TA-MCF: Thermal-Aware Fluid Scheduling for Mixed-Criticality System

2018 ◽  
Vol 28 (02) ◽  
pp. 1950029 ◽  
Author(s):  
Tiantian Li ◽  
Tianyu Zhang ◽  
Ge Yu ◽  
Yichuan Zhang ◽  
Jie Song
Keyword(s):  
Energy Consumption ◽  
State Of The Art ◽  
Schedulability Analysis ◽  
Leakage Power ◽  
The State ◽  
Effect Of Temperature ◽  
Processing Capacity ◽  
Energy Aware ◽  
Fluid Scheduling ◽  
Mixed Criticality

Fluid scheduling allows tasks to be allocated with fractional processing capacity, which significantly improves the schedulability performance. For dual-criticality systems (DCS), dual-rate fluid-based scheduling has been widely studied, e.g., the state-of-the-art approaches mixed-criticality fluid scheduling (MCF) and MC-Sort. However, most of the existing works on DCS either only focus on the schedulability analysis or minimize the energy consumption treating leakage power as a constant. To this end, this paper considers the effect of temperature on leakage power and proposes a thermal and power aware fluid scheduling strategy, referred to as thermal and energy aware (TA)-MCF which minimizes both the energy consumption and temperature, while ensuring a comparable schedulability ratio compared with the MCF and MC-Sort. Extensive experiments validate the efficiency of TA-MCF.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

scholarly journals Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

2019 ◽  
Vol 9 (20) ◽  
pp. 4237 ◽  
Author(s):  
Tuong Le ◽  
Minh Thanh Vo ◽  
Bay Vo ◽  
Eenjun Hwang ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Performance Metrics ◽  
Short Term Memory ◽  
State Of The Art ◽  
Electric Energy ◽  
The State ◽  
Short Term ◽  
Electric Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

scholarly journals Thermal energy aware proportionate scheduler for multiprocessor systems

2018 ◽  
Vol 7 (3) ◽  
pp. 1656
Author(s):  
Ramesh Pasupuleti ◽  
Ramachandraiah Uppu
Keyword(s):  
Energy Consumption ◽  
High Temperature ◽  
Power Consumption ◽  
Thermal Energy ◽  
Simulation Experiment ◽  
Load Condition ◽  
Leakage Power ◽  
Multiprocessor Systems ◽  
Energy Aware ◽  
Battery Lifetime

As per Moore’s law, the power consumption and heat solidity of the multiprocessor systems are increasing proportionately. High temperature increases the leakage power consumption of the processor and thus probably escort to thermal runaway. Efficiently managing the energy consumption of the multiprocessor systems in order to increase the battery lifetime is a major challenge in multiprocessor platforms. This article presents Thermal Energy aware proportionate scheduler (TEAPS) to reduce leakage power consumption. Simulation experiment illustrate that TEAPS reduces 16% of energy consumption with respect to Mixed Proportionate Fair (PFAIR-M) and 36% of energy consumption with respect to Proportionate Fair (PFAIR) Schedulers on the system consisting of 20 processors under full load condition.  

scholarly journals State of the Art of Electricity Generation (2007-2017)

2019 ◽  
Vol 119 ◽  
pp. 00019
Author(s):  
Diana Enescu ◽  
Giovanni Vincenzo Fracastoro ◽  
Bruno Panella ◽  
Filippo Spertino
Keyword(s):  
Energy Consumption ◽  
Electricity Generation ◽  
Nuclear Power ◽  
Fossil Fuels ◽  
State Of The Art ◽  
The State ◽  
Electricity Production ◽  
Emerging Trends ◽  
World Energy ◽  
World Energy Consumption

The statistics for world energy consumption and electricity production in the last decade are presented to highlight the increment of the electricity share, compared to thermal usages and transportation, in the energy sector. The main technologies for electricity production from fossil fuels and nuclear power are summarised, indicating their characteristics, current plants, and emerging trends. Finally, the state of the art, regarding the technical applications of photovoltaic (PV) generators and wind turbines (WT), is presented.

scholarly journals Clustering-based energy-aware virtual network embedding

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772671
Author(s):  
Xu Liu ◽  
Zhongbao Zhang ◽  
Junning Li ◽  
Sen Su
Keyword(s):  
Energy Consumption ◽  
Energy Cost ◽  
State Of The Art ◽  
Electricity Price ◽  
Virtual Network ◽  
Virtual Network Embedding ◽  
Virtual Networks ◽  
Network Embedding ◽  
Energy Aware ◽  
Physical Network

Virtual network embedding has received a lot of attention from researchers. In this problem, it needs to map a sequence of virtual networks onto the physical network. Generally, the virtual networks have topology, node, and link constraints. Prior studies mainly focus on designing a solution to maximize the revenue by accepting more virtual networks while ignoring the energy cost for the physical network. In this article, to bridge this gap, we design a heuristic energy-aware virtual network embedding algorithm called EA-VNE-C, to coordinate the dynamic electricity price and energy consumption to further optimize the energy cost. Extensive simulations demonstrate that this algorithm significantly reduces the energy cost by up to 14% over the state-of-the-art algorithm while maintaining similar revenue.

scholarly journals High Performance Energy-Aware Cloud Computing: A Scope of Future Computing

2020 ◽  
Vol 9 (6) ◽  
pp. 667-683
Keyword(s):  
Energy Consumption ◽  
Power Management ◽  
High Performance ◽  
State Of The Art ◽  
Control Measures ◽  
Computing Environment ◽  
Energy Aware ◽  
Control Techniques ◽  
Hybrid Techniques ◽  
Consumption Control

