COLLABORATIVE RESOURCE ALLOCATION OVER A HYBRID CLOUD CENTER AND EDGE SERVER NETWORK

This paper considers the collaborative resource allocation problem over a hybrid cloud center and edge server network, an emerging infrastructure for efficient Internet services. The cloud center acts as a pool of inexhaustible computation and storage powers. The edge servers often have limited computation and storage powers but are able to provide quick responses to service requests from end users. Upon receiving service requests, edge servers assign them to themselves, their neighboring edge servers, as well as the cloud center, aiming at minimizing the overall network cost. This paper first establishes an optimization model for this problem. Second, in light of the separable structure of the optimization model, we utilize the alternating direction method of multipliers （ADMM） to develop a fully collaborative resource allocation algorithm. The edge servers and the cloud center autonomously collaborate to compute their local optimization variables and prices of network resources, and reach an optimal solution. Numerical experiments demonstrate the effectiveness of the hybrid network infrastructure as well as the proposed algorithm.

A Dynamic Load Balancing Method of Cloud-Center Based on SDN

In order to achieve dynamic load balancing based on data fl ow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring of the service node flow and load condition by the Open Flow protocol. When the load of system is imbalanced, the controller can allocate globally network resources. What's more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure that the load will not tilt over a long period of time, and improve the system throughput.

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.

Research on Comprehensive Performance Assessment for WSN Based on ANP with Equilibrium Variable Weight and Cloud Gravity Center

The index system of comprehensive performance assessment for WSN is established firstly according to the requirement of Smart Grid application, and ANP is adopted to determine the constant weight of index. Then the equilibrium variable weight is used to adjust the constant weight under extreme case. Moreover, the section division of comment grade for qualitative index is treated by the numerical characteristics of cloud model. Finally the cloud gravity center is used to calculate the weight deviation degree presenting the final evaluation result. The method proposed fully considers the interaction among indices and guarantees randomness and fuzziness in evaluation process. The reliability and efficiency of the method is demonstrated by the assessment of actual WSN examples.

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.

采用云重心评判法的港口船舶溢油综合应急能力评价

为突破单环节评价港口船舶溢油应急能力的局限性,合理配置资源,采用云重心评判法全环节评估港口船舶溢油综合应急能力,面向应急准备、预警、行动、恢复全环节,参照人-机-环境-管理系统理论和国际国内规定,建立综合应急能力评价指标体系;针对传统评价指标因素未涉及模糊性和随机性的问题,用加权偏离度确定定性指标与定量数值间的转化关系;利用层次分析法(AHP)优化评价意见,弥补评价样本欠充分带来的精度缺陷;最后,以上海港为例,对船舶溢油综合应急能力评价作实证研究。结果表明:云重心评判法能合理评价港口船舶溢油综合应急能力,为健全和完备应急能力提供理论依据,为港口船舶溢油综合应急能力评价提供了一种新思路。

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。

云计算中心的高效地震处理解释能力体系建设与工业化应用

随着云计算中心硬件环境的建立,如何充分利用云中心的计算、存储和网络基础设施环境建立高效的地震处理解释能力体系,从而实现地震处理解释新技术的大规模推广与工业化应用成为必须解决的问题。文章将重点介绍如何利用云计算中心的基础设施环境,建设高效地震处理解释能力体系。

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。

基于点云的铣刀螺旋槽截面预测模型

基于点云离散的思想,将铣刀棒料离散为点云集合,将砂轮简化为回转面及中心矢量表达的二维模型,建立判断棒料截面点云与砂轮回转面是否包含的磨削切除预测模型,并利用预测截面点云集合计算螺旋槽相关参数。采用OCC建立实际仿真磨削的点云切除过程模块,实现对预测螺旋槽截面的显示以及结构参数的精确计算。通过改变砂轮结构位置参数,给出螺旋槽截面相关参数变化规律,并对其中具体实例进行了结果显示。

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。

城市轨道交通实训中心智能运维管理系统设计

设计了一种基于云-边协同架构、多专业协同融合的城市轨道交通实训中心智能运维管理系统。首先,分析智能化时代实训中心多专业的融合协同、集成化的实时监测、智能化的故障预防、信息化的资源管理、数字化的场景输出和多方面的反哺机制等需求;其次,基于云-边协同架构,对系统逻辑架构和物理架构进行设计;最后,应用智能感知、数据库、云边协同、故障预测与健康管理等技术,实现智能运维管理系统实时监控、数据管理、健康管理、应急管理、物资管理和教学管理等功能。系统测试结果表明,该系统能有效实现上述功能,显著提高实训中心的智能运维管理水平。

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。

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。

使用中心预测-聚类的3D箱体实例分割方法

随着深度学习技术在工业领域的大量部署,应用于运输、装卸、包装、分拣等环节的自动化系统成为仓储物流行业的研究热点。针对机器人箱体拆垛场景提出一个点云中心预测-聚类网络(center prediction-clustering network,CPCN),对箱体垛进行实例分割,并计算每个箱体的上表面中心坐标。CPCN在传统的语义-实例联合分割结构的基础上,为实例分割分支设计了中心预测模块和中心强化模块。中心预测模块帮助定位实例中心以避免中心点分割错误,中心强化模块令属于同一实例的点在特征空间中向中心聚集,二者有效增强了实例特征的辨识能力。在实例特征处理部分设计的中心-实例聚类方法直接对实例特征进行距离度量来计算实例标签,大幅减少了计算时间。在箱体数据集上进行的实验表明,与现有方法相比CPCN在实例分割任务中的平均精确率最低提高了0.7个百分点,最高提高了17.2个百分点,预测实例中心的准确率达到94.4%,中心偏移量低至13.70 mm,且推理速度快于同类型的联合分割网络,对于箱体拆垛任务更有针对性,具有良好的应用价值。

改进Petri网下异构跨域用户身份验证方法

在异构跨域用户身份验证时,由于用户身份存在属性特征难以捕捉、状态描述误差大等问题,导致验证精准度较低,为有效解决这一问题,文中提出一种基于改进Petri网的用户身份验证算法。利用改进Petri网反映不同局域网中事物间依赖关系,将异构跨域用户活动模型与Petri网模型转换,采用同步用户的状态变化提取实时状态特征值。通过建立云间身份认证中心,计算用户不同身份代表性词语在一定周期内出现的次数和频率值。提取频率最高的词语,并计算该词语在待验证用户全部代表词语库中的权重值,对比权重值与验证熵值,将得到的验证参数输入到认证中心,输出验证结果。实验结果表明,所提方法在先验节点比例为8%时,验证准确率达到0.9,且召回率和综合指标均在0.6以上,具备一定的抗攻击性,整体实用性能强、利用价值高。

改进的云模型在化工园区脆弱性评估中的应用

为了评估化工园区的脆弱性,精准防范园区事故风险,提出了基于COWA G1和云重心理论的化工园区脆弱性评价模型。首先,运用鱼骨图对影响化工园区脆弱性的因素进行分析,在此基础上构建化工园区脆弱性评价指标体系;其次,为降低传统赋权方法的主观性,通过连续的有序加权平均(Continuous Ordered Weighted Averaging,COWA)算子和G1法组合确定指标主客观权重;然后,引入云重心理论计算加权相似度,从数据角度科学判别化工园区脆弱性评价等级;最后,以某化工园区为例,验证了该方法的可靠性和有效性。结果表明,化工园区脆弱性等级处于“一般脆弱”水平,表明化工园区韧性良好。该评价模型可利用编程完成模型仿真,简单实用,提高了综合评价的效率和可操作性。研究内容可以为化工园区的日常安全管理及应急处置提供理论支持和决策依据。