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Evaluating the upper bound of energy cost saving by proactive data center management

Evaluating the upper bound of energy cost saving by proactive data center management

The last two studies, [25] and [26] have the same final goal as ours, i.e., to improve energy efficiency in DCs by turning off the unused machines. However, they differ in the way the goal is to be reached. Both [25] and [26] predict the amount of resource requests in the next time interval to decide the number of machines that will be needed. The diverse applications with different priorities, performance and resource requirements together with the heterogeneity of both machines and workloads are taken into account to decide the number and capabilities of machines to serve the demand. In [25] an ARIMA model structure is used to predict the amount of requests while in [26], a Wiener adaptive filter scheme is employed. In this paper, our approach is different, since i) it is the energy consumed which is predicted and not the resource requests, and ii) the number of machines is computed from optimal linear and nonlinear predictors in order to maximize the energy cost saving and not to minimize the prediction error as in [26] or a quadratic cost taking into account the error and the reconfiguration cost as in [25]. Thus, instead of keeping a threshold of extra machines on, we design predictors that optimize this margin based on maximizing energy savings.
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Low pressure design for reducing energy cost of extractive distillation for separating Diisopropyl ether and Isopropyl alcohol

Low pressure design for reducing energy cost of extractive distillation for separating Diisopropyl ether and Isopropyl alcohol

We show how reducing pressure can improve the design of a 1.0-1a mixture homogeneous extractive distillation process and we use extractive efficiency indicators to compare the optimality of different designs. The case study concerns the separation of the diisopropyl ether (DIPE)–isopropyl alcohol (IPA) minimum boiling azeotrope with heavy entrainer 2- methoxyethanol. We first explain that the unexpected energy cost OF decrease following an increase of the distillate outputs is due to the interrelation of the two distillate flow rates and purities and the entrainer recycling through mass balance when considering both the extractive distillation column and the entrainer regeneration column. Then, we find that for the studied case a lower pressure reduces the usage of entrainer and increases the relative volatility of DIPE–IPA for the same entrainer content in the extractive column. A 0.4 atm operating pressure is selected to enable the use of cheap cooling water in the condenser. We run an optimization of the entrainer flow rate, both columns reflux ratios, distillates and feed locations by minimizing the total energy consumption per product unit. Double digit savings in energy consumption are achieved while TAC is reduced significantly. An extractive efficiency indicator that describes the ability of the extractive section to discriminate the desired product between the top and the bottom of the extractive section of the extractive section is calculated for comparing and explaining the benefit of lowering pressure on the basis of thermodynamic insight.
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Low pressure design for reducing energy cost of extractive distillation for separating Diisopropyl ether and Isopropyl alcohol

Low pressure design for reducing energy cost of extractive distillation for separating Diisopropyl ether and Isopropyl alcohol

In this work, we derive in Section 2 for the first time so far, the relation of the two distillates in the extractive distillation process to rationally select adequate purities and distillate flow rates and to anticipate the maximum impurity content in the entrainer recycle stream. It allows us to later explain the non-intuitive behavior that the energy cost decreases fol- lowing the increase of the distillate output flows. Then we investigate in Section 3 whether reducing the pressure is worth for the extractive distillation of the DIPE–IPA minimum boiling azeotrope with 2-methoxyethanol heavy entrainer sys- tem with the sake of reducing the energy cost and TAC for the separation process itself and whether it would be competitive with the best literature design proposed by Luo et al. (2014) . Our methodology is shown in Fig. 2 . It applies to all extrac- tive distillation process for the often encountered separation of minimum boiling azeotropes AB with a heavy entrainer E where the univolatility curve ˛ AB = 1 goes to the AE edge (1.0-
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DECA: a Dynamic Energy cost and Carbon emission-efficient Application placement method for Edge Clouds

DECA: a Dynamic Energy cost and Carbon emission-efficient Application placement method for Edge Clouds

Ehsan Ahvar, Shohreh Ahvar, Zoltan Adam Mann, Noel Crespi, Joaquin Garcia-Alfaro, and Roch Glitho, Abstract—As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emission-efficient Application placement method (DECA) for green ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the applicability and performance of DECA.
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V2B/V2G on energy cost and battery degradation under different driving scenarios, peak shaving, and frequency regulations

