Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer processes.However,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy costs.This paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research process.The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers.These sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for analysis.The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center environment.Through the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors.The model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power costs.Furthermore,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and AlexNet.The NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark algorithms.These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems.The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers.

Star Point Energy Center Correction of Star Simulator Based on Polar Coordinates

In this paper,various aberrations have been analyzed.Not only the effects of aberration on geometrical center position are taken into account,but also the deviation of displayed star position energy center caused by aberration is analyzed.These two aspects have been taken into comprehensive evaluation and star position correction.The correction method based on polar coordinates is proposed,and cumbersome partition correction and calculated quantity based on two-dimensional coordinates can be simplified.The experimental results show that the correction processing based on polar coordinates is simpler and easier compared with any other correction methods.In addition,the correction results are significantly more accurate.

An Innovative Architectural Strategy for the Integration of Energy Issues into the Design Process of a Commercial Center in Switzerland

In a context of growing efforts to develop sustainability strategies, energy-related issues occupy central stage in the built environment. Thus, the energy performance of housings has improved radically over the past decades. Yet other types of buildings, in particular commercial centers, haven’t received the same level of interest. As a result, there is a need for effective and practical measures to decrease their energy consumption, both for heating and electricity. The objective of the paper is to demonstrate that it is possible, through coherent strategies, to integrate energy issues and bioclimatic principles into the design process of commercial centers. It analyzes the exemplary case study of Marin Commercial Center (Switzerland). The interdisciplinary approach, based on integrated design strategies, aimed at increasing the energy efficiency while keeping the cost comparable to the market cost. The main design principles include natural ventilation, nighttime cooling with energy recovery and natural lighting, as well as optimization of mechanical systems. The results of the simulations show that Marin Center attains the best energy performance observed so far among Swiss commercial centers. It also meets the Swiss Minergie standard. The paper thus questions traditional design processes and outlines the need for interdisciplinary evaluation and monitoring approaches tailored for commercial centers. Even though most crucial decisions are taken during the early stages, all phases of the process require systematic optimization strategies, especially operating stages. Recommendations include legal measures, in particular in the fields of ventilation and air-conditioning, education, professional development and technology transfer, and financial incentives for the replacement of energy intensive installations.

Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers

With the rapid development of technologies such as big data and cloud computing,data communication and data computing in the form of exponential growth have led to a large amount of energy consumption in data centers.Globally,data centers will become the world’s largest users of energy consumption,with the ratio rising from 3%in 2017 to 4.5%in 2025.Due to its unique climate and energy-saving advantages,the high-latitude area in the Pan-Arctic region has gradually become a hotspot for data center site selection in recent years.In order to predict and analyze the future energy consumption and carbon emissions of global data centers,this paper presents a new method based on global data center traffic and power usage effectiveness(PUE)for energy consumption prediction.Firstly,global data center traffic growth is predicted based on the Cisco’s research.Secondly,the dynamic global average PUE and the high latitude PUE based on Romonet simulation model are obtained,and then global data center energy consumption with two different scenarios,the decentralized scenario and the centralized scenario,is analyzed quantitatively via the polynomial fitting method.The simulation results show that,in 2030,the global data center energy consumption and carbon emissions are reduced by about 301 billion kWh and 720 million tons CO2 in the centralized scenario compared with that of the decentralized scenario,which confirms that the establishment of data centers in the Pan-Arctic region in the future can effectively relief the climate change and energy problems.This study provides support for global energy consumption prediction,and guidance for the layout of future global data centers from the perspective of energy consumption.Moreover,it provides support of the feasibility of the integration of energy and information networks under the Global Energy Interconnection conception.

