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.

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.

Thermal-aware relocation of servers in green data centers

Rise in inlet air temperature increases the corresponding outlet air temperature from the server. As an added effect of rise in inlet air temperature, some active servers may start exhaling intensely hot air to form a hotspot. Increase in hot air tem- perature and occasional hotspots are an added burden on the cooling mechanism and result in energy wastage in data centers. The increase in inlet air temperature may also result in failure of server hardware. Identifying and comparing the thermal sensi- tivity to inlet air temperature for various servers helps in the thermal-aware arrangement and location switching of servers to minimize the cooling energy wastage. The peak outlet temperature among the relocated servers can be lowered and even be homogenized to reduce the cooling load and chances of hotspots. Based upon mutual comparison of inlet temperature sensitivity of heterogeneous servers, this paper presents a proactive approach for thermal-aware relocation of data center servers. The experimental results show that each relocation operation has a cooling energy saving of as much as 2.1 kW·h and lowers the chances of hotspots by over 77%. Thus, the thermal-aware relocation of servers helps in the establishment of green data centers.

Towards Attaining Reliable and Efficient Green Cloud Computing Using Micro-Smart Grids to Power Internet Data Center

Energy generation and consumption are the main aspects of social life due to the fact that modern people’s necessity for energy is a crucial ingredient for existence. Therefore, energy efficiency is regarded as the best economical approach to provide safer and affordable energy for both utilities and consumers, through the enhancement of energy security and reduction of energy emissions. One of the problems of cloud computing service providers is the high rise in the cost of energy, efficiency together with carbon emission with regards to the running of their internet data centres (IDCs). In order to mitigate these issues, smart micro-grid was found to be suitable in increasing the energy efficiency, sustainability together with the reliability of electrical services for the IDCs. Therefore, this paper presents idea on how smart micro-grids can bring down the disturbing cost of energy, carbon emission by the IDCs with some level of energy efficiency all in an effort to attain green cloud computing services from the service providers. In specific term, we aim at achieving green information and communication technology (ICT) in the field of cloud computing in relations to energy efficiency, cost-effectiveness and carbon emission reduction from cloud data center’s perspective.

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%.

考虑风光荷时空互补的多能源绿色数据中心多目标配置方法

为解决数据中心高能耗、高碳排放的问题,提出了一种考虑风光荷时空互补的绿色数据中心“风-光-储”多种能源多目标容量配置模型。首先,根据数据中心的时空转移特性及储能设备的运行特性,建立绿色数据中心多能架构中灵活性资源的数学模型。接着,在数据中心与储能的灵活性运行基础上,提出一种源荷互补性指标来描述源荷间的供需差异,并以平均度电成本与供需差异最小作为优化目标建立容量优化配置模型。最后,采用风电、光伏的全年出力预测数据进行模拟,利用Benders算法对容量配置模型进行求解,得到各数据中心的最优投资建设方案。算例分析验证了所提模型的有效性,结果表明引入新能源-储能供电以及数据中心时空灵活性能够降低用电成本,并提高了新能源的渗透率。

综合考虑内外生不确定性的绿色数据中心多域资源协同规划方法

数字经济下,发展以“高能源效率、低环境影响”为主要特征的绿色数据中心(green data center,GDC)是应对碳中和挑战和促进社会可持续发展的重要举措。如何科学配置供需侧资源,在满足服务质量约束的前提下,充分利用数据用户的互动响应潜力促进系统提质增效,是未来公有型GDC规划面临的难点。为此,该文基于信息-物理-社会耦合视角,在深入分析不同类型数据负载运行特性的基础上,引入后悔度匹配方法模拟数据用户参与GDC需求响应(GDC demand response,GDC-DR)意愿的中长期动态演化。基于动态预想场景,综合考虑数据用户参与GDC-DR意愿等内生不确定性和可再生能源发电出力、数据负载需求等外生不确定性的影响,提出一种GDC多域资源协同规划模型。模型以系统综合效益最大化为目标,同时考虑设备运行、服务质量等多域约束,通过对GDC设备容量、需求响应激励价格以及各预想场景下系统运行策略进行协同优化,以充分发掘数据负载灵活性潜力,实现系统经济性和碳中和效益的综合趋优。算例仿真结果验证了该文所提模型和方法的有效性。

云计算环境下的绿色数据中心网络优化与资源管理

在云计算快速发展的今天,绿色数据中心成为了降低能源消耗和环境影响的关键。本文探讨了在云计算环境下,如何通过网络优化与资源管理实现数据中心的绿色转型。通过分析现有的网络架构和资源调度机制,本文提出了一系列优化策略,旨在提高能效比和资源利用率,减少碳排放,为数据中心的绿色化转型提供理论依据和实践指导。

