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(LTSA)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.

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.

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.

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.

Virtual machine placement optimizing to improve network performance in cloud data centers

With the wide application of virtualization technology in cloud data centers,how to effectively place virtual machine(VM)is becoming a major issue for cloud providers.The existing virtual machine placement(VMP)solutions are mainly to optimize server resources.However,they pay little consideration on network resources optimization,and they do not concern the impact of the network topology and the current network traffic.A multi-resource constraints VMP scheme is proposed.Firstly,the authors attempt to reduce the total communication traffic in the data center network,which is abstracted as a quadratic assignment problem;and then aim at optimizing network maximum link utilization(MLU).On the condition of slight variation of the total traffic,minimizing MLU can balance network traffic distribution and reduce network congestion hotspots,a classic combinatorial optimization problem as well as NP-hard problem.Ant colony optimization and 2-opt local search are combined to solve the problem.Simulation shows that MLU is decreased by 20%,and the number of hot links is decreased by 37%.

VirtCO:Joint Coflow Scheduling and Virtual Machine Placement in Cloud Data Centers

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines(VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.

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.

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.

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。

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。

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。

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。

面向云数据中心的人工智能模型自动优化框架设计

人工智能模型的自动调优技术能够以较低资源成本提供云数据中心的高性能智能服务。然而,人工智能模型和硬件设备具有异构性,云数据中心执行自动调优操作会产生大量计算时间,占用算力资源,产生能耗成本。针对此问题,本文设计面向云计算数据中心的人工智能模型自动优化框架。提出人工智能模型候选配置项过滤方法,利用模型构建、特征提取、候选项探索、配置查询等技术对候选项搜索空间重新采样,将高效候选项替换低效候选项。在算子优化层面,框架分批并行执行计算组件实现的硬件测量,避免连续探测搜索空间。在模型优化层面,根据多人工智能模型的相对性能加速优先跨集群的计算组件优化。该框架旨在面向不同人工智能模型,降低人工智能模型推理延迟,减少云计算数据中心能耗,从而提升人工智能模型自动调优的成本效益。

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.

云数据中心信息安全建设方案分析

云数据中心是现代网络技术发展的重要成果,其达成了网络虚拟化的最终目标。传统视角下的安全防护策略整体较为固定,主要包括固定IP、静态网络等,面对云数据中心的崛起,各项安全防护策略难以满足实践发展需求。文中以此为切入点,从安全服务需求的角度详尽分析了其架构思路,探讨了云数据中心网络安全服务建设的具体方案。

云数据中心时延优化的数据放置方法

针对云数据中心数据获取效率低和服务器资源浪费问题,为优化云平台的数据访问和资源利用,文中提出了一种时延优化的云数据中心数据放置(LOP)方法。文中首先分析了云平台的性能,建立了云平台的资源利用和数据获取时间模型。然后基于非支配排序算法NSGA-Ⅲ实现了全局最优的数据放置策略,对数据资源进行合理部署,有效利用服务器的资源,提高了数据获取的效率。最后通过CloudSim仿真平台,对提出的数据放置方法进行了仿真和对比实验。实验结果表明,LOP方法能明显提高云服务器的资源利用率,缩短任务的数据获取时间。

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。