An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers

Based on the Saudi Green initiative,which aims to improve the Kingdom’s environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060,NEOM city has been proposed to be the“Saudi hub”for green energy,since NEOM is estimated to generate up to 120 Gigawatts(GW)of renewable energy by 2030.Nevertheless,the Information and Communication Technology(ICT)sector is considered a key contributor to global energy consumption and carbon emissions.The data centers are estimated to consume about 13%of the overall global electricity demand by 2030.Thus,reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon emissions.Therefore,this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming(MILP)model to reduce the carbon emissions associated with ICT infrastructure in Saudi Arabia.This approach considers the Saudi National Fiber Network(SNFN)as the backbone of Saudi Internet infrastructure.First,we compare two different scenarios of data center locations.The first scenario considers a traditional cloud data center located in Jeddah and Riyadh,whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy infrastructure.Then,we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy costs.After that,we optimize the energy efficiency of different cloud data centers’locations(in the SNFN)to reduce the associated carbon emissions and energy costs.Simulation results show that the proposed approach can save up to 94%of the carbon emissions and 62%of the energy cost compared to the current cloud physical topology.These savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy sources.Finally,we design a heuristic algorithm to verify the proposed approach,and it gives equivalent results to the MILP model.

Research on the Trusted Energy-Saving Transmission of Data Center Network

According to the high operating costs and a large number of energy waste in the current data center network architectures, we propose a kind of trusted flow preemption scheduling combining the energy-saving routing mechanism based on typical data center network architecture. The mechanism can make the network flow in its exclusive network link bandwidth and transmission path, which can improve the link utilization and the use of the network energy efficiency. Meanwhile, we apply trusted computing to guarantee the high security, high performance and high fault-tolerant routing forwarding service, which helps improving the average completion time of network flow.

Security and Optimization Challenges of Green Data Centers

Energy consumption in data centers has grown out of proportion in regard to the state of energy that’s available in the universe. Technology has improved services and its application. The need for eco-friendly energy and increase in data centers performance brought about Green Computing into the energy consumption of data centers. Information technology has grown and eaten deep into the society that almost all the sectors if not all are dependent on information technology to move on. The consumption of power has increased greatly. In this research paper the techniques for optimizing energy in data centers for Green Computing would be discussed. This study intends to expose the limitations of existing security solutions for securing data centers by taking into consideration of limitations of existing security frameworks that cannot enhance the security of data centers.

Adaptive energy-preserving algorithms for guiding center system

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized plasmas.The adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving algorithms.These new adaptive algorithms are second order,and their algebraic order is carefully studied.Numerical results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

Scalability of the DVFS Power Management Technique as Applied to 3-Tier Data Center Architecture in Cloud Computing

The increase in computing capacity caused a rapid and sudden increase in the Operational Expenses (OPEX) of data centers. OPEX reduction is a big concern and a key target in modern data centers. In this study, the scalability of the Dynamic Voltage and Frequency Scaling (DVFS) power management technique is studied under multiple different workloads. The environment of this study is a 3-Tier data center. We conducted multiple experiments to find the impact of using DVFS on energy reduction under two scheduling techniques, namely: Round Robin and Green. We observed that the amount of energy reduction varies according to data center load. When the data center load increases, the energy reduction decreases. Experiments using Green scheduler showed around 83% decrease in power consumption when DVFS is enabled and DC is lightly loaded. In case the DC is fully loaded, in which case the servers’ CPUs are constantly busy with no idle time, the effect of DVFS decreases and stabilizes to less than 10%. Experiments using Round Robin scheduler showed less energy saving by DVFS, specifically, around 25% in light DC load and less than 5% in heavy DC load. In order to find the effect of task weight on energy consumption, a set of experiments were conducted through applying thin and fat tasks. A thin task has much less instructions compared to fat tasks. We observed, through the simulation, that the difference in power reduction between both types of tasks when using DVFS is less than 1%.

Mechanical Properties and Energy Absorption of Integrated AlSi10Mg Shell Structures with BCC Lattice Infill

Shell-infill structures comprise an exterior solid shell and an interior lattice infill,whose closed features yield superior comprehensive mechanical performance and light weight.Additive manufacturing(AM)can ensure the fabrica-tion of complex structures.Although the mechanical behaviors of lattice structures have been extensively studied,the corresponding mechanical performances of integrated-manufactured shell structures with lattice infills should be systematically investigated due to the coupling effect of the exterior shell and lattice infill.This study investigated the mechanical properties and energy absorption of AlSi10Mg shell structures with a body-centered cubic lattice infill fabricated by AM.Quasi-static compressive experiments and corresponding finite element analysis were conducted to investigate the mechanical behavior.In addition,two different finite element modeling methods were compared to determine the appropriate modeling strategy in terms of deformation behavior.A study of different parameters,including lattice diameters and shell thicknesses,was conducted to identify their effect on mechanical performance.The results demonstrate the mechanical advantages of shell-infill structures,in which the exterior shell strengthens the lattice infill by up to 2.3 times in terms of the effective Young’s modulus.Increasing the infill strut diameter can improve the specific energy absorption by up to 1.6 times.