V2B/V2G on energy cost and battery degradation under different driving scenarios, peak shaving, and frequency regulations

battery cycling. Keywords: EV; V2B/V2G; peak shaving; frequency regulation; battery degradation; electricity bill 1. Introduction The energy stored in electric vehicles (EVs) would be made available to commercial buildings to actively manage energy consumption and costs in the near future. These concepts known as vehicle-to-X (V2X) (where X = home (H), building (B), or grid (G)) technologies have the potential to provide storage capacity to benefit EVs owners, grid companies, and buildings owners by reducing some of the high cost of EVs, reducing buildings’ energy cost, and providing reliable emergency backup services [ 1 , 2 ]. For example, Tesla S70 has 90 kWh battery capacity, and its daily consumption for trips is about 12.5% of this capacity (average daily distance for light vehicles is 53 km [ 3 ]). Hence of, 60 kWh can be used as an energy storage system after reduction of 20% depletion ratio.
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Analysis of the materials and energy cost to manufacture graphene by roll-based chemical vapor deposition

Analysis of the materials and energy cost to manufacture graphene by roll-based chemical vapor deposition

The demand for graphene is high, what remains outstanding is how to achieve the requisite levels of supply. While the adhesive tape method has appealing simplicity, it is time consuming and tends to produce graphene flakes of varying thickness. [7] There are promising methods of manufacture through additional mechanical means and also through various flavors of chemical vapor deposition. Even after the ideal production method is established though, if graphene is to live up to its plethora of applications, we must understand its cost of manufacture. This thesis seeks to provide a survey of existing production technologies, while maintaining focus on roll-to-roll chemical vapor deposition and its projected theoretical cost minima. The thesis will focus on cost of materials, energy, and operational costs based off process parameters in relation to the desired quality and size of graphene.
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A modelling approach for a cost-based evaluation of the energy produced by a marine energy farmA modelling approach for a cost-based evaluation of the energy produced by a marine energy farm

A modelling approach for a cost-based evaluation of the energy produced by a marine energy farmA modelling approach for a cost-based evaluation of the energy produced by a marine energy farm

Keywords: Marine energy, Energy cost, Site evaluation, Multi-criteria analysis, Decision tools, GIS 1. Introduction Oceans constitute an important source of renewable energy which remains today largely un- tapped. In order to face the increasing demand for energy and the public desire to produce energy with low environmental impact, and also to reduce dependence on fossil energies, the exploitation of marine resources generates a growing interest. In particular, it has been observed that marine renewable energy could make a signicant contribution to electricity production [1]. Several kinds of marine converters have been so far proposed for extracting marine energy from wave, tidal current, wind or exploiting thermal and salinity gradient. However, the choice of the best site and technological options for marine energy converters is a complex decision process involving several spatial and technical dimensions which interact to each other. First, the spa- tial component is closely related to the search of the best implantation site. Sites are chosen principally for their energetic potential and characteristics to accommodate a specic type of sys- tems. Secondly, the selected technologies must also be designed for an optimal exploitation of the available resources, while respecting the constraints of the environment. These constraints come
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How much does a VM cost? Energy-proportional Accounting in VM-based Environments

How much does a VM cost? Energy-proportional Accounting in VM-based Environments

C. User-oriented utilization On the user side, EPAVE can be used as an energy cost metric in order to evaluate the energy-efficiency of a given application running on given VM configuration. This metric can be used in combination with the classical metrics (duration, performance, QoS, etc.). By extrapolation, EPAVE can serve as a basis for a cost-benefit analysis including energy costs. Similarly, the energy costs can be used for comparing different VM configurations for a given application, and thus determine the desirable trade-off between QoS and energy consumption. Combined with energy-aware pricing models on the Cloud provider side, EPAVE can be an energy-aware incentive moti- vation. Energy-efficient users can be rewarded on the basis of their energy cost if they actively act towards its reduction. On the contrary, users can have an energy quota for running their VMs, which can be set by the provider or by the energy-aware user herself.
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Software consolidation as an efficient energy and cost saving solution for a SaaS/PaaS cloud model

