Power Consumption Of Machines Research Articles

Intent-Based Security for Functional Safety in Cyber-Physical Systems

We propose a novel intent-based method to prevent security attacks on the safety functions in cyber-physical systems used in smart manufacturing. Context information about a cyber-physical system is collected from various sensors including non-safety sensors measuring device temperature, motor rotation speed, or instantaneous power consumption of machines. Such contextual information along with operational and business intent of the system under consideration are then used to check whether the current situation is indeed an emergency situation or a normal situation. Unlike the conventional safety systems that only rely on raw sensor data and safety protocol status packets from safety sensors, which might be spoofed and/or modified, decision on the safety situation in our method is intelligently made by comparing aggregated sensor information from the cyber-physical system and its environment for compliance with pre-configured operational intents that define the normal safe and secure operation of the system. We also show how to integrate Machine Learning (ML) and Artificial Intelligence (AI) into the proposed method for efficient and automated analysis of both intents and aggregated context information to make more intelligent decisions in execution of functional safety protocols. Our proposed AI/ML integration approach also enables the prediction of safety critical situations before they occur. The proposed method aims to prevent unnecessary switching to fail-safe mode causing insecure system states such as emergency doors opening or system halting in normal situations. In addition, it prevents sticking in non-safe states and not switching to fail-safe mode in real emergencies which could cause hazard on device and/or people.

Design and optimization of intelligent orchard frost prevention machine under low-carbon emission reduction

In this paper, a self-heating small frost prevention machine is designed according to the agricultural frost prevention demand in mountainous and hilly areas. Firstly, the frost prevention machine's overall structure and key components are meticulously designed and modeled using UG 3D modeling software, tailored to the specific characteristics of the stepped planting environment in the mountainous area. At the same time, the flow field of the frost prevention machine is calculated and analyzed using jet diffusion theory and thermodynamic theory. The frost prevention machine's wind speed and temperature attenuation models are established, and subsequently fine-tuned and validated using Fluent to optimize its attenuation performance and operational range. To enhance frost prevention performance, a multi-objective bionic optimization strategy is applied to systematically optimize the structural and working parameters of the machine. Moreover, an adaptive mutation hybrid frog jump algorithm is introduced to further enhance the optimization accuracy. The simulation experimental results show that structural improvements can reduce axial speed and temperature attenuation loss by approximately 45%, while slightly increasing the machine's power consumption. On the other hand, optimizing working parameters enhances wind resistance and reduces overall power consumption. The actual experimental results show that the optimized frost prevention machine achieves a frost prevention coverage radius of 10 m within the orchard, even under maximum ambient wind speeds of 2 m/s, and it dynamically adapts to environmental changes. Additionally, the synergy of multiple frost prevention machines, powered by solar panels, enhances the overall frost prevention efficiency, leading to improved economy and reduced energy consumption.

Quantifying narrowing partition thickness in a vertical pneumatic separation channel for soybean seed cleaning

The paper highlights that the study of the soybean seed separation processes in a vertical deep aspirating channel contributes to the improvement of technical and economic indicators in grain cleaning equipment. (Research purpose) The research aims to substantiate the thickness of narrowing partitions in the vertical deep aspirating channel. (Materials and methods) A prototype of a vertical deep aspirating channel with three sections of varying height, was used for cleaning soybean seeds. The optimal thickness of the narrowing partitions in the channel has been justified. Impurity extraction completeness and seed separation efficiency within a vertical aspirating channel were assessed for soybean seed cleaning under maximum specific grain load conditions. (Results and discussion) The following parameters have been determined: the optimal thickness for the narrowing partitions is 70 millimeters under a maximum specific grain load of 5 kilograms per square centimeter per hour. Seed losses do not exceed 3 percent in the aspirating channel with narrowing partitions, with impurity extraction completeness of 74 percent for the «Waste» fraction. The efficiency of the separation process stands at 71 percent, and the purity level of the «Seeds» fraction reaches 81 percent in a single pass during the preliminary cleaning stage. (Conclusions) It has been established that the use of narrowing partitions in a vertical aspirating channel during soybean seed cleaning leads to an increase in the specific grain load up to 5 kilograms per square centimeter per hour, compared to 2 kilograms per square centimeter per hour achieved by traditional pneumatic separation channels. The proposed technical solution reduces soybean seed losses to 3 percent (compared to the initial requirements of no more than 10 percent) in a single pass and reduces machine power consumption from 4.5 to 2.25 kilowatt-hours.

