Low-power task scheduling algorithm for large-scale cloud data centers

How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.

Workload-aware request routing in cloud data center using software-defined networking

Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines（VMs）. This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.

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%.

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.

Subject Oriented Autonomic Cloud Data Center Networks Model

This paper investigates autonomic cloud data center networks, which is the solution with the increasingly complex computing environment, in terms of the management and cost issues to meet users’ growing demand. The virtualized cloud networking is to provide a plethora of rich online applications, including self-configuration, self-healing, self-optimization and self-protection. In addition, we draw on the intelligent subject and multi-agent system, concerning system model, strategy, autonomic cloud computing, involving independent computing system development and implementation. Then, combining the architecture with the autonomous unit, we propose the MCDN (Model of Autonomic Cloud Data Center Networks). This model can define intelligent state, elaborate the composition structure, and complete life cycle. Finally, our proposed public infrastructure can be provided with the autonomous unit in the supported interaction model.

基于H3Cloud架构的广播电视云数据中心IT设备层可靠性分析

论述广播电视云数据中心的应用需求,分析基于H3Cloud云架构的广播电视云数据中心的IT设备计算接入层、基础设施层、网络控制与智能保障层以及业务交付层的可靠性技术手段,指出广播电视云数据中心设计和建设过程需要对各层次的可靠性进行深入分析和论证,并根据应用需求进行可靠性强化。

基于云计算的通信IDC机房供电电源系统设计

随着云计算技术的快速发展,传统通信互联网数据中心(Internet Data Center,IDC)机房供电系统已难以满足其高可靠性、高效率、灵活性的需求。文章针对云计算数据中心的特点,提出了一种创新的通信IDC机房供电电源系统设计方案。该方案包括主供电、备用电源和不间断电源(Uninterruptible Power Supply,UPS)3个模块,并在相应模块中集成了智能配电管理系统、云平台能量管理系统、智能UPS能效管理系统。该研究为云计算数据中心的稳定运行和可持续发展提供了有力支撑。

Hybrid Cloud Architecture for Higher Education System

As technology improves,several modernization efforts are taken in the process of teaching and learning.An effective education system should maintain global connectivity,federate security and deliver self-access to its services.The cloud computing services transform the current education system to an advanced one.There exist several tools and services to make teaching and learning more interesting.In the higher education system,the data flow and basic operations are almost the same.These systems need to access cloud-based applications and services for their operational advancement and flexibility.Architecting a suitable cloud-based education system will leverage all the benefits of the cloud to its stakeholders.At the same time,educational institutions want to keep their sensitive information more secure.For that,they need to maintain their on-premises data center along with the cloud infrastructure.This paper proposes an advanced,flexible and secure hybrid cloud architecture to satisfy the growing demands of an education system.By sharing the proposed cloud infrastructure among several higher educational institutions,there is a possibility to implement a common education system among organizations.Moreover,this research demonstrates how a cloud-based education architecture can utilize the advantages of the cloud resources offered by several providers in a hybrid cloud environment.In addition,a reference architecture using Amazon Web Service(AWS)is proposed to implement a common university education system.

基于云数据中心的多源异构数据治理技术研究

目前常规的多源异构数据治理方法主要通过对数据属性进行判断,从而实现分区域数据清洗,由于缺乏对非线性数据的分析,导致治理性能不佳;对此,提出基于云数据中心的多源异构数据治理技术;采用关系型数据库中的ETL功能对数据进行清洗,对数据转换模式以及数据清洗规则进行定义;引入互信息系数对数据相关程度进行判定,并进行非线性数据相关性分析;以云数据中心作为载体,对多源异构数据治理体系进行构建;在实验中,对提出的数据治理技术进行了治理性能的检验;最终的实验结果表明,提出的数据治理技术具备较高的查准率,对云数据中心多源异构数据具备较为理想的数据治理效果。