Software consolidation as an efficient energy and cost saving solution for a SaaS/PaaS cloud model

Software Consolidation. The main problem with previous solutions is that they are limited by the footprint of the VMs consolidated (they are all operating systems). Execution of a VM requires a set of minimum resources, even if the application it runs is idle. Thus, we propose a solution which dynamically packs software into VMs to effectively use the overall VM resources while respecting the individual requirements of the different software applications. With current knowledge, [ 10 ] is the only previous work that studies dynamic software con- solidation on the same OS; however, it does not rely on VMs. [ 10 ] focuses on the MySQL database software and provides a live migration algorithm for that. This algorithm can be plugged into our framework. [ 10 ] (as well as Entropy [ 12 ]) describes a consolidation algorithm based on a CSP. Thus, no previous study has investigated software consolidation onto VMs. In this paper, we developed a working prototype and showed that it can achieve high VM utilization to provide cost and power-saving benefits.
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Cost and energy requirements of hybrid RO and ED brine concentration systems for salt production

Cost and energy requirements of hybrid RO and ED brine concentration systems for salt production

(Figure 8). Each stage shown in Fig. 8 represents a pairing of diluate and concentrate salinities ranging from 0 to 225 g/kg . McGovern et al. experimentally determined the specific process time, a proxy for the membrane area required to treat a given quantity of brine and the specific energy required for treatment. The model described in this work was used to simulate each stage test (Fig. 9a and Fig. 9b). As can be seen from these figures, the model described here closely matched the results from [55]. The average absolute

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Equal Cost Multiple Path Energy-Aware Routing in Carrier-Ethernet Networks with Bundled Links

Equal Cost Multiple Path Energy-Aware Routing in Carrier-Ethernet Networks with Bundled Links

K shortest paths for each demand. Therefore, G-SPB can be solved in O(K.|D|.|V |.(|E| + |V |.log|V |)). B. Fast Greedy SPB Figure 3 reports a diagram description of the process of FG-SPB. The flows in each link take initially the values of the dual variables obtained by solving the MILP (14)- (17), that minimizes the total flow summed on each link, subject to the classical constraints of flow conservation and link allowable capacity utilization. This MILP can achieve an upper bound on energy saving for any feasible solution in the case of using at most the sufficient number of cables that satisfies all traffic demands. The work [20] has shown that this solution performs poorly comparing to the optimal one. Therefore, we propose to continue the FG-SPB proceeding as follows. Each unused link will be powered-off, i.e., each link with f e = 0. The next step sorts the remaining links
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Analysis of novel, above-ground thermal energy storage concept utilizing low-cost, solid medium

Analysis of novel, above-ground thermal energy storage concept utilizing low-cost, solid medium

As shown in Figure 1, surplus thermal energy from clean sources such as nuclear power plants will be stored as sensible thermal heat in a large pile of rock.. When needed,[r]

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Developing a low-cost, systematic approach to increase an existing data center's Energy Efficiency

Developing a low-cost, systematic approach to increase an existing data center's Energy Efficiency

Each of the components consumes electricity, and can be grouped into three categories - power delivery, IT equipment, and heat removal (or cooling equipment). Additionally, mi[r]

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Software consolidation as an efficient energy and cost saving solution for a SaaS/PaaS cloud model

Software consolidation as an efficient energy and cost saving solution for a SaaS/PaaS cloud model

5 Institut Universitaire de France, Paris, France Abstract. Virtual machines (VM) are used in cloud computing environ- ments to isolate different software. Virtualization enables live migration, and thus dynamic VM consolidation. This possibility can be used to reduce power consumption in the cloud. However, consolidation in cloud environments is limited due to reliance on VMs, mainly due to their mem- ory overhead. For instance, over a 4-month period in a real cloud located in Grenoble (France), we observed that 805 VMs used less than 12 % of the CPU (of the active physical machines). This paper presents a solu- tion introducing dynamic software consolidation. Software consolidation makes it possible to dynamically collocate several software applications on the same VM to reduce the number of VMs used. This approach can be combined with VM consolidation which collocates multiple VMs on a reduced number of physical machines. Software consolidation can be used in a private cloud to reduce power consumption, or by a client of a public cloud to reduce the number of VMs used, thus reducing costs. The evaluation was performed using both the SPECjms2007 benchmark and an enterprise LAMP benchmark on both a VMware private cloud and Amazon EC2 public cloud. The results show that our approach can reduce the energy consumed in our private cloud by about 40 % and the charge for VMs on Amazon EC2 by about 40.5 %.
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A chronological probabilistic production cost model to evaluate the reliability contribution of limited energy plants

