Power and environment aware control of Beowulf clusters

Beowulf clusters are now deployed worldwide, chiefly in support of scientific computing. Beowulf clusters yield high computing performance, yet they also pose several challenges: (1) heat-induced hardware failure makes large scale commodity clusters fail quite frequently and (2) cost effectiveness of the Beowulf cluster is challenged by the fact that it lacks means of adapting its power state according to varying work load. This paper addresses these issues by developing a Power and Environment Awareness Module (PEAM) for a Beowulf cluster. The busty nature of computation load in an academic environment inspired the implementation and analysis of a fixed timeout Dynamic Power Management (DPM) policy. Today it is common that many Beowulf clusters in academic environment are composed of older, recycled nodes that may lack of out-of-band management technologies, thus Advanced Configuration and Power Interface (ACPI) and Wake-on-LAN (WOL) technology is exploited to control the power state of cluster nodes. A data center environment monitoring system that uses Wireless Sensor Networks (WSN) technology is developed and deployed to realize environment awareness of the cluster. Our PEAM module has been implemented on our cluster at Purdue University, reducing the operational cost and increasing the reliability of the cluster by reducing heat generation and optimizing workload distribution in an environment aware manner.     

Power management by load forecasting in web server clusters

The complexity and requirements of web applications are increasing in order to meet more sophisticated business models (web services and cloud computing, for instance). For this reason, characteristics such as performance, scalability and security are addressed in web server cluster design. Due to the rising energy costs and also to environmental concerns, energy consumption in this type of system has become a main issue. This paper shows energy consumption reduction techniques that use a load forecasting method, combined with DVFS (Dynamic Voltage and Frequency Scaling) and dynamic configuration techniques (turning servers on and off), in a soft real-time web server clustered environment. Our system promotes energy consumption reduction while maintaining user’s satisfaction with respect to request deadlines being met. The results obtained show that prediction capabilities increase the QoS (Quality of Service) of the system, while maintaining or improving the energy savings over state-of-the-art power management mechanisms. To validate this predictive policy, a web application running a real workload profile was deployed in an Apache server cluster testbed running Linux.     

Green flexible opportunistic computing with task consolidation and virtualization

Energy efficiency and high computing power are basic design considerations across modern-day computing solutions due to different concerns such as system performance, operational cost, and environmental issues. Desktop Grid and Volunteer Computing System (DGVCS) so called opportunistic infrastructures offer computational power at low cost focused on harvesting idle computing cycles of existing commodity computing resources. Other than allowing to customize the end user offer, virtualization is considered as one key techniques to reduce energy consumption in large-scale systems and contributes to the scalability of the system. This paper presents an energy efficient approach for opportunistic infrastructures based on task consolidation and customization of virtual machines. The experimental results with single desktops and complete computer rooms show that virtualization significantly improves the energy efficiency of opportunistic grids compared with dedicated computing systems without disturbing the end-user.     

Rack aware scheduling in HPC data centers: an energy conservation strategy

Energy consumption in high performance computing data centers has become a long standing issue. With rising costs of operating the data center, various techniques need to be employed to reduce the overall energy consumption. Currently, among others there are techniques that guarantee reduced energy consumption by powering on/off the idle nodes. However, most of them do not consider the energy consumed by other components in a rack. Our study addresses this aspect of the data center. We show that we can gain considerable energy savings by reducing the energy consumed by these rack components. In this regard, we propose a scheduling technique that will help schedule jobs with the above mentioned goal. We claim that by our scheduling technique we can reduce the energy consumption considerably without affecting other performance metrics of a job. We implement this technique as an enhancement to the well-known Maui scheduler and present our results. We propose three different algorithms as part of this technique. The algorithms evaluate the various trade-offs that could be possibly made with respect to overall cluster performance. We compare our technique with various currently available Maui scheduler configurations. We simulate a wide variety of workloads from real cluster deployments using the simulation mode of Maui. Our results consistently show about 7 to 14 % savings over the currently available Maui scheduler configurations. We shall also see that our technique can be applied in tandem with most of the existing energy aware scheduling techniques to achieve enhanced energy savings. We also consider the side effects of power losses due to the network switches as a result of deploying our technique. We compare our technique with the existing techniques in terms of the power losses due to these switches based on the results in Sharma and Ranganathan, Lecture Notes in Computer Science, vol. 5550, 2009 and account for the power losses. We there on provide a best fit scheme with the rack considerations. We then propose an enhanced technique that merges the two extremes of node allocation based on rack information. We see that we can provide a way to configure the scheduler based on the kind of workload that it schedules and reduce the effect of job splitting across multiple racks. We further discuss how the enhancement can be utilized to build a learning model which can be used to adaptively adjust the scheduling parameters based on the workload experienced.     

