基于改进胖树结构的数据中心网络设计

随着云服务的广泛应用,部署云服务的数据中心网络向着大型化,多路径的结构发展。胖树运用简单的拓扑模型为数据中心提供出色的聚合带宽性能。本文提出一种基于改进胖树结构的新型数据中心网络,该网络利用边缘交换机直接连接核心交换机,并为其设计了错误避免路由算法,为数据中心网络提供简单高效的路由生成办法。通过计算得知改进型胖树结构的数据中心网络有更短的路径,同时能简单快速构建路由表。     

两种路由策略实现光纤物理路由最大不相关算法

本文对光网络生存性中的基于通路的保护机制进行了研究,采用两种路由策略实现了用于多纤光网络的光纤物理路由最大不相关算法。仿真结果表示：该算法能迅速为连接请求计算出物理路由最大不相关的工作路径和保护路径。     

用column generation算法规划网络编码业务

网络编码能够有效降低网络中关键边的资源消耗,改善网络的负载均衡。但是普通的启发式路由算法通常只能为单个业务寻找最优路由,无法优化网络的整体性能。运用column generation算法对网络编码业务进行规划,为松弛系数赋予具体的物理含义,并据此进行路径更新,有针对性地为每个业务寻找路由。与启发式算法相比,column generation从整体上提高了网络的吞吐量,改善了网络的负载均衡。同时,与普通ILP算法相比,column generation算法无需计算大量备选路径,且函数始终处于收敛状态,不会产生振荡,求解总时间缩短了23.5％,总代价优化2.5％。     

基于蚁群算法的SDN数据中心网络大象流调度研究

针对传统方法调度大象流时容易造成数据中心网络拥塞和负载不均衡等问题,提出一种基于蚁群算法的SDN（software defined network）数据中心网络流量调度算法ACO-SDN。对大象流调度问题建立整型线性规划ILP（integral linear programing）模型,优化目标为最小化最大链路利用率。通过重定义蚁群算法的参数和操作求解ILP模型,得到大象流重路由的最优路径。实验结果表明,与ECMP（equal-cost multi-path routing）和GFF（global first fit）流量调度算法相比,ACO-SDN算法降低了网络最大链路利用率,有效地提高了网络对分带宽。     

移动对等网络中的感知蚁群路由算法

针对移动对等网络的实际需要,文中提出了一种感知蚁群路由算法.该算法通过感知节点能量、链路质量和链路生存性等网络状况,可以有效地均衡网络能量,提高分组投递率.该算法在路由发现阶段采用基于信息素的选播机制,既保证及时找到有效路径,又避免传统广播机制浪费能量的现象;在评价建立的路径时,既考虑了沿途节点的能量,又兼顾了链路质量和链路生存性;在数据路由时,结合了概率型路由的自动均衡和确定型路由的快速收敛.模拟实验结果表明了它的有效性.     

基于软件定义数据中心网络的节能路由算法

由于数据中心网络是云计算和下一代网络技术的平台和基础设施,日益增长的网络数据在满足用户需求的同时,也大幅增加了数据中心的能耗。许多针对数据中心网络的节能策略被提出,多数采用硬件与软件相结合的策略来完成节能模型的设计。为了进一步降低能耗,从网络负载均衡和节能路由设计的角度提出了一种新的节能路由算法,其基本思想是首先对负载均衡进行量化分析,然后提出带宽限定的负载均衡与节能相结合的节能路由算法,充分考虑到网络整体的可达性和可靠性。该算法为数据中心节能提供了一种新的视角。通过与传统的节能路由作比较,验证了该算法能够在保证较高网络可靠性的同时能耗较低。通过对实验数据的分析和解释得到了若干有益的结论,为进一步的研究工作奠定了基础。     

基于P2P覆盖网络的服务领域部署策略

针对服务部署策略不完善的问题,提出P2P覆盖网络框架下自上而下的2级服务部署策略。在顶层P2P网络中部署领域,并在每个领域中部署各种服务组件。对单个领域和伙伴关系领域的放置情况进行建模,使用3种算法进行求解。仿真实验结果表明,伙伴关系领域的邻近放置策略降低了跨领域组合服务的路由开销。     

改进蚁群算法在QoS组播路由中的应用

QoS组播路由是网络传输中的一项关键技术,蚁群算法是解决多QoS约束组播路由问题的一种启发式算法。针对蚁群算法的缺点,提出了一种双向蚁群算法对该问题进行求解,并改进了蚁群算法的信息素更新策略。仿真实验表明,该算法能快速搜索并收敛到全局（近似）最优解,且随着网络规模的增大,算法保持了良好的特性。     