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion strategies.Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload pro-cessing.Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains unexplored.In this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed DC.The primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy.On the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming workload.The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

Assessment of the Energy Impact of Using Building Integrated Photovoltaic and Electrochromic Glazing in Office Building in UAE

The aim of this research was to explore the energy benefits and future potential of using Building Integrated Photovoltaic (BIPV) and Electrochromic Glazing (EG) within the climatic conditions of the city of Abu Dhabi. The Integrated Environmental Solutions (IES-VE) energy modeling software was used to assess the energy performance, mainly the reductions in HVAC and lighting, for different configurations and compare that to the base case scenario for south, east, west, and north facing facades. The results showed that the BIPV is most advantageous on the south fa?ade while the EC glazing performs best on the north facing windows. Moreover, the change in sensor location increased the energy savings for both cases, although the change was very marginal compared to the change of the glass properties. Using an automated light control system with dimming for both models, compared against the standard on-off lighting mechanism in the base case, the BIPV proves to have a higher total annual energy saving potential for most orientations, upto 33.5% while dynamic EC was best suited for the North orientation resulting in 7.4% reduction in the total annual energy consumption.

Design and Analysis of Sustainable Green Data Center with Hybrid Energy Sources

Development of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.

Data Center as a Key Player of a District Electric Power and Heat Network System： A Comparison in Urban and Suburb Regions

A DC （data center） demands air-conditioning power as large as the 1/3-1/2 of total electricity consumption. Thus, energy saving of cooling power of DC yields considerable effect on both economic and environmental views. PV （Photovoltaic） and absorption refrigerator with CGS （cogeneration systems） or gas boiler are possible power saving options. The waste warm air from DC would be utilized for greenhouse heating when DC and greenhouse locate near in the suburbs. In this study, the authors develop an energy network model to assess the potential contribution of DC as a major electric power and chilled air consumer as well as the warm air supplier in a district to the energy efficiency improvement. The evaporation heat of LNG （liquefied natural gas） utilization is also considered as well as PV, CGS. This model is applied to the cases of the urban area in Tokyo which involves athletic center, shops and hospital and the suburbs including greenhouse and then compared.

Platform Construction of Regional Energy Management Center

Based on the analysis of different requirements of energy management center construction and the data acquisition of various industries in wide area network, as well as the practices of real-lime online system, the paper puts forward the construction scheme of regional energy management center （REMC） which can achieve real time online monitoring of organizations＇ energy consumption via data collection, and also proposes the design idea of energy data acquisition based on national standards.

Interaction between an Accelerated Mass in Straight Motion and a Hidden Energy Reservoir as a Strict Mathematical Consequence of Special Relativity

A. Einstein and H.A. Lorentz had found that the mass of an accelerated body traveling at relativistic velocity appears to depend on whether the acceleration is performed in the direction of motion or in a transverse direction. E.P. Epstein rejected this result in the “Annalen der Physik”;he rather postulated an additional force that turns up when the body is accelerated in the longitudinal direction. It can be shown that the concept of an increased longitudinal mass is based on a simple mathematical error. When correcting this error, it turns out that Epstein’s additional, hidden force is indispensable in order to avoid an inner inconsistency of Special Relativity. It does most of the total work absorbed by the moving object, and is thus responsible for most of the increase in its energy (=mass), given the speed attained is relativistic. In other words: While the total force on the body needed to maintain a constant acceleration a0 is “(1-v2/c2)-1ma0=m0(1-v2/c2)-3/2a0”, the technical force needed to maintain that acceleration amounts only to “ma0=m0(1 - v2/c2)-1/2a0”. The total energy of two objects that undergo a symmetrical, elastic head-on collision is therefore not conserved during the collision, thus requiring the involvement of a hidden reservoir of energy. This result is confirmed by calculations that use the concept of momenergy. The phenomenon of an apparent disappearance of energy has been noticed in particle physics already (target-experiment), but its consequences have been ignored. Instead, an explanation has been given (reduced “energy of the center of mass”) which is inconsistent and violates the relativity principle.