Carbon Footprint of Proton Therapy: How Many Trees do We Need to Offset It?

<h3>Purpose/Objective(s)</h3> There is an increasing concern about rising carbon dioxide (CO2) levels and its hazardous impact on human health and climate change. Radiation oncology uses high energy machines; however, their carbon footprint is not well understood. This study quantifies the energy utilization of proton therapy, and estimates corresponding carbon footprint. Additionally, the study evaluates possible ways to offset CO2 with planting new trees. <h3>Materials/Methods</h3> Patients treated between 07/2020 and 06/2021 using Mevion S250 proton passive scattering system were evaluated. Current measurements were recorded at 2 second intervals and converted to kilowatt (kW) power consumption. Patients treated were reviewed for disease, site treated, dose, number of fractions, and duration of beam during each fraction. EPA web calculator was used to convert power consumption to tons of CO2, and the number of new trees required to offset it. <h3>Results</h3> There were 185 patients treated (average 19.9 per day); 63% had full course with protons, 31% had photon boost, and 6% had protons as boost. Total of 5,176 fractions were delivered (average 28 fractions per treatment course). Total BeamOn time was 538,546 seconds (149.6 hours), average 104 seconds per fraction. Power consumption was 55.8 kW in standby/night mode and 64.4 kW during BeamOn. Average daily power use was 1,346 kWh, and annual consumption was ∼490,000 kWh. BeamOn consumption accounted for 1.7% of total machine power draw. Breast cancer with RNI required highest power consumption (140 kWh per patient), while prostate cancer lowest (28 kWh), average across all patients was 52 kWh. Highest cumulative power use was for CNS tumors (2,753 kWh), lowest for lymphomas (208 kWh). Power draw of the clinical and administrative areas was 11 kW, and annual consumption was ∼96,000 kWh, for a program total of 586 MWh. The corresponding carbon footprint for BeamOn time was 6.83 metric tons of CO2, or 37 kg per patient course (highest for breast cancer at 99 kg, lowest for prostate cancer 20 kg). Carbon footprint for the annual machine power consumption was 347.5 tons CO2, and for the proton program was 415.5 tons of CO2. Attributed footprint was thus 415.5 tons / 185 = 2,200 kg CO2 per patient treated. The carbon offset required for BeamOn time would be 113 new trees planted (average 0.6 trees per patient courses). The corresponding offset required to operate the Mevion system for a year would be 5,743 new trees planted, and the proton program composite offset would be 6,867 new trees planted. The attributed carbon offset per patient treated would thus be 37 new trees planted. <h3>Conclusion</h3> Carbon footprint varied by disease treated and was on average 37 kg of CO2 per patient, and 415.5 tons of CO2 for the proton program. The number of new trees to be planted to offset the power use would be 6,867 new trees annually, or 37 per patient treated. We hope this study inspires radiation oncology practices to evaluate their own carbon footprint.

FACTORS AFFECTING MECHANICS OF MACHINING IN WOOD AND WOOD-BASED MATERIALS

Odun ve odun kökenli malzemeler mobilya, doğrama ve yapı endüstrilerinde çeşitli makinelerde işlenerek kullanılmaktadır. İşleme mekaniklerine dayalı çeşitli kesme kuvvetleri ortaya çıkmaktadır. Kesme gücü ve güç tüketimi faktörlerinin kesme kuvvetlerine bağlı olduğu belirtilmektedir. Kesiş sürecinin analizinde özellikle kesme kuvvetleri ana çıktı olarak kullanılmakta olup, kesişte etkili faktörlerin daha iyi anlaşılmasında fiziko-mekanik kesiş modelleri oldukça önemlidir. Odun ve odun kökenli malzemelerin fiziksel ve teknolojik özellikleri, makinede işleme koşulları ve kesici aletlerin mekanik durumu işleme mekaniklerini etkileyen faktörler olarak belirtilmektedir. Titreşim, ses, sıcaklık ve işleme kusurları yanında kesme gücü ile yüzey ve yonga kalitesi genellikle göz önünde bulundurulmamaktadır. İşlemede en düşük güç tüketimi ile düzgün yüzeyler elde edilebilmesi bakımından verimli ve ekonomik çalışmalar ile uygun işleme koşullarının belirlenmesi, malzeme ve kesici geometrisi ile işleme mekaniklerine dayandırılmaktadır. Bu araştırmada, odun ve odun kökenli malzemelerin işlenmesinde işleme mekaniklerini etkileyen faktörler tartışılmıştır. Kesme kuvvetleri, kesme gücü ve bunların ölçüm yöntemleri ile kesici ve işleme geometrisi incelenmiştir.