基于退火下山法的源路由算法

虽然动态路由已经成为趋势,但是由于在某些考虑到安全性的路由系统(如洋葱路由系统)中,还是需要用源路由作为最主要的路由形式,而当需要路由的网络十分庞大的时候,传统的路由方法的效率实在是太低了,这里给出一种新的启发式算法用在源路由大规模网络上。结合模拟退火算法与下山法各自的优点,得到了一种高效、收敛的启发式算法：模拟退火下山算法,它是针对大规模网络路由的复杂性而提出的一种有效快速的算法。作为一种启发式算法,它本身有一定的优缺点,它可以保证得到全局最优解,但是如果要更快速地收敛于最优解的话,则对初始路径的设定有一定要求。一般来说,若初始路径是一个较优值,并且比较恰当,则寻优过程会很迅速。     

基于层间信息路由的多层业务量疏导算法研究

研究在网络资源一定的情况下如何提高网络利用率问题.利用业务量疏导技术,在相同业务量情况下,以最小化网络资源为目标,为了优化网络的性能,提出了一种基于层间信息路由的多层业务量疏导的启发式算法CLIR-MLTG.采用基本权重作为虚链路扩展光路的惩罚权重,使业务尽可能路由对下层资源影响最少的虚链路上,避免了本来不需要在下层保护的光路变得需要在下层被保护.通过仿真实验,结果表明采用上述算法减少了对物理资源的占用,同时运行速度更快,有效解决了大型MPLS over WDM可生存性业务量疏导问题.     

数据中心网络路由研究进展

随着云计算的迅速发展,运营商对数据中心的需求与日俱增.作为数据中心网络的关键技术,路由负责在数据中心内部以及数据中心之间为流量选路,为不同服务质量要求的流量提供差异化的路由转发服务.当数据中心规模比较大时,由于应用不可预估的通信流量以及数据中心网络的拓扑特点,传统因特网路由方法不能提供令人满意的高吞吐率和资源利用率,网...     

A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

Cloud-based data centers consume a significant amount of energy which is a costly procedure. Virtualization technology, which can be regarded as the first step in the cloud by offering benefits like the virtual machine and live migration, is trying to overcome this problem. Virtual machines host workload, and because of the variability of workload, virtual machines consolidation is an effective technique to minimize the total number of active servers and unnecessary migrations and consequently improves energy consumption. Effective virtual machine placement and migration techniques act as a key issue to optimize the consolidation process. In this paper, we present a novel virtual machine consolidation technique to achieve energy–QoS–temperature balance in the cloud data center. We simulated our proposed technique in CloudSim simulation. Results of evaluation certify that physical machine temperature, SLA, and migration technique together control the energy consumption and QoS in a cloud data center.     

FRP: a fast resource placement algorithm in distributed cloud computing platform

We consider a large‐scale online service system of placing resources geographically distributed over multiple regional cloud data centers. Service providers need to place the resources in these regions so as to maximize profit, accounting for demand granting revenues minus resource placement costs. The challenge is how to optimally place these resources to fulfill varying demands (e.g., multidimensional and stochastic demands) among these cloud data centers. Considering demand stochasticity will significantly increase time complexity of resource placement algorithm, resulting in inefficiency when handling a large number of resources. We propose a fast resource placement algorithm (FRP) to obtain the maximum resource revenue from distributed cloud systems. Experiments show that in scenarios with general settings, FRP can achieve up to 99.2% revenue of existed best solution while reducing execution time by two orders of magnitude. Therefore, FRP is an effective supplement to existing algorithms under time‐tense scheduling scenarios with a large number of resources. Copyright © 2015 John Wiley & Sons, Ltd.     

VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10–20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.     

云环境下利用用户到数据中心建模的高效节能路由算法

针对云计算用户、服务、供应商和数据中心的密度不断增长导致传输数据、网络流量和基础设施的大量能耗问题,提出针对云数据的高效节能路由算法。其目的是在用户和数据中心之间定位出最低能量消耗路线,同时确保用户需求。首先,对用户到数据中心的连通性进行建模,分析了网络拓扑结构;然后为了用户意图最简化和能量最小化,通过遍历节点最小数的基线最短路径算法进行评估,将用户任务通过最节能路径发送到数据中心,从而最小化能量损耗和服务响应时间(SRT)。实验的网络拓扑结构使用互联网服务提供商(ISP)的分支设计。实验结果表明提出的算法具有更短的路由路径长度和更低的路由能耗。此外,最短路径方法只有在成功发送或接收之后才能确定最节能的路由。     