Power consumption evaluation of different fed-batch strategies for enzymatic hydrolysis of sugarcane bagasse

The minimization of costs in the distillation step of lignocellulosic ethanol production requires the use of a high solids loading during the enzymatic hydrolysis to obtain a more concentrated glucose liquor. However, this increase in biomass can lead to problems including increased mass and heat transfer resistance, decreased cellulose conversion, and increased apparent viscosity with the associated increase in power consumption. The use of fed-batch operation offers a promising way to circumvent these problems. In this study, one batch and four fed-batch strategies for solids and/or enzyme feeding during the enzymatic hydrolysis of sugarcane bagasse were evaluated. Determinations of glucose concentration, power consumption, and apparent viscosity were made throughout the experiments, and the different strategies were compared in terms of energy efficiency (mass of glucose produced according to the energy consumed). The best energy efficiency was obtained for the strategy in which substrate and enzyme were added simultaneously (0.35 kg_glucose kWh^?1). This value was 52 % higher than obtained in batch operation.     

MapReduce framework energy adaptation via temperature awareness

MapReduce has become a popular framework for Big Data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. Our goal with this paper is to explore the possibility of scheduling in a power-efficient manner without the need for expensive power monitors on every node. We begin by considering that no cluster is truly homogeneous with respect to energy consumption. From there we develop a MapReduce framework that can evaluate the current status of each node and dynamically react to estimated power usage. In so doing, we shift work toward more energy efficient nodes which are currently consuming less power. Our work shows that given an ideal framework configuration, certain nodes may consume only 62.3 % of the dynamic power they consumed when the same framework was configured as it would be in a traditional MapReduce implementation.     

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.     

Environmental impact assessment of centralized municipal wastewater management in Thailand

Purpose

Urbanization and industrial development intensify water utilization and wastewater generation. The efficiency of wastewater treatment systems varies and depends on system design and wastewater condition. The research aims to examine seven existing centralized municipal wastewater treatment plants (WWTPs) in Bangkok to discover which system configuration yields the best environmental and economic performance. The degree of environmental impact and operational costs from different system designs were investigated to help select future wastewater treatment systems.

Methods

Life cycle assessment (LCA) has been conducted to evaluate environmental impacts from centralized municipal wastewater treatment systems. Life cycle impact assessment method based on endpoint modeling (LIME) was applied, with three major potential environmental impact categories including eutrophication, global warming, and acidification. All seven centralized municipal WWTPs in Bangkok were investigated as case studies. The system configurations are classified into five types of activated sludge (AS) systems. The contribution of impacts from individual processes in each type of AS system was analyzed. The methodology covered major on-site and off-site operational processes excluding construction and maintenance phases. Average annual data were calculated to develop an inventory dataset. JEMAI-Pro software was utilized in this study to analyze the life cycle impact of the systems.

Results and discussion

The level of environmental impact from a WWTP depends on the configuration of the AS system. The highest potential environmental impact from a municipal WWTP is eutrophication, which is obviously affected by ammonium and phosphorous discharges into water bodies. The vertical loop reactor activated sludge (VLRAS) system yielded the best treatment performance among the five AS sub-systems. The consumption of electricity used to operate the system contributed significantly to global warming potential and correlated considerably with operating costs. Comparing among three system sizes, the large-scale WWTP revealed inefficient electricity consumption, whereas the medium plant provided better performance in chemical use and operating costs.

