Energy-aware Approaches Research Articles

Intelligent energy aware approaches for residential buildings: state-of-the-art review and future directions

Intelligent energy aware approaches for residential buildings: state-of-the-art review and future directions

Routing with Renewable Energy Management in Wireless Sensor Networks.

In wireless sensor networks (WSNs), power consumption is an important aspect when designing routing protocols. When compared to other components of a sensor node, the power required by radio transmitters is responsible for most of the consumption. One way to optimize energy consumption is by using energy-aware protocols. Such protocols take into consideration the residual energy information (i.e., remaining battery power) when making decisions, providing energy efficiency through the careful management of energy consumption. In this work, we go further and propose a new routing protocol that uses not only the residual energy information, but also the available renewable energy information from renewable energy sources such as solar cells. We then present the Renewable Energy-Based Routing (REBORN) algorithm, an energy-aware geographic routing algorithm, capable of managing both the residual and the available energy. Our results clearly show the advantages and the efficiency achieved by our REBORN algorithm when compared to other proposed energy-aware approaches.

A Novel Resource Productivity Based on Granular Neural Network in Cloud Computing

In recent years, due to the growing demand for computational resources, particularly in cloud computing systems, the data centers’ energy consumption is continually increasing, which directly causes price rise and reductions of resources’ productivity. Although many energy‐aware approaches attempt to minimize the consumption of energy, they cannot minimize the violation of service‐level agreements at the same time. In this paper, we propose a method using a granular neural network, which is used to model data processing. This method identifies the physical hosts’ workloads before the overflow and can improve energy consumption while also reducing violation of service‐level agreements. Unlike the other techniques that use a single criterion, namely, worked on the basis of the history of using the processor, we simultaneously use all the productivity rates criteria, that is, processor productivity rates, main memory, and bandwidth. Extensive real‐world simulations using the CloudSim simulator show the high efficiency of the proposed algorithm.

A Review of Energy-aware Cloud Computing Surveys

The increasing demands on the usage of data centers especially in provisioning cloud applications (i.e. data-intensive applications) have drastically increased the energy consumption and becoming a critical issue. Failing to handle the increasing in energy consumption leads to the negative impact on the environment, and also negatively affecting the cloud providers’ profits due to increasing costs. Various surveys have been carried out to address and classify energy-aware approaches and solutions. As an active research area with increasing number of proposals, more surveys are needed to support researchers in the research area. Thus, in this paper, we intend to provide the current state of existing related surveys that serve as a guideline for the researchers as well as the potential reviewers to embark into a new concern and dimension to compliment existing related surveys. Our review highlights four main topics and concludes to some recommendations for the future survey.

An Advanced Battery Model for WSN Simulation in Environments With Temperature Variations

Wireless sensor networks (WSNs) can be used to support monitoring activities in a wide range of applications and communication environments. Its usage in extreme conditions, in what concerns pressure, temperature, and humidity, must be carefully assessed before the network deployment. In particular, the temperature variations have a direct impact upon the behavior of WSNs through the batteries of sensor nodes. These electrochemical devices are highly susceptible to temperature variations, which modifies the offered effective charge capacity. In this context, it is difficult to estimate the behavior of batteries over time, impairing the extraction of relevant information for energy-aware approaches. Such information, particularly battery state of charge, voltage, and lifetime, is often used by WSN simulators to predict the communication behavior in different scenarios. Nevertheless, WSN simulators generally use simplistic battery models, causing significant deviations in simulation results when compared with actual WSN deployments. This paper describes the implementation of the Temperature-Dependent Kinetic Battery Model (T-KiBaM) in the Castalia simulator, which enables a considerable improvement of the accuracy of simulations in communication environments with different temperature conditions. An experimental assessment has been performed with temperature variations over time to validate the usage of the T-KiBaM battery model. The experimental results indicate that the T-KiBaM model is quite accurate when estimating battery behavior under both different temperature set points and different temperature variations.

Quantum virtual machine: power and performance management in virtualized web servers clusters

This paper presents a model for managing virtual machines (VMs) on web servers clusters, in addition to providing energy savings, our model has linear scalability and is independent of the processing platform. We define a default processing virtual web server, named as quantum virtual machine (QVM). A set of QVM performs a logical web server, which operates in a flexible manner, changing its performance and power consumption depending on the workload of the applications. Concepts of agile VM clone, co-allocation of VMs in the same core, and dynamic voltage and frequency scaling are used in the model, enabling rapid configuration actions and a fine-grained QoS control. Experiments evaluate the effectiveness of the proposed model by means of power consumption reduction and QoS violations as compared to the Linux CPU governors and state-of-the-art energy-aware approaches based on optimization. The results show our model conserves up to 51.8% of the energy required by a cluster designed for peak workload scenario, with a negligible impact on the applications performance.