高等职业技术学院云机房设计及优势探讨

本文通过分析高等职业技术学院传统机房使用中存在维护工作量大、投资费用高、资源利用低等诸多问题,提出了利用云计算技术来解决传统机房不足之处。通过云机房方案设计,采用分布式存储系统,提出整体架构由基础框架、管理软件、桌面控制器、终端接入设备构成,并总结了云机房部署灵活、运维方便、随时教学、安全高效的优势,最后结合实际分析了云机房网络依赖性高、终端处理能力弱、专业性软件适应性差等问题。

基于CNN-LSTM神经网络的磁盘故障预测方法

运维人员准确预测将要发生的磁盘故障是保障数据安全的关键。然而,不平衡数据、不准确磁盘特性标记影响预测的准确性。提出一种基于预故障重置窗口(pre_Failure Reseting Window,pre_FRW)数据处理并组合卷积神经网络(CNN)和长短期记忆网络(LSTM),即pre_FRW-CNN-LSTM的磁盘故障预测方法。pre_FRW数据处理既可以解决样本不平衡,又能减少潜在的模糊样本。而CNN-LSTM模型结构能提取数据的空间特征,还能有效捕捉时间序列之间的依赖关系。在真实监控数据集上实验表明,pre_FRW-CNN-LSTM的磁盘故障预测方法对比业界其他方法提升2%~10%的故障预测率,并保持较低的错误告警率。

面向数据中心安全的密码保障体系研究

随着国家电子政务和各行业信息化建设工作的稳步推进,数据中心建设需求和发展趋势越来越明显。数据中心融合了云、大数据、信息安全、密码保密、隐私保护等众多技术,如何有效规范数据中心建设,确保数据安全成为当前研究热点之一。围绕数据中心建设过程中的数据安全管控问题,积极探索并建立以密码技术为核心的数据安全保障体系和安全保障思路,提出基于密码基础设施、信任服务、应用密码服务、网络加密、存储保护等机制的数据安全密码保障技术框架,设计数据安全密码保障流程,为各领域、各行业数字化转型和数据中心建设提供参考。

基于冗余分析的数据中心服务器能耗特征选择

应用精确的服务器能耗模型对能耗进行预测,可为资源调度方法提供重要依据。考虑到云数据中心服务器能耗特征维度高、冗余特征难以判断的题,分析特征与特征之间、特征与目标值之间皮尔逊相关性系数的联系,并给出服务器能耗冗余特征的判断准则,在此基础上提出一种基于冗余分析的服务器能耗特征选择算法。实验结果表明了所提能耗特征选择算法在服务器能耗模型构建中的有效性。

基于SDN技术的数据中心基础网络构建

随着我国信息技术的不断发展,客户对信息数据的要求也在不断地提高,如需要多样化的数据、数据传递要更加迅速、数据要有较高的自身处理能力等,这就意味着必须要对网络进行灵活的控制。软件定义网络(Software Defined Network,SDN)技术的出现有效地实现了这一特点,其不仅可以实现资源的灵活配置、自动配置,还满足数据中心网络的应用需求。因此,就对基于SDN技术的数据中心基础网络构建进行研究。首先分析其构建数据中心的优势,然后从其基本架构、抽象服务等方面来对SDN技术的应用以及基础网络构建进行深入的探讨和分析,以此来为相关部门提供参考。

基于云-边-端协同的区域级风电场大数据中心数据管理框架及优化运行方法

区域级风电场大数据中心面临运行能效低、交互性差、资源重复建设等问题,难以满足数字化特征凸显的新型电力系统对数据实时计算、绿色计算的需求。该文系统性梳理了风电场大数据中心典型数字化业务的类别、算力敏感性特征和可调节潜力,构建了基于云-边-端协同技术的数据管理架构,从数据交互、业务执行、资源调度和质量治理4个维度提出了大数据中心优化运行方法。算例仿真分析了所提架构在数据传输、存储和能耗等方面的性能,结果表明:相较于集中式架构,云-边-端协同可为大数据中心云服务器节约350%的存储容量,降低21.04%的能耗,验证了所提架构在实际工程中应用的有效性和合理性。