Conclusions

Centralized municipal WWTPs with capacities ranging from 10 to 350?×?10³ m³/day were evaluated with respect to environmental performance and costs during the operating phase. Among all case studies, a medium-scale WWTP with a VLRAS system offered the best operating performance in terms of low environmental impact, resource consumption, and cost. To enhance WWTP management, it is vital to improve the efficiency of electricity consumption in primary and secondary treatment processes and increase wastewater collection efficiency to maximize the plant operating capacity and minimize overall environmental impacts.

     

Conventional and exergetic life cycle assessment of organic rankine cycle implementation to municipal waste management: the case study of Mae Hong Son (Thailand)

Purpose

The critical issue of waste management in Thailand has been rapidly increasing in almost all of the cities due to the economic growth and rising population that could double the amount of solid waste in landfill area. The alternative ways of waste treatment that have more efficiency and effectiveness in terms of energy, ecology, and resources become the key issue for each municipality to replace the old fashioned technology and be able to enhance the ability of solid waste problem management. Waste to energy is one of the favorable approaches to diminish the amount of waste to landfill and utilize waste for electricity. The aim of this study is to identify and quantify the life cycle impacts of the municipal solid waste (MSW) of Mae Hong Son municipality (MHSM), and the case study is the selected waste treatment technology of the Refuse-Derived Fuel (RDF) hybrid with 20 kW of Organic Rankine Cycle (ORC).

Methods

The functional unit is defined as 1 t of MSW. The energy, environment, and resource impacts were evaluated by using Life Cycle Assessment (LCA); ReCipe and Net Energy Consumption were referred to calculate the environmental impacts and the benefits of energy recovery of WtE technology. Exergetic LCA was used to analyze the resource consumption, especially land use change.

Results and discussion

The results indicated that the environmental impacts were comparatively high at the operation stage of RDF combustion. On the other hand, the production stage of RDF illustrated the highest energy consumption. The ORC power generation mainly consumed resources from material and energy used. The ORC system demonstrated better results in terms of energy and resource consumption when applied to waste management, especially the land required for landfill. Substitution of electricity production from ORC system was the contributor to the reduction of both energy and resource consumption. Installation of spray dry and fabric filter unit to RDF burner can reduce heavy metals and some pollutants leading to the reduction of most of the impacts such as climate change, human toxicity, and fossil depletion which are much lower than the conventional landfill.

Conclusions

LCA results revealed that the environmental impacts and energy consumption can be reduced by applying the RDF and ORC systems. The exergetic LCA is one of the appropriate tools used to evaluate the resource consumption of MSW. It is obviously proven that landfill contributed to higher impacts than WtE for waste management.

     

VPM tokens: virtual machine-aware power budgeting in datacenters

Power consumption and cooling overheads are becoming increasingly significant for enterprise datacenters, affecting overall costs and the ability to extend resource capacities. To help mitigate these issues, active power management technologies are being deployed aggressively, including power budgeting, which enables improved power provisioning and can address critical periods when power delivery or cooling capabilities are temporarily reduced. Given the use of virtualization to encapsulate application components into virtual machines (VMs), however, such power management capabilities must address the interplay between budgeting physical resources and the performance of the virtual machines used to run these applications. This paper proposes a set of management components and abstractions for use by software power budgeting policies. The key idea is to manage power from a VM-centric point of view, where the goal is to be aware of global utility tradeoffs between different virtual machines (and their applications) when maintaining power constraints for the physical hardware on which they run. Our approach to VM-aware power budgeting uses multiple distributed managers integrated into the VirtualPower Management (VPM) framework whose actions are coordinated via a new abstraction, termed VPM tokens. An implementation with the Xen hypervisor illustrates technical benefits of VPM tokens that include up to 43% improvements in global utility, highlighting the ability to dynamically improve cluster performance while still meeting power budgets. We also demonstrate how VirtualPower based budgeting technologies can be leveraged to improve datacenter efficiency in the context of cooling infrastructure management.

