多媒体云计算平台关键技术研究
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摘要
云计算在近几年取得了迅速的发展,使用云平台承载各种大规模服务已经成为了信息产业发展的大势所趋。此外,随着多媒体技术的进步,互联网上也涌现出了大量新型多媒体业务并在广大用户中普及。在这样的两个大背景下,如何使用云计算平台承载多媒体业务,便成为了一个很有研究价值的问题。
从多媒体业务自身的特性来看,对运算能力要求较高的特征,进一步增加了使用云计算的必要性。然而,多媒体业务的固有特性需要在设计云计算平台时对其进行针对性考虑,现有的普通云计算平台在这方面做得颇为不足。本文由此出发,结合多媒体业务的固有特性,重点研究了针对多媒体业务的云计算平台中的关键技术,力争解决多媒体业务在云计算平台中部署时存在的问题。具体说来,论文的主要研究内容和创新点如下:
1.对现有的云计算平台和新型多媒体业务进行了调查和研究,总结了新型多媒体业务的特点,分析了现有云计算平台在承载这些业务时面临的问题和存在的不足。
2.提出了一种针对多媒体云计算的网络构架。针对现有云计算平台在网络和基础构架方面支持多媒体业务时面临的基础设施支持不足、对异构终端支持不足等问题,本文提出了一种无中心的专用拓扑结构,设计了针对该拓扑的节点和服务一体化管理模块、基于RPC协议的软件构架、基于代理服务器的多媒体内容感知缓存框架等关键模块,专门针对多媒体云平台上的业务特性和异构终端特性做出了优化。实验结果表明,平台设计具有合理性,平台中使用的各项技术也具有有效性。
3.提出了一种应用于多媒体云计算平台的并行化流量控制机制。为了解决多媒体云计算平台中大规模数据流量的有效管理问题,并针对云计算对不同用户和终端按照不同优先级进行分级服务的特点,本文提出了使用基于HTB流量控制技术的基本方案。为了进一步解决HTB由于性能缺陷而无法在云平台中使用的问题,本文研究了传统HTB处理速率的瓶颈,继而采用无锁FIFO技术,设计了新的算法,实现了一种流水线风格的无锁并行化HTB。实验结果表明,该并行化HTB在处理能力上有着大幅度提升,同时能够维持良好的稳定性,因而完全胜任多媒体云这种有着巨大数据流量的平台。
4.提出了一种多媒体云平台中的并行化深度包检测机制。针对多媒体云计算平台中大量实时数据流所带来的安全隐患,本文提出了在平台中使用深度包检测技术。针对将该技术用于多媒体云平台时存在的稳定性和速度缺陷,本文分析了正则式规则中的长度限制这一影响稳定性的主要因素,研究了“重叠匹配表达式”在生成自动机时所带来的空间消耗问题,针对性地提出了BSPM算法。在此基础上,本文设计了一种并行化深度包检测机制,针对不同类型表达式设置了计数预处理、普通DFA匹配、BSPM匹配几个模块,使之可以同时运行。实验结果表明,该机制在提高稳定性上有显著效果,同时还可以保持较好的处理速度,从而在综合性能上有着很优越的表现。
As a new born IT concept, cloud computing is developing rapidly in recent years. Deploying large scale services in cloud is becoming the major trend of IT industry. Moreover, as a result of multimedia technologies‘development, many new types of multimedia applications have merged on the Internet, which are already accepted by common people.
On the basis of the two points, using cloud for multimedia applications is considered as a new effective serving mode, which also meets the requirement of high computing intensity of multimedia applications. To solve the problem that current cloud computing platform lacks consideration for multimedia applications, in this paper the key technology in a multimedia cloud computing platform has been researched, aiming at solving the problems of deploying multimedia applications in cloud. The main content and achievements can be described as follows:
1. In this paper the current cloud computing platforms and multimedia applications have been researched and concluded, which gives a description on the characteristics of modern multimedia applications, and the problems of deploying these applications in the current cloud.
