IaaS云中面向能源效率优化的虚拟机调度算法(英文)

In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.     

云计算在电信支撑系统领域的应用分析

本文介绍了云计算服务类型,并对云计算关键技术的成熟度进行了分析。对电信支撑系统云部署需求进行了较为详细的分析,并以中国电信为例,给出了电信支撑系统实施云计算部署的方案选择和建议,并以服务器虚拟化为重点对具体方案进行了阐述。     

云计算起源探析

探讨了云计算概念的起源和发展过程,展示了云计算从一个拼凑的新单词逐渐成为IT领域最流行的趋势并形成产业链的历程。最早从企业层次提出Cloud Computing的是Dell,但对云计算概念产生较深影响的是IBM-Google并行计算项目和亚马逊EC2产品,随后越来越多的媒体、公司、技术人员开始追逐云计算,甚至将很多IT创新都放入云计算概念中,使得云计算概念和产业得以推动和发展,形成了如今包括IaaS,PaaS,XaaS,以及众多的硬件制造商、基础设施运营商等参与的一个完整产业链。     

云计算平台的自适应资源供给

云计算系统中laaS层通过对虚拟化后的基础设施进行池化管理来完成云内基础设施资源的管理.资源的池化为云服务提供按需索取的资源供应以及动态的资源配给.针对现有云计算平台缺乏对资源供应量的自动调整机制的问题,研究了云计算平台中基础设施资源供给的自适应性.通过二次平均时间序列预测法对未来一个时段内的业务负载峰值进行预测,并将预测值交予云平台转化为资源需求.对给定的资源需求,模型通过不断寻找最小虚拟机所能提供的资源与预期资源需求量之差的向量长度,做出虚拟机调度决策.仿真实验表明,本文提出的云计算自适应模型具有良好的精确性及稳定性.     

国内外IaaS服务发展分析及策略建议

以IaaS为切入点,对国内外IaaS发展状况及特点进行梳理,通过对比分析,总结我国包括IaaS在内的公共云服务发展面临的主要问题,提出推动公共云发展的具体建议,并简要介绍国内产业界为此进行的努力。     

云资源池中基于命令行接口的硬件自动控制实现

云计算是当前IT业界热门的新型商业模式,得到了广泛关注和应用,并能够大大减少企业运行成本。IaaS作为云计算最基础的一种服务模式,通过将计算、存储、网络等资源虚拟化,按需提供给用户。资源池作为IaaS中最核心的组成部分,管理了大量各式各样的硬件设备,对这些硬件的自动配置,就成了资源池的一个核心工作。本文提出并实现了一个基于命令行接口的硬件自动配置方案,实现了对资源池中多种硬件的自动配置,并达到了高通用性和高可维护性,具有重要的实践意义。     

Heuristics based server consolidation with residual resource defragmentation in cloud data centers

Server Consolidation is one of the foremost concerns associated with the effective management of a Cloud Data Center as it has the potential to accomplish significant reduction in the overall cost and energy consumption. Most of the existing works on Server Consolidation have focused only on reducing the number of active physical servers (PMs) using Virtual Machine (VM) Live Migration. But, along with reducing the number of active PMs, if a consolidation approach reduces residual resource fragmentation, the residual resources can be efficiently used for new VM allocations, or VM reallocations, and some future migrations can also be reduced. None of the existing works have explicitly focused on reducing residual resource fragmentation along with reducing the number of active PMs to the best of our knowledge. We propose RFAware Server Consolidation, a heuristics based server consolidation approach which performs residual resource defragmentation along with reducing the number of active PMs in cloud data centers.     

Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.