A comparative study on resource allocation and energy efficient job scheduling strategies in large-scale parallel computing systems

In the large-scale parallel computing environment, resource allocation and energy efficient techniques are required to deliver the quality of services (QoS) and to reduce the operational cost of the system. Because the cost of the energy consumption in the environment is a dominant part of the owner’s and user’s budget. However, when considering energy efficiency, resource allocation strategies become more difficult, and QoS (i.e., queue time and response time) may violate. This paper therefore is a comparative study on job scheduling in large-scale parallel systems to: (a) minimize the queue time, response time, and energy consumption and (b) maximize the overall system utilization. We compare thirteen job scheduling policies to analyze their behavior. A set of job scheduling policies includes (a) priority-based, (b) first fit, (c) backfilling, and (d) window-based policies. All of the policies are extensively simulated and compared. For the simulation, a real data center workload comprised of 22385 jobs is used. Based on results of their performance, we incorporate energy efficiency in three policies i.e., (1) best result producer, (2) average result producer, and (3) worst result producer. We analyze the (a) queue time, (b) response time, (c) slowdown ratio, and (d) energy consumption to evaluate the policies. Moreover, we present a comprehensive workload characterization for optimizing system’s performance and for scheduler design. Major workload characteristics including (a) Narrow, (b) Wide, (c) Short, and (d) Long jobs are characterized for detailed analysis of the schedulers’ performance. This study highlights the strengths and weakness of various job scheduling polices and helps to choose an appropriate job scheduling policy in a given scenario.     

Real-time environmental assessment of electricity use: a tool for sustainable demand-side management programs

Purpose

Demand-side management is a promising way to increase the integration of renewable energy sources by adapting part of the demand to balance power systems. However, the main challenges of evaluating the environmental performances of such programs are the temporal variation of electricity generation and the distinction between generation and electricity use by including imports and exports in real-time.

Methods

In this paper, we assessed the environmental impacts of electricity use in France by developing consumption factors based on historical hourly data of imports, exports, and electricity generation of France, Germany, Great Britain, Italy, Belgium, and Spain. We applied a life cycle approach with four environmental indicators: climate change, human health, ecosystem quality, and resources. The developed dynamic consumption factors were used to assess the environmental performances of demand-side management programs through optimized changes in consumption patterns defined by the flexibility of 1 kWh every day in 2012–2014.

Results and discussion

Between 2012 and 2014, dynamic consumption factors in France were higher on average than generation factors by 21.8% for the climate change indicator. Moreover, the dynamic consideration of electricity generation of exporting countries is essential to avoid underestimating the impacts of electricity imports and therefore electricity use. The demand response programs showed a range of mitigation up to 38.5% for the climate change indicator. In addition, an environmental optimization cost 1.4 € per kg CO2 eq. for 12% mitigation of emissions as compared to an economic optimization. Finally, embedding the costs of some environmental impacts in the electricity price with a carbon price enhanced the efficiency of economic demand response strategies on the GHG emissions mitigation.

Conclusions

The main scientific contribution of this paper is the development of more accurate dynamic electricity consumption factors. The dynamic consumption factors are relevant in LCAs of industrial processes or operational building phases, especially when consumption varies over time and when the power system participates in a wide market with exports and imports such as in France. In the case of demand-side management programs, dynamic consumption factors could prevent an environmentally damaging energy from being imported, despite the economic interest of system operators. However, the approach used in this study was attributional and did not assess the local grid responses of load shifting programs. Therefore, a more comprehensive model could be created to assess the local short-term dynamic consequences of located prospective consumptions and the global long-term consequences of demand-side management programs.

