Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

Enhancing Security by Using GIFT and ECC Encryption Method in Multi-Tenant Datacenters

Data security and user privacy have become crucial elements in multi-tenant data centers.Various traffic types in the multi-tenant data center in the cloud environment have their characteristics and requirements.In the data center network(DCN),short and long flows are sensitive to low latency and high throughput,respectively.The traditional security processing approaches,however,neglect these characteristics and requirements.This paper proposes a fine-grained security enhancement mechanism(SEM)to solve the problem of heterogeneous traffic and reduce the traffic completion time(FCT)of short flows while ensuring the security of multi-tenant traffic transmission.Specifically,for short flows in DCN,the lightweight GIFT encryption method is utilized.For Intra-DCN long flows and Inter-DCN traffic,the asymmetric elliptic curve encryption algorithm(ECC)is utilized.The NS-3 simulation results demonstrate that SEM dramatically reduces the FCT of short flows by 70%compared to several conventional encryption techniques,effectively enhancing the security and anti-attack of traffic transmission between DCNs in cloud computing environments.Additionally,SEM performs better than other encryption methods under high load and in largescale cloud environments.

VERITAS NetBackup DataCenter

VERITAS NetBackup Data Center主机级备份与恢复解决方案适合多操作系统平台，为大规模的Unix、Linux、windows和NetWare环境提供全面的数据保护。通过NetBackup Data Center可以管理所有备份和恢复任务，为企业制定完全一致的备份策略，包括为Oracle、SAP、Informix、Sybase、DB2UDB与Lotus Notes等提供的数据库识别和应用识别备份与恢复解决方案。

Is Minimizing Flow Completion Time the Optimal Way in Meeting Flow's Deadline in Datacenter Networks

In modern datacenters, the most common method to solve the network latency problem is to minimize flow completion time during the transmission process. Following the soft real-time nature, the optimization of transport latency is relaxed to meet a flow's deadline in deadline-sensitive services. However, none of existing deadline-sensitive protocols consider deadline as a constraint condition of transmission.They can only simplify the objective of meeting a flow's deadline as a deadline-aware mechanism by assigning a higher priority for tight-deadline constrained flows to finish the transmission as soon as possible, which results in an unsatisfactory effect in the condition of high fan-in degree. It drives us to take a step back and rethink whether minimizing flow completion time is the optimal way in meeting flow's deadline. In this paper, we focus on the design of a soft real-time transport protocol with deadline constraint in datacenters and present a flow-based deadline scheduling scheme for datacenter networks(FBDS).FBDS makes the unilateral deadline-aware flow transmission with priority transform into a compound centralized single-machine deadlinebased flow scheduling decision. In addition, FBDS blocks the flow sets and postpones some flows with extra time until their deadlines to make room for the new arriving flows in order to improve the deadline meeting rate. Our simulation resultson flow completion time and deadline meeting rate reveal the potential of FBDS in terms of a considerable deadline-sensitive transport protocol for deadline-sensitive interactive services.

GRSA: Service-Aware Flow Scheduling for Cloud Storage Datacenter Networks

The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.

Optimizing the Resource Utilization of Datacenter Networks with OpenFlow

Decreasing the flow completion time(FCT) and increasing the throughput are two fundamental targets in datacenter networks(DCNs), but current mechanisms mostly focus on one of the problems. In this paper, we propose OFMPC, an Open Flow based Multi Path Cooperation framework, to decrease FCT and increase the network throughput. OFMPC partitions the end-to-end transmission paths into two classes, which are low delay paths(LDPs) and high throughput paths(HTPs), respectively. Short flows are assigned to LDPs to avoid long queueing delay, while long flows are assigned to HTPs to guarantee their throughput. Meanwhile, a dynamic scheduling mechanism is presented to improve network efficiency. We evaluate OFMPC in Mininet emulator and a testbed, and the experimental results show that OFMPC can effectively decrease FCT. Besides, OFMPC also increases the throughput up to more than 84% of bisection bandwidth.

DataCenter as a Service新型工业互联网数据中心

2016年,工业互联网产业联盟发布了《工业互联网体系架构(版本1.0)》,推动产业各界在认识层面达成共识,为开展工业互联网实践提供了参考依据。2020年,工业互联网产业联盟在工业和信息化部的指导下,研究制定了《工业互联网体系架构(版本2.0)》,在继承版本1.0核心理念、要素和功能体系的基础上,从业务、功能、实施等3个视图重新定义了工业互联网的参考体系架构,见图1。

Fault diagnosis based on dial-test data in datacenter networks

The fast growth of datacenter networks,in terms of both scale and structural complexity,has led to an increase of network failure and hence brings new challenges to network management systems.As network failure such as node failure is inevitable,how to find fault detection and diagnosis approaches that can effectively restore the network communication function and reduce the loss due to failure has been recognized as an important research problem in both academia and industry.This research focuses on exploring issues of node failure,and presents a proactive fault diagnosis algorithm called heuristic breadth-first detection(HBFD),through dynamically searching the spanning tree,analyzing the dial-test data and choosing a reasonable threshold to locate fault nodes.Both theoretical analysis and simulation results demonstrate that HBFD can diagnose node failures effectively,and take a smaller number of detection and a lower false rate without sacrificing accuracy.

