and patterns in energy use to be identified. These trends can include differences between annual profiles, differences between winter and summer months, and differences in weekday and weekend energy use. Additionally, the effect of other variables such as climate, user behaviour and general building data on the building’s energy consumption can be investigated. A database of half hourly school energy demand data, with corresponding building details has been set up and a preliminary analysis preformed. Alternative methods of pattern recognition in non-domestic energy usage are discussed, and the variables necessary to calibrate this information are evaluated. This allowed the possibility of creating ‘generic’ electricity demand profiles for each category of school in each season, leading to a more detailed energy performance benchmark table. Understanding the energy demand, both electricity and gas use, of a building can help the issue of determining how and when energy is used in a day, week, month or year. Only after this knowledge has been gained can energy saving measures be successfully applied and, in turn, can the energy consumption of the non-domestic sector be reduced