     

A Comparison of the Energy Yield at the End User for M. x giganteus Using Two Different Harvesting and Transport Systems

A comparison between two different harvest systems for Miscanthus x giganteus crop (direct cut/chip and mow/bale) in terms of the net energy delivered to an end user, and the various energy costs and energy yields associated with each system was conducted. Only minor differences in terms of energy consumption were observed between the two harvest systems when all phases of the harvesting chain had been taken into account. Chip harvesting consumed 0.11 GJ?t^?1 compared with 0.13 GJ?t^?1 for bale harvesting. Chip transportation was considerably more expensive than bale transportation for a set distance of 50 km (0.18 and 0.11 GJ?t^?1 for chip and bale, respectively). Despite this, higher overall net energy yield was achieved by direct cutting and chipping the material. This was due to the higher proportion of harvestable energy lost in the field as a result of the use of a mowing/baling system. The overall net energy delivered in terms of harvestable material by the direct cut and chip system was 12.45 GJ?t^–1 compared with 11.78 GJ?t^?1 by the mow and bale system, making direct cut the more efficient system even up to a transport distance of 400 km. A sensitivity analysis indicated that the choice of transport system becomes more important for energy efficiency as transport distance increases.     

Life-cycle assessment of a hydrogen-based uninterruptible power supply system using renewable energy

Purpose

The paper provides an empirical assessment of an uninterruptible power supply (UPS) system based on hydrogen technologies (HT-UPS) using renewable energy sources (RES) with regard to its environmental impacts and a comparison to a UPS system based on the internal combustion engine (ICE-UPS).

Methods

For the assessment and comparison of the environmental impacts, the life-cycle assessment (LCA) method was applied, while numerical models for individual components of the UPS systems (electrolyser, storage tank, fuel cell and ICE) were developed using GaBi software. The scope of analysis was cradle-to-end of utilisation with functional unit 1 kWh of uninterrupted electricity produced. For the life-cycle inventory analysis, quantitative data was collected with on-site measurements on an experimental system, project documentation, GaBi software generic databases and literature data. The CML 2001 method was applied to evaluate the system’s environmental impacts. Energy consumption of the manufacturing phase was estimated from gross value added (GVA) and the energy intensity of the industry sector in the manufacturer’s country.

Results and discussion

In terms of global warming (GW), acidification (A), abiotic depletion (AD) and eutrophication (E), manufacturing phase of HT-UPS accounts for more than 97 % of environmental impacts. Electrolyser in all its life-cycle phases contributes above 50 % of environmental impacts to the system’s GW, A and AD. Energy return on investment (EROI) for the HT-UPS has been calculated to be 0.143 with distinction between renewable (roughly 60 %) and non-renewable energy resources inputs. HT-UPS’s life-cycle GW emissions have been calculated to be 375 g of CO₂ eq per 1 kWh of uninterruptible electric energy supplied. All these values have also been calculated for the ICE-UPS and show that in terms of GW, A and AD, the ICE-UPS has bigger environmental impacts and emits 1,190 g of CO₂ eq per 1 kWh of uninterruptible electric energy supplied. Both systems have similar operation phase energy efficiency. The ICE-UPS has a higher EROI but uses almost none RES inputs.

Conclusions

The comparison of two different technologies for providing UPS has shown that in all environmental impact categories, except eutrophication, the HT-UPS is the sounder system. Most of HT-UPS’s environmental impacts result from the manufacturing phase. On the contrary, ICE-UPS system’s environmental impacts mainly result from operational phase. Efficiency of energy conversion from electricity to hydrogen to electricity again is rather low, as is EROI, but these will likely improve as the technology matures.     