A Review of Energy Efficiency in Data Processing Centers

This article explores, through a case study, measures of energy efficiency in data processing centers. An analysis of this case demonstrates how the design criteria could improve the rate of consumption in IT centers, which is currently the second most contaminating industry on the planet, and is the responsible for 2% of CO2 emissions, surpassed only by the aeronautical industry. The present and future situation of IT center energy consumption and associated environmental effects is analyzed, and also looks at how state-of-the-art technology, correctly implemented, could ensure significant rationalization of data processing center energy consumption. The article will examine optimization techniques, specific problems and case studies.

PEC柱-钢梁摩擦耗能部分自复位组合框架抗震试验研究

为系统研究梁柱采用自复位连接组合框架的抗震机理,选取预拉杆长度设置、PEC柱截面强弱轴布置和柱脚连接方式3个设计参数,设计制作了4榀PEC柱-钢梁梁柱摩擦耗能部分自复位连接组合框架1∶2缩尺试件并进行拟静力抗震试验。通过试验现象观察和数据分析,对试件的滞回特性、抗侧刚度退化、复位能力、滞回耗能等抗震性能进行研究。结果表明:摩擦耗能部分自复位连接通过T形件长圆孔合理设置实现了“设计地震水平阶段实现复位,大震设计水平阶段自复位连接连接转化为伴随出现螺栓承压型受力实现部分自复位”的性能化设计目标;设计地震阶段,试件主要通过辅助摩擦耗能件耗能,且卸载残余侧移小于自复位结构侧移限值(0.3%),在大震作用阶段,试件通过辅助摩擦耗能件与结构构件损伤联合耗能,承载能力仍继续增大,且仍具有部分自复位能力;预拉杆设置有限长度可显著增强结构整体性,PEC柱弱轴布置一定程度上降低了结构整体性,而PEC柱脚采用铰接可显著削弱结构整体性。

EC风机在能源中心通风系统中的节能改造实践应用——以能源中心燃气分布式供能机房项目为例

通风系统在工业生产领域中扮演着重要角色,它不仅保障了室内空气质量、温度、湿度,还能有效地排除特定区域内的有毒有害和易爆气体,尤其是在天然气使用场所,完善的通风系统可以有效降低使用过程中因泄漏所引起的各类安全风险。以上海某大型区域集中式供能项目中的能源中心燃气分布式供能机房进排风系统为研究对象,通过实际项目改造案例,分析研究EC风机相比传统AC风机在进排风系统中的应用优势,分别从EC风机结构原理,风机优势,方案可行性和经济性,节能减排方面进行全面分析,以此验证EC风机在传统进排风系统优化升级改造中的应用可行性。

ECMWF模式强降水预报偏差订正方法研究及应用

利用安徽省81站逐日降水量资料、NCEP 500 hPa再分析资料、ECMWF(以下简称EC)降水和500 hPa高度预报,基于暴雨中心和天气类型的客观判定,分类统计2012-2018年23个强降水过程降水中心的预报偏差。结果表明在西路强冷空气和东路冷空气天气类型下,当EC预报降水中心位于115°-120°E 584 dagpm线以北时,降水中心预报往往偏北,依据两者的纬度差和降水中心预报偏差建立了基于天气分类的主雨带位置订正方法;同时依据23个强降水过程最大降水区域降水量预报的日平均偏差,建立了暴雨的强度订正方法。将偏差订正方法应用于2020年安徽省梅汛期预报,结果发现无论位置还是强度订正都能使暴雨预报TS评分明显提高。同时进行位置和强度订正后,暴雨TS评分提高更加明显,尤其是对2020年两次最强降水过程订正效果显著。