A chronological probabilistic production cost model to evaluate the reliability contribution of limited energy plants

To compare the effects of LEP dispatch on system reliability, this case study calcu- lates the ENSE, LOLP, total system cost, and hydro revenue for every possible[r]

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Cost and energy needs of RO-ED-crystallizer systems for zero brine discharge seawater desalination

Cost and energy needs of RO-ED-crystallizer systems for zero brine discharge seawater desalination

RO-ED-crystallizer concept for producing salt and water from seawater, to the best of our knowledge, no study has reported the specific cost of an RO-ED-crystallizer system designed for zero brine discharge sea- water desalination and evaluated the economics along with the energy needs of such a system. Furthermore, no study has looked at how the flow configuration and coupling between the RO and ED systems changes

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The cost of passive solar energy

The cost of passive solar energy

range of passive solar issues and test them for an understan- ding of their impact on overall building costs.. Because of the scope of.[r]

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What is Cost?

What is Cost?

Recall Schreier’s theorem: a finitely generated infinite normal subgroup of a free group Fn must have finite index. This theorem extends to those groups Γ whose free actions have cost > 1. More- over, Schreier’s formula (p − 1) = i(n − 1) re- lates the rank n of the ambient free group Fn to the rank p of a subgroup of finite index i. This formula admits a counterpart in cost the- ory: [Cost(Rα|A) − 1] = µ(A) [Cost(Rα) − 1] (compression formula), where Rα|A is the re- striction of the equivalence relation Rα to some Borel subset A ⊂ X that meets each orbit and that is equipped with the normalized restricted measure.
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Revision of Threshold Luminance Levels in Tunnels Aiming to Minimize Energy Consumption at No Cost: Methodology and Case Studies

Revision of Threshold Luminance Levels in Tunnels Aiming to Minimize Energy Consumption at No Cost: Methodology and Case Studies

method that can take place before the renovation of the tunnels. This method results in significant energy savings and lower CO 2 emissions at no additional cost. In short, the actions involved are the following: (a) a new calculation method of the stopping distance, (b) the estimation of the corresponding L20 value, and (c) the programming of the Supervisory Control and Data Acquisition (SCADA) system of the tunnel. The proposed method is presented more analytically in Figure 1. The traffic weighted L20 method was used. The main influencing factors for the examined cases were medium for traffic flow (500–1500 vehicles per hour per lane for one-way traffic), and motorized traffic only. According to these two factors, the tunnel class was defined and then, as a next step, the new corresponding threshold zone luminance (Lth’) was calculated. The new Lth’ value, was used for each tunnel, in order to define the new triggering points of the lighting stages using the SCADA control system. Because of the new lower Lth’ values in comparison to the initial Lth values, the triggering points correspond to lower lighting levels and thus to lower amounts of associated energy consumption.
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Two-Way Cost Automata and Cost Logics over Infinite Trees

Two-Way Cost Automata and Cost Logics over Infinite Trees

It is well-known that µ-calculus is equivalent to alternating automata. The simplicity of the corresponding translations implies that many properties can be expressed equally well at the automaton level and at the logic level. For instance, the structure of the acceptance condition of the automaton is tightly related to the nesting structure of fixed points in µ- calculus formulae: least fixed points correspond to odd priori- ties, and greatest fixed points to even priorities. Moreover, the number of priorities in the automaton reflects the nesting of µ and ν operators in the formula. As a special case that we are particularly interested in, the alternation free fragment of µ- calculus (see below) corresponds exactly to weak alternating automata. We can extend some of these relationships to cost functions.
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