This publication discusses high-performance energyaware cloud (HPEAC) computing state-of-the-art strategies to acknowledgement and categorization of systems and devices, optimization methodologies, and energy / power control techniques in particular. System types involve single machines, clusters, networks, and clouds, while CPUs, GPUs, multiprocessors, and hybrid systems are known to be device types. Objective of Optimization incorporates multiple calculation blends, such as “execution time”, “consumption of energy”& “temperature” with the consideration of limiting power/energy consumption. Control measures usually involve scheduling policies, frequency based policies (DVFS, DFS, DCT), programmatic API’s for limiting the power consumptions (such as” Intel- RAPL”,” NVIDIA- NVML”), standardization of applications, and hybrid techniques. We address energy / power management software and APIs as well as methods and conditions in modern HPEACC systems for forecasting and/or simulating power/energy consumption. Eventually, programming examples are discussed, i.e. programs & tests used in specific works. Based on our study, we point out some areas and there significant issues related to tools & technologies, important for handling energy aware computations in HPEAC computing environment

scholarly journals Spin Wave Based 4-2 Compressor

2021 ◽  
Author(s):  
Abdulqader Mahmoud ◽  
Frederic Vanderveken ◽  
Florin Ciubotaru ◽  
Christoph Adelmann ◽  
Sorin Cotofana ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Energy Consumption ◽  
Circuit Design ◽  
Wall Motion ◽  
State Of The Art ◽  
Magnetic Tunnel Junction ◽  
Domain Wall Motion ◽  
The State ◽  
Micromagnetic Simulations ◽  
Low Energy Consumption

By their very nature, Spin Waves (SWs) consume ultra-low amounts of energy, which makes them suitable for ultra-low energy consumption applications. In addition, a compressor can be utilized to further reduce the energy consumption and enhance the speed of a multiplier. Therefore, we propose a novel energy efficient SW based 4-2 compressor consisting of 4 XOR gates and 2 Majority gates. The proposed compressor is validated by means of micromagnetic simulations and compared with the state-of-the-art SW, 22nm CMOS, Magnetic Tunnel Junction (MTJ), Domain Wall Motion (DWM), and Spin-CMOS technologies. The performance evaluation shows that the proposed compressor consumes 2.5x less and 1.25x less energy than the 22nm CMOS and the conventional SW compressor, respectively, whereas it consumes at least 3 orders of magnitude less energy than the MTJ, DWM, and Spin-CMOS designs. Furthermore, the compressor achieves the smallest chip real-estate. In summary, the performance evaluation of our proposed compressor shows that the SW technology has the potential to progress the state-of-the-art circuit design in terms of energy consumption and scalability.

A SELECTIVE READ-BEFORE-WRITE SCHEME FOR ENERGY-AWARE SPIN TORQUE TRANSFER RAM (STT-RAM) CACHE DESIGN

2013 ◽  
Vol 22 (05) ◽  
pp. 1350038 ◽  
Author(s):  
TIEFEI ZHANG ◽  
TIANZHOU CHEN ◽  
JIANZHONG WU ◽  
YOUTIAN QU
Keyword(s):  
State Of The Art ◽  
Leakage Power ◽  
Optimization Techniques ◽  
Spin Torque ◽  
Energy Aware ◽  
Low Leakage ◽  
Spin Torque Transfer ◽  
L2 Cache ◽  
On Chip ◽  
Cache Design

Due to its low leakage power and high density, spin torque transfer RAM (STT-RAM) has become a good candidate for future on-chip cache. However, STT-RAM suffers from higher write energy compared to the SRAM. One state-of-the-art technique to alleviate this problem is read-before-write (RBW). In this paper, we study the pattern of the write accesses to the L2 cache and show that directly applying the RBW to a STT-RAM L2 cache can be problematic from energy perspective. We then propose a selective read-before-write (SRW) scheme to further reduce the dynamic write energy of the STT-RAM cache. Additional optimizations are included in the design of SRW so that it can save a considerable amount of energy at negligible overheads. The experimental results show that SRW achieves a 86.0% reduction in write energy consumption vs. a baseline without any write optimization techniques, and a 6.55% more reduction compared to the RBW scheme.

scholarly journals Spin Wave Based 4-2 Compressor

2021 ◽  
Author(s):  
Abdulqader Mahmoud ◽  
Frederic Vanderveken ◽  
Florin Ciubotaru ◽  
Christoph Adelmann ◽  
Sorin Cotofana ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Energy Consumption ◽  
Circuit Design ◽  
Wall Motion ◽  
State Of The Art ◽  
Magnetic Tunnel Junction ◽  
Domain Wall Motion ◽  
The State ◽  
Micromagnetic Simulations ◽  
Low Energy Consumption

By their very nature, Spin Waves (SWs) consume ultra-low amounts of energy, which makes them suitable for ultra-low energy consumption applications. In addition, a compressor can be utilized to further reduce the energy consumption and enhance the speed of a multiplier. Therefore, we propose a novel energy efficient SW based 4-2 compressor consisting of 4 XOR gates and 2 Majority gates. The proposed compressor is validated by means of micromagnetic simulations and compared with the state-of-the-art SW, 22nm CMOS, Magnetic Tunnel Junction (MTJ), Domain Wall Motion (DWM), and Spin-CMOS technologies. The performance evaluation shows that the proposed compressor consumes 2.5x less and 1.25x less energy than the 22nm CMOS and the conventional SW compressor, respectively, whereas it consumes at least 3 orders of magnitude less energy than the MTJ, DWM, and Spin-CMOS designs. Furthermore, the compressor achieves the smallest chip real-estate. In summary, the performance evaluation of our proposed compressor shows that the SW technology has the potential to progress the state-of-the-art circuit design in terms of energy consumption and scalability.

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