On Peer-Assisted Data Dissemination in Data Center Networks: Analysis and Implementation简

Data Center Networks （DCNs） are the fundamental infrastructure for cloud computing. Driven by the massive parallel computing tasks in cloud computing, one-to-many data dissemination becomes one of the most important traffic patterns in DCNs. Many architectures and protocols are proposed to meet this demand. However, these proposals either require complicated configurations on switches and servers, or cannot deliver an optimal performance. In this paper, we propose the peer-assisted data dissemination for DCNs. This approach utilizes the rich physical connections with high bandwidths and mutli-path connections, to facilitate efficient one-to-many data dissemination. We prove that an optimal P2P data dissemination schedule exists for FatTree, a specially- designed DCN architecture. We then present a theoretical analysis of this algorithm in the general multi-rooted tree topology, a widely-used DCN architecture. Additionally, we explore the performance of an intuitive line structure for data dissemination. Our analysis and experimental results prove that this simple structure is able to produce a comparable performance to the optimal algorithm. Since DCN applications heavily rely on virtualization to achieve optimal resource sharing, we present a general implementation method for the proposed algorithms, which aims to mitigate the impact of the potentially-high churn rate of the virtual machines.     

URMG: Enhanced CBMG-Based Method for Automatically Testing Web Applications in the Cloud简

To satisfy the rapid growth of cloud technologies, a large number of web applications have been developed and deployed, and these applications are being run in clouds. Due to the scalability provided by clouds, a single web application may be concurrently visited by several millions or billions of users. Thus, the testing and performance evaluations of these applications are increasingly important. User model based evaluations can significantly reduce the manual work required, and can enable us to determine the performance of applications under real runtime environments. Hence, it has become one of the most popular evaluation methods in both industry and academia. Significant efforts have focused on building different kinds of models using mining web access logs, such as Markov models and Customer Behavior Model Graph （CBMG）. This paper proposes a new kind of model, named the User Representation Model Graph （URMG）, which is built based on CBMG. It uses an algorithm to refine CBMG and optimizes the evaluations execution process. Based on this model, an automatic testing and evaluation system for web applications is designed, implemented, and deployed in our test cloud, which is able to execute all of the analysis and testing operations using only web access logs. In our system, the error rate caused by random access to applications in the execution phase is also reduced, and the results show that the error rate of the evaluation that depends on URMG is 50% less than that which depends on CBMG.     

Mobile Internet Big Data Platform in China Unicom简

China Unicorn, the largest WCDMA 3G operator in China, meets the requirements of the historical Mobile Internet Explosion, or the surging of Mobile Internet Traffic from mobile terminals. According to the internal statistics of China Unicom, mobile user traffic has increased rapidly with a Compound Annual Growth Rate （CAGR） of 135%. Currently China Unicorn monthly stores more than 2 trillion records, data volume is over 525 TB, and the highest data volume has reached a peak of 5 PB. Since October 2009, China Unicom has been developing a home-brewed big data storage and analysis platform based on the open source Hadoop Distributed File System （HDFS） as it has a long-term strategy to make full use of this Big Data. All Mobile Internet Traffic is well served using this big data platform. Currently, the writing speed has reached 1 390 000 records per second, and the record retrieval time in the table that contains trillions of records is less than 100 ms. To take advantage of this opportunity to be a Big Data Operator, China Unicom has developed new functions and has multiple innovations to solve space and time constraint challenges presented in data processing. In this paper, we will introduce our big data platform in detail. Based on this big data platform, China Unicom is building an industry ecosystem based on Mobile Internet Big Data, and considers that a telecom operator centric ecosystem can be formed that is critical to reach prosperity in the modern communications business.     

Estimation of Cloud Node Acquisition Waseem Ahmed and Yongwei Wu简

Over the past decade, there has been a paradigm shift leading consumers and enterprises to the adoption of cloud computing services. Even though most cases are still in the early stages of transition, there has been a steady increase in the implementation of the pay-as-you-go or pay-as-you-grow models offered by cloud providers. Whether applied as an extension of virtual infrastructure, software, or platform as a service, many users are still challenged by the estimation of adequate resource allocation and the wide variations in pricing. Customers require a simple method of predicting future demand in terms of the number of nodes to be allocated in the cloud environment. In this paper, we review and discuss existing methodologies for estimating the demand for cloud nodes and their corresponding pricing policies. Based on our review, we propose a novel approach using the Hidden Markov Model to estimate the acquisition of cloud nodes.     

BPGM: A Big Graph Mining Tool简

The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel （BSP） computing model named BSP-based Parallel Graph Mining （BPGM）. This system has four sets of parallel graph mining algorithms programmed in the BSP parallel model and a well-designed workflow engine optimized for cloud computing to invoke these algorithms. Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.     