Improving the Profitability of Willow Crops��Identifying Opportunities with a Crop Budget Model

Short-rotation woody crops like shrub willow are a potential source of biomass for energy generation and bioproducts. However, since willow crops are not widely grown in North America, the economics of this crop and the impacts of key crop production and management components are not well understood. We developed a budget model, EcoWillow v1.4 (Beta), that allows users to analyze the entire production-chain for willow systems from the establishment to the delivery of wood chips to the end-user. EcoWillow was used to analyze how yield, crop management options, land rent, fuel, labor, and other costs influence the Internal Rate of Return (IRR) of willow crop systems in upstate New York. We further identified cost variables with the greatest potential for reducing production and transport costs of willow biomass. Productivity of 12 oven-dried tons (odt) ha^?1 year^?1 and a biomass price of $ (US dollars) 60 odt^?1 results in an IRR of 5.5%. Establishment, harvesting, and transportation operations account for 71% of total costs. Increases in willow yield, rotation length, and truck capacity as well as a reduction in harvester down time, land costs, planting material costs, and planting densities can improve the profitability of the system. Results indicate that planting speed and fuel and labor costs have a minimal effect on the profitability of willow biomass crops. To improve profitability, efforts should concentrate on (1) reducing planting stock costs, (2) increasing yields, (3) optimizing harvesting operations, and (4) co-development of plantation designs with new high-yielding clones to reduce planting density.     

Embedded Real-time Battery State-of-Charge Forecasting in Micro-Grid Systems

Micro-grid systems (MGS) are increasingly investigated for green and energy efficient buildings in order to reduce energy consumption while maintaining occupants’ comfort. It includes renewable energy sources for power production, storage devices for storing power excess, and control strategies for orchestrating all components and improving the system's efficiency. In fact, MGS can be seen as complex systems composed of different heterogeneous entities that interact dynamically and in collective manner in order to balance between energy efficiency and occupants’ comfort. However, the uncertainty and intermittency of energy production and consumption requires the development of real-time forecasting methods and predictive control strategies. The State-of-Charge (SoC) of batteries is one of the main parameters used in MGS predictive control algorithms. It indicates how much energy is stored and how long MGS can be relying on deployed storage devices. Several methods have been developed for SoC estimation, but little work, however, has been dedicated for SoC forecasting in MGS. In this paper, we focus on advancing MGS predictive control through near real-time embedded forecasting of batteries SoC. In fact, we have deployed, on two platforms, two forecasting methods, Long Short-Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA). Their accuracy and performance have been evaluated in both classical batch mode and streaming mode. Extensive experiments have been conducted for different forecasting horizons and results are presented using two main metrics, the accuracy and the computational time. Obtained results show that LSTM outperforms ARIMA for real-time forecasting, it has the better tradeoff in terms of forecasting accuracy and performance.     

Design and implementation of an integrated management system in a plant factory to save energy

Recently, various studies have been conducted to stabilize food production and improve product quality and energy saving efficiency in the face of climate change by integrating ICT (Information and Communication Technology) into existing agricultural technologies. A representative example of this technology, the fully artificial plant factory, facilitates a high degree of environment control and growth prediction based on the cultivation environment and monitoring of crop growth, and has the advantage that environment-friendly crops are cultured. However, the concept has met difficulties in entering the market due to the large investment in facilities and expensive operation costs involved. There are many considerations, including calculating the optimal environment parameters for plants, designing and controlling artificial lights characterized by high-level efficiency and low power consumption, and selecting value-added crops. Among others parameters, those of the optimal environment may be utilized as a very important input element for maximizing plant growth and minimizing energy injection costs. To this end, data, including those related to environment, growth, and energy, will be monitored in real time, and integration management systems will be developed in advance to realize the effective control of connected devices based on the concept of the fully artificial plant factory. However, existing systems are designed for horticultural facilities and have been operated by simply monitoring environment or growth information or individually controlling each device through different interface environments, without considering the energy consumption of the devices. The purpose of this study is to design a method for monitoring in real time integrated environment and growth data and the energy consumption of the devices in a fully artificial plant factory, and to design and implement a plant factory integration management system that actively controls devices based on these data. In the future, the environment/growth/energy data collected from sensors in the proposed system will allow the optimal environment parameters to be calculated for each crop through correlation analysis and each device to be integrated and controlled, contributing to an increase in crop productivity and quality, as well as overall energy consumption efficiency in the plant factory. In addition, the database information collected from the system and then processed will be useful as input data for the integrated database of the SSN (Social Sensor Network) or the intelligent plant factory system.     