Closer:Scalable Load Balancing Mechanism for Cloud Datacenters

Cloud providers(e.g.,Google,Alibaba,Amazon)own large-scale datacenter networks that comprise thousands of switches and links.A loadbalancing mechanism is supposed to effectively utilize the bisection bandwidth.Both Equal-Cost Multi-Path(ECMP),the canonical solution in practice,and alternatives come with performance limitations or significant deployment challenges.In this work,we propose Closer,a scalable load balancing mechanism for cloud datacenters.Closer complies with the evaluation of technology including the deployment of Clos-based topologies,overlays for network virtualization,and virtual machine(VM)clusters.We decouple the system into centralized route calculation and distributed route decision to guarantee its flexibility and stability in large-scale networks.Leveraging In-band Network Telemetry(INT)to obtain precise link state information,a simple but efficient algorithm implements a weighted ECMP at the edge of fabric,which enables Closer to proactively map the flows to the appropriate path and avoid the excessive congestion of a single link.Closer achieves 2 to 7 times better flow completion time(FCT)at 70%network load than existing schemes that work with same hardware environment.

环回互连数据中心光网络(特邀)

【目的】针对数据中心交换网络,文章提出了一种基于商用光交换器件的环回互连数据中心光网络(LIDON)。【方法】文章所提架构主要由机架扩展模块(REM)和环回互连模块(LIM)两个光交换模块组成。数据中心中的组内和组间交换通过这两个模块共同工作来实现。通过开发波长选择开关诊断端口的新功能,LIDON具有较高的路径多样性和容错性等特点。此外,文章所提方案可以保证LIDON的可扩展性,以支持交换容量的灵活扩展。【结果】交换器件的滤波性能实验验证了LIDON交换网络分离传输组内和组间信号的可行性。对不同业务的任务完成时间的仿真验证了该架构的容错性能。【结论】测试和仿真结果展示了文章所提架构具有较高路径多样性和容错性能。

基于时延的趋势感知的无损数据中心网络拥塞控制

在当前的高速数据中心网络中,拥塞控制对于保证持续的高性能十分重要。在过去十年里,研究人员和开发人员已经探索了多种拥塞信号,比如ECN(显式拥塞通知)、RTT(往返时间)和INT(内部网络遥测)。然而,现有的大多数拥塞控制算法要么因为信号含糊而导致拥塞检测不精确,要么因为过于激进的降速而导致过多的带宽损失。本文提出了一种名为DELTA的新型拥塞控制机制,这是一种为无损数据中心网络设计的基于延迟的趋势感知方法。DELTA通过利用RTT的变化识别拥塞趋势,并相应地调整发送速率。通过分析拥塞趋势,发送端将发送速率调整至合理水平应对拥塞,同时仍然保持高带宽利用率消除拥塞。基于此,使用NS-3仿真平台对DELTA进行广泛评估。实验结果表明,DELTA在流量完成时间(FCT)和收敛速度方面均优于拥塞控制算法。

多群落粒子群优化供应链数据中心任务调度

针对数据中心规模和任务需求不断增加带来的服务效率降低等问题,提出了一种负载均衡多群落粒子群优化任务调度方法。通过改进的适应度函数对任务的最大完工时间和各机器完工时间方差进行组合优化以提升集群的负载均衡性;利用新的自适应惯性权重函数改进粒子搜索效率,提升算法收敛速率;采取新的粒子初始化方法提高初始解的质量和多样性,并利用多群落粒子协同搜索使得最终结果更加接近最优解。在阿里巴巴数据中心的公开数据集上对算法性能进行了验证和对比,实验结果表明,该方法能够提高数据中心在多样化供应链环境下的任务调度效率。

面向应用定义优先级调度的用户态协议栈研究

针对数据中心负载中混杂请求对延迟敏感型请求响应尾延迟产生干扰,而现有研究无法在不同负载场景下为延迟敏感型请求提供灵活优先调度的问题,本文提出了应用定义优先级调度的用户态协议栈。该设计利用用户态协议栈在数据中心请求处理所处的关键位置,支持上层应用根据负载特征灵活定制优先级识别与调度策略,并由协议栈为优先级识别提供数据包、传输控制协议(TCP)流等丰富的状态信息,实现了不需要改动网络协议栈,就可以对不同负载场景实现灵活的应用定义优先级识别与调度,从而避免延迟敏感型请求受到其他混杂负载请求带来的排队延迟与阻塞延迟干扰。实验结果表明,在不同的负载场景下,通过灵活准确的应用定义优先级调度,可以将延迟敏感型请求的响应尾延迟降低98.5%,有效保障了用户体验。

