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.

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.

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion strategies.Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload pro-cessing.Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains unexplored.In this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed DC.The primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy.On the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming workload.The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

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.

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.

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.

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。

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。

Gyration:基于RTT测量的报文偏转拥塞控制算法

高效的拥塞控制一直是数据中心网络领域的一个重要挑战。往返时延RTT的精确测量是基于RTT的反应型拥塞控制算法的关键。基于Swift拥塞控制算法,提出一种基于RTT测量的报文偏转拥塞控制算法——Gyration,将偏转报文延迟添加到RTT计算过程,在偏转拥塞控制算法中增加偏转延迟的测量,使RTT计算更准确,更加精确地评估网络拥塞情况。实验结果表明,相比Swift算法,在重负载的Cache Follower、Data Mining、Web Search和Web Server流量模式下,Gyration将流完成时间FCT缩短了20%,80%,13%和60%,吞吐量提高了38%,6%,15%和2%,实现了对数据中心网络更加及时、精确的拥塞控制,有效缓解了数据中心网络拥塞问题。

基于可编程交换机的网内灰色故障检测技术研究进展

灰色故障是指对生产网络产生细微影响的交换机故障.然而,当这些轻微故障相互叠加或与新增故障叠加时,可能会导致整个生产网络的瘫痪.因此,检测灰色故障对生产网络的稳定性至关重要.传统解决方案关注的是在控制平面收集数据平面交换机中的流记录,并对其进行处理以检测灰色故障.然而,此类解决方案存在着不足:(1)缓存和处理大量的流记录会引入庞大的资源开销;(2)较高的检测时延无法保证灰色故障检测的时效性.近年来,可编程交换机的出现为灰色故障检测技术带来了新机遇:网络管理员可以将灰色故障检测算法部署运行至可编程交换机的线速ASIC流水线上,从而支持低开销、低时延、高精度的网内灰色故障检测技术.本文针对基于可编程交换机的网内灰色故障检测技术进行综述,在对灰色故障的概念、普遍性及对生产网络的危害进行描述的基础上,分析与讨论了现有基于可编程交换机的网内灰色故障检测技术的研究现状与进展,详细介绍每项技术的工作原理及流程,搭建真实的实验平台评估各项技术的检测指标,在文末指出了现有技术所面临的问题与挑战.

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。

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.

分布式异构多级空间数据中心的研究与设计

多年来作为国土管理工作重要依据的空间数据不能进行及时有效地更新交换,上下级之间数据不能同步,以至各级审批节点只能进行费时、费力的“定性”审批.通过对国土资源数据现状的分析,提出了树状层次结构数据中心部署模型,详细分析了数据中心体系模型和数据中心交换模型;在模型的基础上,提出了表文件级更新和地理实体全球唯一编码的记录级数据更新方式,完成了异构数据更新和传输子模块的设计,实现了多级分布式数据中心之间的数据更新机制.实践表明,在湖南省国土资源多级数据交换体系中成功应用后,省厅与长沙市局之间能够远程动态实时交换空间数据,实现了政务审批工作从定性粗放到定量精细的飞跃.