Effect of Treatment with Cadmium on Structure-Function Relationships in Rat Liver Mitochondria: Studies on Oxidative Energy Metabolism and Lipid/Phospholipids Profiles

Effects of treatment with a single intraperitoneal injection of cadmium (Cd) on oxidative energy metabolism and lipid/phospholipid profiles of rat liver mitochondria were examined at the end of 1 week and 1 month. Following Cd treatment the body weight increased only in the 1 month group, whereas the liver weight increased in both groups. State 3 and 4 respiration rates in general decreased significantly, with the maximum effect being seen with succinate. The 1 week Cd group showed decreased respiratory activity with glutamate, pyruvate + malate, and succinate as the substrates. In the 1 month Cd-treated group respiration rates recovered with glutamate and pyruvate + malate but not with succinate. All cytochrome contents decreased in the 1 week Cd-treated group but recovered in the 1 month group. ATPase activity registered an increase in both Cd-treated groups. Dehydrogenase activities increased in the 1 week group but decreased in the 1 month Cd-treated group. The mitochondrial cholesterol content increased in the 1 week Cd-treated group. In the 1 week Cd-treated group the lysophospholipid (Lyso), sphingomyelin (SPM), and diphosphatidylglycerol (DPG) components increased. By contrast, the phosphatidylethanolamine (PE) component decreased. In the 1 month Cd-treated group the phosphatidylinositol, phosphatidylserine, and DPG components increased, whereas the Lyso, SPM, and phosphatidylcholine components decreased. The results demonstrate that single-dose Cd treatment can have adverse effects on liver mitochondrial oxidative energy metabolism and lipid/phosphopholipid profiles, which in turn can affect membrane structure-function relationships.     

Energy efficient job scheduling with workload prediction on cloud data center

Data centers are the backbone of cloud infrastructure platform to support large-scale data processing and storage. More and more business-to-consumer and enterprise applications are based on cloud data center. However, the amount of data center energy consumption is inevitably lead to high operation costs. The aim of this paper is to comprehensive reduce energy consumption of cloud data center servers, network, and cooling systems. We first build an energy efficient cloud data center system including its architecture, job and power consumption model. Then, we combine the linear regression and wavelet neural network techniques into a prediction method, which we call MLWNN, to forecast the cloud data center short-term workload. Third, we propose a heuristic energy efficient job scheduling with workload prediction solution, which is divided into resource management strategy and online energy efficient job scheduling algorithm. Our extensive simulation performance evaluation results clearly demonstrate that our proposed solution has good performance and is very suitable for low workload cloud data center.     

Transparency Built‐in

The supply chains found in modern manufacturing are often complex and long. The resulting opacity poses a significant barrier to the measurement and minimization of energy consumption and therefore to the implementation of sustainable manufacturing. The current article investigates whether the adoption of additive manufacturing (AM) technology can be used to reach transparency in terms of energy and financial inputs to manufacturing operations. AM refers to the use of a group of electricity‐driven technologies capable of combining materials to manufacture geometrically complex products in a single digitally controlled process step, entirely without molds, dies, or other tooling. The single‐step nature affords full measurability with respect to process energy inputs and production costs. However, the parallel character of AM (allowing the contemporaneous production of multiple parts) poses previously unconsidered problems in the estimation of manufacturing resource consumption. This research discusses the implementation of a tool for the estimation of process energy flows and costs occurring in the AM technology variant direct metal laser sintering. It is demonstrated that accurate predictions can be made for the production of a basket of sample parts. Further, it is shown that, unlike conventional processes, the quantity and variety of parts demanded and the resulting ability to fully utilize the available machine capacity have an impact on process efficiency. It is also demonstrated that cost minimization in additive manufacturing may lead to the minimization of process energy consumption, thereby motivating sustainability improvements.