基于云计算的园区网群设计

建造校园网群的目的是:提供单一、通用和公共的网络构架,以高速有效地传输数据、声音、图像和视频等信息,满足师生的需求。本文介绍了校园网群建设的几种关键技术(包括IP over SDH、IP over WDM和千兆以太网技术)及校园网群信息中心的云计算解决方案,并对它们进行了比较分析,在此基础上,提出了建设校园网群网络系统的核心层、汇聚层、接入层三层结构的层次化设计,以及数据中心分层设计、虚拟桌面方案,并实施于实际的系统中。     

云计算:安全技术问题探讨

云计算可以为用户提供最大化资源利用的、自定义的、可靠的服务,是一种崭新的分布式计算模式.本文对云计算模式和应用做了简单介绍,给出了云计算模式自身特点以及云计算的安全技术框架.论述了该安全技术框架中的平台服务安全、网络及基础设施安全、通用安全措施、应用服务安全以及云计算的安全政策、法规、标准等等,并给出了云计算安全关键技术.最后,给出了云计算安全不只是技术问题,还需要从多方面综合来解决云计算安全问题的结论.     

浅谈云计算与图书馆数据中心

云计算代表着整个IT行业的发展大趋势,已经成为目前最受瞩目的中心话题;并且亚马逊（AMAZON）、谷歌（GOOGLE）、微软、EMC等许多国际知名IT公司都在积极地研究和部署云计算,并已经开始提供云计算商业服务。鉴于此,探讨了未来在图书馆建立云计算数据中心的可能性。     

云计算技术及其对网络的影响

云计算代表一种全新的计算模型,并从根本上改变IT服务的方方面面。云环境的主要优势在于降低数据中心的成本支出,并在需要资源时获得动态接入,而不需耗费人力、时间,不影响工作人员的效率和组织竞争力。但云计算仍然存在一些网络性能和安全的问题。所有运营云的企业、政府机构和服务提供商,还有支持云基础设施的设备提供商必须在其设计中考虑这些因素。可用云性能和安全测试工具检测云应有的性能,以实现其结果。     

云计算中数据存储安全的研究

云计算作为IT发展的下一阶段,尽管有很多的优势,如提高业务的灵活性、可扩展性、工作效率以及企业的盈利能力,但是在带来这些优势的同时也带来云计算新的安全风险和问题.在众多的安全问题中,数据的存储安全已经成为云计算安全的核心问题.针对云计算数据的特点,在对数据加密技术研究的基础之上,本文讨论数据的敏感性问题,结合数据加密方法的优点,提出将数据分为不同安全级别然后结合密码技术,从而达到数据的在云计算环境下安全存储的方案.     

Cloud Computing-Based Forensic Analysis for Collaborative Network Security Management System

Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service (DDoS) attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management (UTM) and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloud-based security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively.     

多租户云计算环境下Overlay虚拟化网络研究

从多租户网络技术需求出发,针对现有网络架构面临的困境,通过对3种Overlay技术关键特性的深入分析,以及与现有的相关网络技术进行对比,提出了基于VXLAN技术的Overlay虚拟化网络架构,给出分布式多租户跨越互联网的Overlay技术组网方案,从而解决了多租户网络互通、跨越广域网互联的问题.     

一种基于虚拟化技术增强云安全的可信赖模型

云计算的安全性问题不仅是安全技术自身问题,也涉及到云系统的可靠性、保密性、信息匮乏等相关问题.为了增强异构、独立的云计算环境的安全性,提出一种云可信赖性模型CDMV(cloud dependability model by virtualization techniques),主要贡献体现在以下两个方面:提出了一种新的具有较好可扩展性、通用性的云系统可信赖性模型CDMV,并系统分析了CDMV模型的可信赖特性;给出了云系统可信赖性的定义及可信赖性的度量指标,并予以量化分析.     

面向移动云计算的轻量级数据完整性验证方法

研究了面向移动云计算的数据完整性验证技术,依托BLS短签名算法和Merkle哈希树,提出了一种适合在移动云计算环境中部署的数据完整性验证方案.该方案针对移动云计算环境中的移动设备计算能力较低和通信传输能力较弱的情况进行设计,能以相对较少的计算量和较低的数据通信量完成可信度较高的数据完整性验证.该方案还具有支持验证外包、无需源文件块直接参与验证、验证中无状态信息保存、以及支持对云端数据的动态操作等特性,适合于移动云计算环境中面向数据的应用.     

云计算模型在铁路大规模数据处理中的应用

对铁路行业的计算资源、数据处理进行了特征分析.探讨了云计算模型中的虚拟化技术在铁路计算资源整合及MapReduce编程模型在大规模数据处理中的应用模式.采用Hadoop开源平台和Intel的虚拟化管理平台Tashi,提出和实现了基于云计算技术的铁路数据处理框架模型,并通过大规模的货票数据处理实例验证了框架模型的可扩展性和高效性.