Energy-Aware Computing for Infrastructure Clouds Using DVFS

With the increasing demand of cloud enabled services, service providers are deploying more and more data centers, resulting in huge amount of energy consumption. According to various surveys, energy is one of the dominant factors that impact the cost of cloud usage. Excessive power consumption leads to large power bills in addition to emission of greenhouse gases. Sufficient amount of energy can be conserved by running services at low power mode and powering off servers that are idle. Different authors have come up with different energy-aware approaches with the aim to minimize overall energy consumption. In this paper we are trying to investigate the difference in energy consumption level when data centre is equipped with energy aware approach i.e., using Dynamic Voltage and Frequency Scaling (DVFS) approach and without using energy efficient approach. Simulation results show that DVFS supported data centers consume 66% less energy as compared to non power aware approaches. Average resource utilization is optimal with DVFS as compared to non power aware approach.

An Distributed Virtual Machine Placement Algorithm for Balanced Resource Utilization and Low Energy Consumption

Virtual machine placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Traditional load balancing or energy-aware VM placement approaches either allocate VMs to PMs in centralized manner or ignore PM’s cost-capacity ratio to implement energy-aware VM placement. We address these two issues by introducing a distributed VM placement approach. A auction-based VM placement algorithm is devised for help VM to find the most suitable server in large heterogeneous cloud data centers. Our algorithm is evaluated by simulation. Experimental results show two major improvements over the existing approaches for VM placement. First, our algorithm efficiently balances the utilization of multiple types of resource by minimizing the amount of physical servers used. Second, it reduces system cost compared with existing approaches in heterogeneous environment.

Adaptive Energy-Aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing

In cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, thereby requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focusing on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider that the current network traffic causes performance degradation and thus may not really reduce SLA violation under a variety of workloads. In this context, this paper proposes three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation. Energy-aware methods for overloaded host detection and VM selection from an overloaded host are necessary to improve the energy efficiency and SLA violation of a cloud data center after migrating all VM from underloaded host turn to idle host, which switch to energy-saving mode is also beneficial. Gdr and MCP are adaptive energy-aware algorithms based on the robust regression model, for overloaded host detection. A Bw dynamic VM selection policy selects VM according to the network traffic from the overloaded host under SLAs. Experimental results on the real workload traces show that the proposed algorithms reduce energy consumption while maintaining the required performance levels in a cloud data center using a CloudSim simulator to validate the proposed algorithms.

Energy-Aware Approaches for Energy Harvesting Powered Wireless Sensor Nodes

Intensive research on energy harvesting powered wireless sensor nodes (WSNs) has been driven by the needs of reducing the power consumption by the WSNs and increasing the power generated by energy harvesters. The mismatch between the energy generated by the harvesters and the energy demanded by the WSNs is always a bottleneck as the ambient environmental energy is limited and time varying. This paper introduces a combined energy-aware interface with an energy-aware program to deal with the mismatch through managing the energy flow from the energy storage capacitor to the WSNs. These two energy-aware approaches were implemented in a custom developed vibration energy harvesting powered WSN. The experimental results show that, with the 3.2-mW power generated by a piezoelectric energy harvester under an emulated aircraft wing strain loading of $600~\mu \varepsilon $ at 10 Hz, the combined energy-aware approaches enable the WSN to have a significantly reduced sleep current from $28.3~\mu \text{A}$ of a commercial WSN to $0.95~\mu \text{A}$ and enable the WSN operations for a long active time of about 1.15 s in every 7.79 s to sample and transmit a large number of data (388 B), rather than a few ten milliseconds and a few bytes, as demanded by vibration measurement. When the approach was not used, the same amount of energy harvested was not able to power the WSN to start, not mentioning to enabling the WSN operation, which highlighted the importance and the value of the energy-aware approaches in enabling energy harvesting powered WSN operation successfully.

HVAC-Aware Occupancy Scheduling (Extended Abstract)

My research focuses on developing innovative ways to control Heating, Ventilation, and Air Conditioning (HVAC) and schedule occupancy flows in smart buildings to reduce our ecological footprint (and energy bills). We look at the potential for integrating building operations with room booking and meeting scheduling. Specifically, we improve on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. From computational standpoint, this is a challenging topic as HVAC models are inherently non-linear non-convex, and occupancy scheduling models additionally introduce discrete variables capturing the time slot and location at which each activity is scheduled. The mechanism needs to tradeoff minimizing energy cost against addressing occupancy thermal comfort and control feasibility in a highly dynamic and uncertain system.

Energy-Aware Web Browsing on Smartphones

Smartphone based web browsing wastes a lot of power when downloading webpages due to the special characteristics of the wireless radio interface. In this paper, we identify these special characteristics, and address power consumption issues through two novel techniques. First, we reorganize the computation sequence of the web browser when loading a webpage, so that the web browser can first run the computations that will generate new data transmissions and retrieve these data from the web server. Then, the web browser can put the wireless radio interface into low power state, release the radio resource, and then run the remaining computations. Second, we introduce a practical data mining based method to predict the user reading time of webpages, based on which the smartphone can switch to low power state when the reading time is longer than a threshold. To demonstrate the effectiveness of our energy-aware approaches, we develop a testbed with Android phones on T-Mobile UMTS network. Experimental results show that our approach can reduce the power consumption of smartphone by more than 30 percent during web browsing. Moreover, our solution can further reduce the webpage loading time and increase the network capacity.