Carbon Footprint of Clinical Photon Therapy: Initial Estimates

<h3>Purpose/Objective(s)</h3> There is an increasing concern about rising carbon dioxide (CO2) levels and its hazardous impact on human health and climate change. Radiation oncology uses high energy machines; however, their carbon footprint is not well understood. This study estimates the energy utilization of photon therapy, and estimates corresponding carbon footprint. Additionally, the study evaluates possible ways to offset CO2 with planting new trees. <h3>Materials/Methods</h3> Patients treated between 07/2020 and 06/2021 using a technology company's medical linear accelerator system at one of our satellite locations were evaluated. A technology company data sheet was used to estimate power draw of the system. BeamOn mode was specified as 39 kVA, Ready mode as 15 kVA, On mode as 11 kVA, and Standby mode as 7 kVA; these were converted to kW. This is likely the worst-case power use scenario, with power factor typically 0.75 – 1.0. Patients treated were reviewed for dose, number of fractions, number of fields, and duration of beam during each fraction. Field delivery durations were obtained from the EMR, and kWh power consumption was estimated per fraction and per treatment course. EPA web calculator was used to convert power consumption to tons of CO2, and the number of new trees required to potentially offset it. <h3>Results</h3> There were 176 patients treated for 191 treatment courses. Total of 4,517 fractions were delivered (average 23.6 fractions per treatment course). Total BeamOn time was 710,606 seconds (197.4 hours), average 157 seconds per fraction. Total BeamOn power consumption would be 7,698 kWh, or ∼1.7 kWh per fraction and ∼40 kWh per treatment course. Annual power consumption including standby mode would be ∼81,250 kWh; BeamOn consumption would account for 9.5% of total machine power usage. The corresponding carbon footprint for BeamOn time would be 5.46 metric tonnes of CO2, or 29 kg of CO2 per patient course. Carbon footprint for the annual machine power consumption would be 57.6 tonnes of CO2. Attributed footprint would thus be 57.6 / 191 = 301 kg CO2 per patient treatment course. The carbon offset required for patient BeamOn time would be 90 new trees planted (average 0.5 trees per patient course). The corresponding offset required to operate the medical linear accelerator system for a year would be 952 new trees planted. The attributed carbon offset per treatment course would thus be 5 new trees planted. <h3>Conclusion</h3> Carbon footprint of the technology company's medical linear accelerator system is likely to be ∼30 kg of CO2 equivalent for direct BeamOn treatment course energy used, ∼300 kg of CO2 equivalent per overall patient energy expenditure, and ∼5.5 metric tonnes of CO2 in total to operate per year. The corresponding number of new trees to be planted to offset the power use would be 0.5 new trees, 5 new trees, and 952 new trees respectively. We are working on measuring the actual power draw during various treatment modes, which could be 5-15% lower than the kVA specification, and calculating the actual carbon footprint of clinical radiation therapy for our next study.

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

In order to solve the problem of outlier detection of integrated energy security defense monitoring software, an automatic detection algorithm of virtual machine power anomaly in a cloud computing environment is proposed. The method is implemented through three main steps: data preprocessing, pattern recognition, and prediction of virtual machine power anomaly detection model. It is found through experiments that with the increase of node number, the convergent iterations of the model are less and RMSE is lower, but the increase of node number of the hidden layer will lead to a longer model running time. When the number of nodes reaches 100, the test results of the validation set are significantly improved, and the loss function of the validation set is minimal when the number of nodes is less than 30 iterations. Finally, the hidden layer of the model consists of 100 LSTM units, followed by a dense output layer with 1 neuron, and 0.2 loss, retrospection, and foresight equal to 1. Adam optimizer was used to train LSTM and stop it in advance after 50 iteration steps. Its parameters remained default, with a learning rate of 0.001 and attenuation of 0.9. It can be seen that this model can well predict the virtual machine power consumption data and effectively solve the problem of outlier detection of integrated energy security defense monitoring software.