基于直接拥塞通告的数据中心无损网络传输控制机制

数据中心网络广泛采用基于优先级的流量控制(Priority-based Flow Control,PFC)机制来避免因缓存溢出而丢包.然而,PFC机制在保证无损传输的同时带来了队头阻塞和拥塞扩散等负面影响.近年来,一些具备端到端拥塞感知能力的传输控制协议被提出来,有效缓解了网络拥塞,减少了PFC的触发.但是在突发流量造成的瞬时拥塞场景下,这些研究工作仍会使得PFC频繁触发而导致严重的队头阻塞和拥塞扩散.针对该问题,在端到端拥塞控制基础上,提出了一种实现于交换机上的直接拥塞通告解决方案(Direct COngestion Notification,DCON),该方案在突发拥塞场景下能及时识别出与非拥塞流(与造成拥塞无关的流)共享入端口的拥塞流(真正造成拥塞的流),并从交换机直接通告发送端对该拥塞流精确地降速.实验结果表明,相比于现有的端到端拥塞控制传输协议,DCON有效避免了PFC的队头阻塞和拥塞扩散,平均流完成时间的最大降幅达到55%.

Improved Harris Hawks Optimization Algorithm Based Data Placement Strategy for Integrated Cloud and Edge Computing

Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows.The individual tasks of a scientific work-flow necessitate a diversified number of large states that are spatially located in different datacenters,thereby resulting in huge delays during data transmis-sion.Edge computing minimizes the delays in data transmission and supports the fixed storage strategy for scientific workflow private datasets.Therefore,this fixed storage strategy creates huge amount of bottleneck in its storage capacity.At this juncture,integrating the merits of cloud computing and edge computing during the process of rationalizing the data placement of scientific workflows and optimizing the energy and time incurred in data transmission across different datacentres remains a challenge.In this paper,Adaptive Cooperative Foraging and Dispersed Foraging Strategies-Improved Harris Hawks Optimization Algorithm(ACF-DFS-HHOA)is proposed for optimizing the energy and data transmission time in the event of placing data for a specific scientific workflow.This ACF-DFS-HHOA considered the factors influencing transmission delay and energy consumption of data centers into account during the process of rationalizing the data placement of scientific workflows.The adaptive cooperative and dispersed foraging strategy is included in HHOA to guide the position updates that improve population diversity and effectively prevent the algorithm from being trapped into local optimality points.The experimental results of ACF-DFS-HHOA confirmed its predominance in minimizing energy and data transmission time incurred during workflow execution.

云计算系统中数据中心的节能算法研究

简要介绍了云计算的定义和特点,重点研究了云计算数据中心的高能耗问题,对目前的节能算法进行了分类,重点综述了基于DVFS的节能算法、基于虚拟化的节能算法以及基于主机关闭/开启的节能算法,并对算法的优缺点和适用环境作了比较分析。最后总结了云计算数据中心的能耗管理中进一步的研究难题。

一种基于网络感知的虚拟机再调度算法

有效的虚拟机调度策略能够提高数据中心的资源利用率,降低运行时能耗.现有调度算法综合考虑了虚拟机在CPU、内存和网络方面的需求,通过合理部署虚拟机,以期最小化计算、存储与网络的代价.然而,在线的虚拟机部署策略较少考虑由于虚拟机退出所造成的资源利用率下降与网络延迟上升的问题.为此,文中深入研究面向网络感知的周期性资源重配置问题,提出了面向网络感知的虚拟机再调度算法,通过适当的虚拟机迁移,提高部署在虚拟机上任务的性能以及数据中心整体的网络通信效率.算法通过尽可能低代价的虚拟机迁移来提高虚拟机之间的网络通信能力,以提升虚拟机组的整体运行效率,并保持物理机占用但不显著提高.作者通过两个测试平台在真实环境中验证了算法的有效性;通过真实的数据集和模拟实验,在多种虚拟机部署算法下,对比了应用虚拟机再调度算法前后虚拟机的部署效果,验证了该算法能够以较小的代价使得高网络通信代价的任务数明显减少,虚拟机组的网络通信能力显著提高.

VxLAN在云数据中心组网的应用

梳理了数据中心云化演进对新技术、新架构的需求,对当前Vx LAN主流实现方案进行了分析。研究了Vx LAN技术在云数据中心的几种典型组网应用,并总结了Vx LAN在云数据中心规模商用尚需解决的问题。

云计算数据中心的新能源应用:研究现状与趋势

随着大规模云计算数据中心在全球范围内的广泛部署,其高能耗、高费用、高污染等问题日益突出.为了节能减排,越来越多的云服务商尝试利用绿色新能源,像太阳能或风能,为其数据中心供电.然而,不同于稳定供电的传统电网,新能源往往具有不稳定性、间歇性和随时变化等特点,这使得新能源在数据中心中的高效可靠应用面临诸多新挑战.本文剖析了新能源应用的机遇与挑战,回答了为什么、何时、何地及如何在云计算数据中心利用新能源.从绿色数据中心现状与新评价标准、新能源产电模型与预测机制、绿色数据中心能源配额规划、数据中心内负载调度机制、跨区域云数据中心间负载均衡机制五个关键方面,对新能源在云计算数据中心应用的最新研究成果进行分类、对比与总结,并展望了未来研究趋势.