张家口集群数据中心绿色发展现状及路径分析

为实现“双碳”目标,推动绿色、低碳发展,介绍张家口集群数据中心的产业规模、政策支持、产业布局等发展现状。在此基础上,分析了张家口集群数据中心的绿色发展优势,包括政策支撑、区位气候、绿电资源、基础设施、产业覆盖全等方面,并结合政策支持、标准引领、开展算力调度等维度提出了绿色发展路径建议。

上海市数据中心绿色低碳发展浅析

“十四五”以来上海市在数据中心绿色低碳发展方面稳步开展工作,取得了一定成绩。政策方面,各部门陆续发布30余项相关政策,从建设规划、能效指标、全生命周期管理、分类施策、财政支持等各方面引导行业健康有序发展。标准方面,建设已经形成体系,标准层级梯次分布,涵盖数据中心全生命周期管理。现状方面,2023年数据中心数量131家,占上海市用电量比例已达2.50%,PUE、CPUE等能效指标逐年优化。实践方面,积极创建国家绿色数据中心、国家新型数据中心和上海零碳数据中心,涌现出余热利用、可再生能源、峰谷蓄电、错峰蓄冷、液冷和间接蒸发冷却等绿色低碳技术应用典型。

绿色数据中心发展研究综述

围绕数据中心全生命周期管理,聚焦节能减碳与能效提升,分析绿色数据中心等级评估体系的演进,总结从规划设计到建造运营的一系列促进数据中心绿色转型、节能提效的创新措施。最后展望绿色数据中心运营AI化发展方向和总体拥有成本优化。

数据中心蒸气压缩-吸收式复合制冷系统工质对选取研究

利用绿电进行蓄热作为驱动热源,提出一种数据中心蒸气压缩-吸收式复合制冷系统,建立系统数学模型,模拟研究不同工质对(NH_(3)/H_(2)O,NH_(3)/LiNO_(3),NH_(3)/NaSCN,R32/DMF,R134a/DMF和R600a/noctane)对吸收式制冷模式性能系数和绿电消纳能力的影响。结果表明,在不同工况下,采用NH3/LiNO_(3)工质对时吸收式制冷模式性能系数最高,在0.496~0.635;在设计工况下,采用NH_(3)/LiNO_(3)工质对时系统绿电能效系数COPG和绿电消纳能力系数COGP最大,分别为0.833和2.307。考虑到吸收式制冷能效和绿电消纳能力,NH_(3)/LiNO_(3)工质对是更适合复合制冷系统的工质对。

数据中心的光伏储能一体化绿色能源转型

随着数字化时代的来临,数据中心的能源消耗迅速增加,导致环境和能源压力日益严重。为实现可持续发展,数据中心绿色能源转型成为重点。文章探讨光伏储能一体化解决方案在数据中心绿色能源转型中的应用,通过阐述光伏发电技术和储能技术的原理与特点,提出了基于光伏储能一体化的数据中心绿色能源转型策略,并探讨了系统设计、能源互联、智能控制、经济效益以及环境影响等方面。此外,分析了实施过程中可能出现的问题与挑战,并提出了相应的解决方案与优化策略。通过深入研究,为推动数据中心的可持续发展提供了有力支持。

An online electricity cost budgeting algorithm for maximizing green energy usage across data centers

With the sky-rocketing development of Internet services, the power usage in data centers has been signifi- cantly increasing. This ever increasing energy consumption leads to negative environmental impact such as global warming. To reduce their carbon footprints, large Internet service operators begin to utilize green energy. Since green energy is currently more expensive than the traditional brown one, it is important for the operators to maximize the green en- ergy usage subject to their desired long-term （e.g., a month） cost budget constraint. In this paper, we propose an online algorithm GreenBudget based on the Lyapunov optimization framework. We prove that our algorithm is able to achieve a delicate tradeoff between the green energy usage and the en- forcement of the cost budget constraint, and a control parameter V is the knob to arbitrarily tune such a tradeoff. We evaluate GreenBudget utilizing real-life traces of user requests, cooling efficiency, electricity price and green energy avail- ability. Experimental results demonstrate that under the same cost budget constraint, GreenBudget can increase the green energy usage by 11.55% compared with the state-of-the-art work, without incurring any performance violation of user requests.

Green challenges to system software in data centers

With the increasing demand and the wide application of high performance commodity multi-core processors, both the quantity and scale of data centers grow dramatically and they bring heavy energy consumption. Researchers and engineers have applied much effort to reducing hardware energy consumption, but software is the true consumer of power and another key in making better use of energy. System software is critical to better energy utilization, because it is not only the manager of hardware but also the bridge and platform between applications and hardware. In this paper, we summarize some trends that can affect the efficiency of data centers. Meanwhile, we investigate the causes of software inefficiency. Based on these studies, major technical challenges and corresponding possible solutions to attain green system software in programmability, scalability, efficiency and software architecture are discussed. Finally, some of our research progress on trusted energy efficient system software is briefly introduced.