Can Surface Coating of Circular Saw Blades Potentially Reduce Dust Formation?

Coating of steel is a frequently applied approach to increase the resistance of moving machine parts towards abrasion, surface oxidation, and corrosion. Here, we show that plating circular saw blades with certain metals can help to reduce the electrical charging of wood dust during cutting, which has significant implications for occupational safety, healthcare, and lifetime of filter systems. With the example of beech wood planks, machine net energy consumption EV (J cm−3) and cumulated field strength (kV m−4) as caused by electrically charged particles were compared for cutting of 10- and 20-mm deep grooves (800 mm length) using saw blades of different toothing (24, 60 teeth) and surface coating (Cu, Ag, and Cr). To ensure uniform feed per tooth (fz = 0.063 mm), saw blades were operated at different rotation speeds (4000 vs. 1600 rpm). The results demonstrate that the extent of electrostatic sawdust charging can be manipulated to a certain extent by the type of saw blade plating. Coating with chromium turned out to be most effective in shifting the electrostatic charge of the wood particles towards neutralization. Lowering of rotation speed using circular saw blades of higher toothing was an additional measure significantly reducing electrostatic charging of wood dust. Hence, cutting with a chrome-coated blade with 60 teeth can be specifically recommended as the reduction of electrical saw dust charging is not associated with higher machine power consumption.

Scheduling a cellular manufacturing system based on price elasticity of demand and time-dependent energy prices

Cellular manufacturing system (CMS) is a novel production system adaptable to the make-to-order production. The present study focuses on scheduling CMS aimed at maximizing total profits as a function of the revenues earned from sales as well as energy consumption cost and order tardiness penalties. The components to be considered in the problem in hand include the time-dependency of energy price, price elasticity of demand, and speed-based power consumption of machines. Two linearization approaches are used to determine order quantities. The first chooses lot sizes from a continuous range while the second chooses them among prespecified discrete levels. Especially developed mathematical models are used to solve the problem in either approach. For the second linearization approach, a constraint programming model, and a hybrid algorithm based on the fix-and-optimize and variable neighborhood search metaheuristic (FOVNS, for short) are additionally developed. Changing the branching procedure as a technique and three dominance rules are also proposed to improve the performance of the CP and FOVNS models while their effectiveness is examined using the full factorial design of experiments. Also, the parameters of the FOVNS are tuned using the Taguchi method. Exact methods are found capable of optimizing medium-size problems in less than an hour while FOVNS is able to optimize large-size ones in 822 seconds on average with a deviation of 1.8% from the optimal solution. Statistical analysis show that considering a time-dependent energy price in the scheduling decreases the energy cost by about 40%.

Power consumption prediction in cloud data center using machine learning

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

Effects of Machining Parameters on the Quality in Machining of Aluminium Alloys Thin Plates

Nowadays, the industry looks for sustainable processes to ensure a more environmentally friendly production. For that reason, more and more aeronautical companies are replacing chemical milling in the manufacture of skin panels and thin plates components. This is a challenging operation that requires meeting tight dimensional tolerances and differs from a rigid body machining due to the low stiffness of the part. In order to fill the gap of literature research on this field, this work proposes an experimental study of the effect of the depth of cut, the feed rate and the cutting speed on the quality characteristics of the machined parts and on the cutting forces produced during the process. Whereas surface roughness values meet the specifications for all the machining conditions, an appropriate cutting parameters selection is likely to lead to a reduction of the final thickness deviation by up to 40% and the average cutting forces by up to a 20%, which consequently eases the clamping system and reduces machine consumption. Finally, an experimental model to control the process quality based on monitoring the machine power consumption is proposed.