一种新的云存储服务模型研究*

如何用可生存理论与技术来增强存储服务的可生存性是一个重要的研究内容。本文提出了一种基于云存储的可生存存储服务模型,对模型架构进行了详细设计和构造,并分别采用了一种存储服务模型可用性的度量方法(资源分配失效概率模型)进行了分析,通过该方法分析可得本文提出的可生存云存储服务模型能够满足较高的可生存性的需要。     

Adaptive data replication strategy in cloud computing for performance improvement

Cloud computing is becoming a very popular word in industry and is receiving a large amount of attention from the research community. Replica management is one of the most important issues in the cloud, which can offer fast data access time, high data availability and reliability. By keeping all replicas active, the replicas may enhance system task successful execution rate if the replicas and requests are reasonably distributed. However, appropriate replica placement in a large-scale, dynamically scalable and totally virtualized data centers is much more complicated. To provide cost-effective availability, minimize the response time of applications and make load balancing for cloud storage, a new replica placement is proposed. The replica placement is based on five important parameters: mean service time, failure probability, load variance, latency and storage usage. However, replication should be used wisely because the storage size of each site is limited. Thus, the site must keep only the important replicas.We also present a new replica replacement strategy based on the availability of the file, the last time the replica was requested, number of access, and size of replica. We evaluate our algorithm using the CloudSim simulator and find that it offers better performance in comparison with other algorithms in terms of mean response time, effective network usage, load balancing, replication frequency, and storage usage.     

Layered virtual machine migration algorithm for network resource balancing in cloud computing

Due to the increasing sizes of cloud data centers, the number of virtual machines (VMs) and applications rises quickly. The rapid growth of large scale Internet services results in unbalanced load of network resource. The bandwidth utilization rate of some physical hosts is too high, and this causes network congestion. This paper presents a layered VM migration algorithm (LVMM). At first, the algorithm will divide the cloud data center into several regions according to the bandwidth utilization rate of the hosts. Then we balance the load of network resource of each region by VM migrations, and ultimately achieve the load balance of network resource in the cloud data center. Through simulation experiments in different environments, it is proved that the LVMMalgorithm can effectively balance the load of network resource in cloud computing.     

BCDC: A High-Performance,Server-Centric Data Center Network

The capability of the data center network largely decides the performance of cloud computing. However, the number of servers in the data center network becomes increasingly huge, because of the continuous growth of the application requirements. The performance improvement of cloud computing faces great challenges of how to connect a large number of servers in building a data center network with promising performance. Traditional tree-based data center networks have issues of bandwidth bottleneck, failure of single switch, etc. Recently proposed data center networks such as DCell, FiConn, and BCube, have larger bandwidth and better fault-tolerance with respect to traditional tree-based data center networks. Nonetheless, for DCell and FiConn, the fault-tolerant length of path between servers increases in case of failure of switches; BCube requires higher performance in switches when its scale is enlarged. Based on the above considerations, we propose a new server-centric data center network, called BCDC, based on crossed cube with excellent performance. Then, we study the connectivity of BCDC networks. Furthermore, we propose communication algorithms and fault-tolerant routing algorithm of BCDC networks. Moreover, we analyze the performance and time complexities of the proposed algorithms in BCDC networks. Our research will provide the basis for design and implementation of a new family of data center networks.     

Exploring High-Performance Architecture for Data Center Networks

As a critical infrastructure of cloud computing, data center networks (DCNs) directly determine the service performance of data centers, which provide computing services for various applications such as big data processing and artificial intelligence. However, current architectures of data center networks suffer from a long routing path and a low fault tolerance between source and destination servers, which is hard to satisfy the requirements of high-performance data center networks. Based on dual-port servers and Clos network structure, this paper proposed a novel architecture to construct high-performance data center networks. Logically, the proposed architecture is constructed by inserting a dual-port server into each pair of adjacent switches in the fabric of switches, where switches are connected in the form of a ring Clos structure. We describe the structural properties of in terms of network scale, bisection bandwidth, and network diameter. architecture inherits characteristics of its embedded Clos network, which can accommodate a large number of servers with a small average path length. The proposed architecture embraces a high fault tolerance, which adapts to the construction of various data center networks. For example, the average path length between servers is 3.44, and the standardized bisection bandwidth is 0.8 in (32, 5). The result of numerical experiments shows that enjoys a small average path length and a high network fault tolerance, which is essential in the construction of high-performance data center networks.