绿色数据中心的热量管理方法研究

数据中心的高能耗是一个亟待解决的问题.尤其是随着云计算的发展,更多的资源集中到云端.构建绿色数据中心、实现节能减排成为了近年来业界关注的热点.数据中心的能耗主要由计算能耗和制冷能耗两部分组成.数据中心的热量管理主要从减少制冷能耗的角度出发,为实现绿色计算提供了新的思路.该文从绿色数据中心的状态监控、热量建模、热量管理策略以及热量管理评价4个方面综述了近年来数据中心热量管理方面的研究工作.该文提出了绿色数据中心热量管理的总体架构,总结了其分布式监控系统的一般框架;对现有的热量管理方法按面向单节点/面向多节点进行分类,并且从复杂度、灵活度、实施效果等多方面进行了比较,分析了各种方法的优势和局限性.文中提出了数据中心全局能耗评价、制冷系统效率评价、热量及温度评价的分类方法,对现有的评价方法进行总结.最后论文列出了未来需要进一步研究的十个方向.

绿色数据中心不完备能耗大数据填补及分类算法研究

随着云计算和大数据时代的到来,大规模数据中心在全球范围内得到了广泛的部署.但大规模数据中心的高能耗仍然是当今亟待解决的问题.为解决这一问题,通常采用太阳能等可再生绿色新能源为数据中心供电.绿色数据中心能够根据新能源的变化配合市电为数据中心提供高效、低能耗且稳定的电能供给,这是数据中心发展的趋势.文中针对数据收集不完善和断电等因素会造成一定程度的数据缺失情况,提出了一种基于完备相容类的不完备大数据填补算法,来填补数据中心的缺失数据;针对绿色数据中心能耗大数据的不稳定、间歇性和随时变化等特点,提出了一种基于离散弱相关的决策森林并行分类算法,通过对数据中心能耗大数据并行分类,来指导供电方式,以利于高效节能和延长电池寿命;此外进一步提出了一种增量更新决策森林的算法,来增量更新分类模型,该算法能够保障分类模型不断适应数据变化,防止分类准确率随时间而下降,从而避免电池频繁充放电,以保证稳定供电.整体来说,文中提出了一种数据中心能耗大数据管理模型,该模型针对大规模绿色数据中心的能源供给相关问题,运用不完备能耗大数据的填补、能耗大数据的并行分类、分类模型更新这三方面的技术方法,动态调控太阳能和市电供电端口,为数据中心提供高效、低能耗且稳定的电能供给.最后,采用绿色数据中心真实的能耗相关大数据集进行实验,实验结果说明文中提出的能耗大数据管理模型,能够帮助绿色数据中心有效管理太阳能和其他资源来配合市电提供稳定且充足的电能供应,从而为整个数据中心服务体系提供高效的能源服务.

数据中心机电工程BIM技术应用初探

基于信息通信行业绿色低碳发展要求,引入建筑信息模型(BIM)概念;从国家、省、市层面列举工程设计领域BIM的应用概况,并给出机电BIM设计总流程;介绍BIM技术融入机电设计的方式;以某数据中心机电工程项目为例,对比分析三维BIM设计与二维CAD设计的区别;最后,总结BIM技术在数据中心机电工程应用的实践意义,并提出当前BIM技术在数据中心机电工程建造应用中还存在的若干问题。

绿色云计算环境下基于资源整合的任务调度算法研究

为降低绿色数据中心的能耗,实现绿色通信的目标,在详细分析了绿色云计算的任务调度过程基础上,结合遗传算法具有较好的鲁棒性和分布性等特征,本文利用资源整合策略,设计一种新的遗传并行任务调度算法.该算法从资源整合和资源配置角度,充分考虑到绿色云计算平台的分布性和异构性等特征.仿真测试的实验结果充分显示出该算法在响应时间和能耗优化等方面,都明显优于其他算法.

Seadown:一种异构MapReduce集群中面向SLA的能耗管理方法

数据中心的能耗管理已经成为大规模数据处理中的热点问题,其主要目标是控制相关成本的急剧增长.大量的工作围绕着在集群利用率较低时,关闭部分服务器来降低能耗,但是这些方法都严重受限于数据存储策略,同时难以保证应用的实时性能.MapReduce集群作为目前流行的大规模数据处理平台,能耗问题尤为突出.文中针对异构MapReduce集群,提出一种面向SLA的能耗管理方法Seadown.首先,提出一种混合数据副本存储策略,它允许关闭大量节点,同时保证数据的完整性和集群的容错能力.其次,设计了一种基于历史记录的响应时间预测方法,它根据服务器节点的数量、性能参数和运行时间的历史信息准确估计程序的响应时间,相对误差大都在6%以下.最后,通过选择性地关闭部分节点以达到最小化能耗,同时保证应用程序的实时性能.文中证明了该优化问题是NP-hard问题,并提出了一种启发式的节点关闭策略.实验结果表明,在节点关闭策略下MapReduce应用的实时性得以保证,同时降低了大量的能耗.