Real‐time power monitoring using field‐programmable gate array with IoT technology

Automation technologies are often impacted undesirably by downtime caused by machine failures. An approach to overcome machine failures is through failures prediction, achievable via the monitoring of power consumption. Unusual power consumption could serve as an alert for possible machines faults. Real-time power consumption monitoring is made possible with the deployment of Internet of Things (IoT), which brings together digital cloud computing and conventional automation technology to rapidly increase the efficiency of production. In this work, the authors have developed an IoT-based power monitoring system to detect unusual power consumption of machines and present the data to users remotely through Android application and cloud storage. The competent integration of Wi-Fi module ESP8266, CT sensor module and field-programmable gate array DE1-SoC board allows the system to accurately measure power consumption and instantaneously store the data in cloud storage. Performance of the developed system was characterised by current measurement of five different loads. The absolute errors, given by the difference between current measured by digital multimeter and the developed system, range from 0.01 to 0.26 A. The close agreement between the two measurements endorses the accuracy and reliability of the real-time power monitoring system, on top of its easy-accessibility gifted by IoT.

PENGARUH GETARAN PADA MOTOR SAMPLING PUMP ROUGHER SCAVENGER TERHADAP KONSUMSI DAYA LISTRIK DI PT NEWMONT NUSA TENGGARA

The purpose of this research is to know the influence of vibration that occur on a machine on its power consumption along with knowing the damage that made the machine vibrated.This research conducted at PT Newmont Nusa Tenggara on Concentrator Area. Sampling pump rougher scavenger machine is used as an experiment devices. Microlog CMVA55 with accelerometer sensor was used for measuring vibration velocity and data logger was used for power consumption measurer. The vibration velocity value was taken on five point in sampling pump rougher scavenger machine and the power consumption value was taken in electrical power room. Both of the measuring result will be compared to find the influence of vibration toward machine power consumption. Fish bone analysis was used to found out the root cause of the excessive vibration on the machine. The solution of the vibration problem was found by using Failure Mode and Effect Analysis method. The results showed there isan influence toward machine power consumption by the increase of vibration velocity. For 0.06 mm/s vibration velocity excalation, the power consumption increase to 40 watt per hour. For 0.08 mm/s, power consumption increase to 80 watt per hour and the highest is 324 watt per hour with vibration velocity excalation up to 0.36 mm/s. The highest power consumption 17 kWh occurred on machine A with vibration velocity average is 6.12 mm/s and the lowest occurred on machine B that is 15 kWh with vibration velocity average is 2.99 mm/s.Keywords: vibration, vibration velocity, vibration spectrum, power consumption, mechanical vibration, spectrum analyze.

Performance Evaluation of New Sugarcane Peeling Machine Prototype

The present investigation was carried out to develop and evaluate the performance of a new small-scale sugarcane peeler machine. The developed machine prototype was tested through real experiments carried out at a sugarcane private store in Kafrelsheikh province during 2014/2015 season. The experiments illustrated the effects of the main design and operating parameters, on the machinery performance and finished product quality. The investigated parameters included three different peeling drum brush types namely: zigzag, straight and spiral, four peeling drum speeds of 3.53, 5.30, 7.47 and 9.18m/s, three peeling drum clearances of 1.0, 1.5 and 2.0cm and three feeding rates of 3, 6 and 9 canes/min. The developed machine performance were evaluated in terms of: machine production efficiency, cane stalks peeling efficiency, peel retention on peeled stalks, cane stalks weight losses, machine power consumption and machine unit cost. The gained results revealed in general that using the zigzag peeling drum brush type tends to improve the peeling performance of the developed machine, compared to straight and spiral brush types. In addition, the obtained results indicated that, the maximum machine production efficiency (88.85%) and the minimum electrical power consumption (5.56kW), were achieved at peeling drum speeds of 9.18 and 3.53m/s, peeling drum clearance of 2cm and No. of feeding canes per minute of 3 canes, respectively. Moreover, the maximum peeling efficiency, the minimum cane stalks losses and the lowest percentage of the peel retention on peeled stalks were 91.40%, 3.02% and 2.46%, respectively. These percentages were recorded at peeling drum speed of 9.18m/s, peeling drum clearance of 1cm and 3 canes/min feeding rate. Also, the minimum machinery unit cost was 67.49LE/Mg at peeling drum speed of 3.53m/s, drum clearance of 1.5cm and 9 cans/min feeding rate.

PMC-based power measuring technique in virtualisation environments

Recently, resource virtualisation technology has been widely applied in many high-performance datacentres. However, measuring the power of individual virtual machine is still a challenging issue. In this work, we proposed a novel technique which uses performance monitor counters PMC for power metering on per-VM basis. Unlike the classical utilisation-based techniques which can only provide coarse-grained power metering with unbounded error, our approach can increase the accuracy when metering virtual machine power consumption by bounded error range. The experiments conducted by various benchmarks on different platforms show that the proposed approach outperforms existing approaches in terms of accuracy and power-efficiency.