Resilient Power Systems Operation with Offshore Wind Farms and Cloud Data Centers

To enhance the resilience of power systems with offshore wind farms(OWFs),a proactive scheduling scheme is proposed to unlock the flexibility of cloud data centers(CDCs)responding to uncertain spatial and temporal impacts induced by hurricanes.The total life simulation(TLS)is adopted to project the local weather conditions at transmission lines and OWFs,before,during,and after the hurricane.The static power curve of wind turbines(WTs)is used to capture the output of OWFs,and the fragility analysis of transmission-line components is used to formulate the time-varying failure rates of transmission lines.A novel distributionally robust ambiguity set is constructed with a discrete support set,where the impacts of hurricanes are depicted by these supports.To minimize load sheddings and dropping workloads,the spatial and temporal demand response capabilities of CDCs according to task migration and delay tolerance are incorporated into resilient management.The flexibilities of CDC’s power consumption are integrated into a two-stage distributionally robust optimization problem with conditional value at risk(CVaR).Based on Lagrange duality,this problem is reformulated into its deterministic counterpart and solved by a novel decomposition method with hybrid cuts,admitting fewer iterations and a faster convergence rate.The effectiveness of the proposed resilient management strategy is verified through case studies conducted on the modified IEEERTS 24 system,which includes 4 data centers and 5 offshore wind farms.

云数据中心的资源调优方法研究与应用

近年来,通信行业云计算业务保持快速增长,云用户及云服务数量持续增加,云数据中心的规模也不断扩大,报告了运维云数据中心的工作中所面临资源利用率不均衡、云主机应用安全认知疏忽等问题的现状。文章通过对目前市场上云数据中心所拥有的相关技术进行了研究,提出在现网环境下解决以上现状问题的思路与方法,通过实践应用目前现网环境针对以上问题提升了百分之八十的显著效果,因此运维人员能联系实际运维情况,更好的解决云数据中心IaaS层、PaaS层的相关资源调优与维护问题。

Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

Virtual Machine（VM） allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant＇s VMs may considerably impact the tenant＇s Quality of Service（Qo S）. In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs＇ network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem（LP-MKP）. The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants＇ overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.

基于云原生的下一代智慧图书馆服务平台研究

本文分析了智慧图书馆服务平台现状,总结现有平台使用的入侵式微服务架构存在功能不全、无法跨语言、部署升级难等问题,提出可以通过引入ServiceMesh微服务架构,实现业务逻辑和非业务逻辑解耦,从而解决入侵式微服务架构的缺陷.文章搭建了基于云原生的智慧图书馆服务平台系统架构,实现业务中台和数据中台双中台建设方案,全面赋能图书馆智慧服务。

通信计算联合优化的图分割工作流部署方法

为提高计算效率,将复杂的大规模任务分解为简单任务并建模为工作流,交由并行分布式计算集群来完成,已成为云中心处理持续增长的计算和网络任务的重要手段。然而,分布式计算的任务间数据传输所带来的通信带宽占用却容易造成云中心的网络拥塞。如何兼顾计算效率和通信开销,科学地部署工作流意义重大。两类典型的工作流部署算法为基于列表的部署算法和基于分簇的部署算法。然而,前者致力于提高计算效率,未关注工作流中任务之间的通信开销,大规模工作流的部署易带来较重的网络负荷;后者关注通信开销的最小化,但牺牲了工作流中任务的并行计算效率,导致工作流完成时间较长。文中从图论的角度出发,充分挖掘工作流中各任务之间的依赖性和并行性,通过对经典图分割算法进行改进,实现了工作流任务分区过程中通信开销最小化和计算并行性最大化之间的平衡。仿真结果表明,在不同的工作流规模下,所提算法的通信开销比列表部署算法平均减少约35%~50%,工作流完成时间比分簇部署算法平均降低约50%~65%,且对于具有不同通信计算比的工作流均具有良好的稳定性。