Prediction Model for Virtual Machine Power Consumption in Cloud Environments

Power consumption has become a crucial issue in cloud computing environments because of environmental and financial concerns. It is necessary to estimate individual virtual machine power consumption to enforce efficient power aware policies in cloud. Existing solutions are built on linear power models to infer power consumption through VM resource utilization. However, linear models do not capture dependencies among multiple parameters and hence they do not ensure prediction accuracy across multiple workloads. In this paper, a non-linear support vector regression based power model using performance monitor counters is proposed to predict individual virtual machine power consumption. Experimental results with various standard benchmark workloads demonstrate that the prediction accuracy of proposed approach is better than the existing linear regression based power model.

Optimal workpiece orientation to reduce the energy consumption of a milling process

Energy consumption in the manufacturing sector is becoming a very hot topic due to its significant ecological relevance, especially for energy intensive processes such as machining. Machining finds nowadays large application mainly due to its high performance, in terms of both surface finish and tolerances achievable; as an example this is the key technology in dies and molds production, largely used in the automotive and housewares sectors. Process optimization could be carried out using different strategies, as already proposed by many authors, such as by optimizing machining parameters or implementing alternative toolpath capable of reducing both machining time and energy consumption. Within this paper will be presented a novel approach that takes into account the product orientation within the working zone of the machine. Milling machines are usually non symmetric regarding the energy consumptions of the axes due to the different masses that have to be moved, hence product orientation could sensibly affect energy consumption in performing a toolpath. Optimizing product orientation has the advantage not to affect product quality and require no adjustment of machining parameters. An approach to model the machine power consumption and to optimize the workpiece orientation is presented together with the results of validation experiments.

Power-level sampling of metal cutting machines for data representation in discrete event simulation

An extension to the application area for discrete event simulation (DES) has been ongoing since the last decade and focused only on economic aspects to include ecologic sustainability. With this new focus, additional input parameters, such as electrical power consumption of machines, are needed. This paper aim at investigating how NC machine power consumption should be represented in simulation models of factories. The study includes data-sets from three different factories. One factory producing truck engine blocks, one producing brake disc parts for cars and one producing forklift components. The total number of data points analysed are more than 2,45,000, where of over 1,11,000 on busy state for 11 NC machines. The low variability between busy cycles indicates that statistical representations are not adding significant variability. Furthermore, results show that non-value-added activities cause a substantial amount of the total energy consumption, which can be reduced by optimising the production flow using dynamic simulations such as DES.

Estimating Machine Power Consumptions through Aggregated Measurements and Machine Data Acquisition

Monitoring the electric power consumption of machines in manufacturing becomes more and more important due to rising electricity costs and environmental awareness. This paper proposes a cost-efficient monitoring concept requiring significantly fewer electricity meters. Power consumption of several machines is monitored aggregately and then broken down into the estimated power consumption of each machine by means of a disaggregation procedure utilizing machine data acquisition. In this paper the disaggregation procedure is presented, uncertainty limitations are discussed and an application example of an industrial environment is given.

Power consumption estimation of CPU and peripheral components in virtual machines

Energy consumption of IT increased continuously during the last decades. Numerous works have been accomplished for improving energy efficiency of hardware whereas software energy efficiency has been ignored for a long time. This contribution presents a novel approach for estimating energy consumption of computer systems in dependency of software-caused workloads in different execution environments. The system is the basis for automatic optimization of software execution in an energy-efficient way by finding the best-suiting host computer (and best-suiting peripheral devices). Thus, it opens novel ways to further improve energy-efficiency of IT systems by migrating software-caused load to an energy-efficient target. Exemplary, the approach is tested in a virtualized data center environment, where virtual machines are the applications. The presented approach is a vehicle for automatically computing an energy-efficient virtual machine placement. The paper presents a new algorithm for estimating virtual machine power consumption, which consists of CPU power consumption estimation as well as power usage estimation of peripheral components like hard disk drive and network interface controller. The accuracy of the presented approach is proved by means of measurements.