2. A novel architecture of multimedia cloud computing platform‘s network has been proposed in this paper. Aiming at solving the problem of lacking basic support for multimedia application, insufficient consideration for heterogeneous terminals and so on。This architecture uses a no center topology specially designed for the platform. Several key modules and technologies including the content and service oriented DHT management module, the RPC architecture, and the media content aware proxy cache technology are adopted. The experiment results indicate the rationality of platform network architecture design, and the effectiveness of the technologies applied.
3. A parallel traffic control mechanism for multimedia cloud computing platform has been proposed to manage the tremendous traffics in the platform effectively. Because cloud computing need to serve different levels of users with different qualities, in this paper HTB is proposed as the basic traffic management module for multimedia cloud, which can both provide service with different levels and make full use of resources. To break the bottleneck of HTB‘s processing speed and make it capable for cloud computing, the new algorithm of HTB has been designed. As a result, the concurrent region of traditional HTB can be reduced as much as possible, and a novel pipeline-based lock-free parallel HTB can be realized after applying of lock-free FIFOs. The experiments results show that the parallel HTB can make an obvious improvement on the traditional HTB, and keep stability in the mean time. So it is capable for multimedia cloud computing platform.
4. A novel parallel DPI mechanism has been proposed in this paper for the real-time security in multimedia cloud computing. To make DPI suitable for cloud computing, it is necessary to solve its problem on stability and speed. So it is analyzed that the main reason for DPI‘s instability is the length-restricted regular expressions. To solve the problem, a novel algorithm called BSPM has been proposed. Based on this, a parallel DPI mechanism consisting of pre-process module, DFA module and BPSM module has been designed to process different kinds of expressions simultaneously. From the experiment results it is shown that the new mechanism has notable improvement on the stability, and keeps a well performance in processing speed.
从多媒体业务自身的特性来看,对运算能力要求较高的特征,进一步增加了使用云计算的必要性。然而,多媒体业务的固有特性需要在设计云计算平台时对其进行针对性考虑,现有的普通云计算平台在这方面做得颇为不足。本文由此出发,结合多媒体业务的固有特性,重点研究了针对多媒体业务的云计算平台中的关键技术,力争解决多媒体业务在云计算平台中部署时存在的问题。具体说来,论文的主要研究内容和创新点如下:
1.对现有的云计算平台和新型多媒体业务进行了调查和研究,总结了新型多媒体业务的特点,分析了现有云计算平台在承载这些业务时面临的问题和存在的不足。
2.提出了一种针对多媒体云计算的网络构架。针对现有云计算平台在网络和基础构架方面支持多媒体业务时面临的基础设施支持不足、对异构终端支持不足等问题,本文提出了一种无中心的专用拓扑结构,设计了针对该拓扑的节点和服务一体化管理模块、基于RPC协议的软件构架、基于代理服务器的多媒体内容感知缓存框架等关键模块,专门针对多媒体云平台上的业务特性和异构终端特性做出了优化。实验结果表明,平台设计具有合理性,平台中使用的各项技术也具有有效性。
3.提出了一种应用于多媒体云计算平台的并行化流量控制机制。为了解决多媒体云计算平台中大规模数据流量的有效管理问题,并针对云计算对不同用户和终端按照不同优先级进行分级服务的特点,本文提出了使用基于HTB流量控制技术的基本方案。为了进一步解决HTB由于性能缺陷而无法在云平台中使用的问题,本文研究了传统HTB处理速率的瓶颈,继而采用无锁FIFO技术,设计了新的算法,实现了一种流水线风格的无锁并行化HTB。实验结果表明,该并行化HTB在处理能力上有着大幅度提升,同时能够维持良好的稳定性,因而完全胜任多媒体云这种有着巨大数据流量的平台。
4.提出了一种多媒体云平台中的并行化深度包检测机制。针对多媒体云计算平台中大量实时数据流所带来的安全隐患,本文提出了在平台中使用深度包检测技术。针对将该技术用于多媒体云平台时存在的稳定性和速度缺陷,本文分析了正则式规则中的长度限制这一影响稳定性的主要因素,研究了“重叠匹配表达式”在生成自动机时所带来的空间消耗问题,针对性地提出了BSPM算法。在此基础上,本文设计了一种并行化深度包检测机制,针对不同类型表达式设置了计数预处理、普通DFA匹配、BSPM匹配几个模块,使之可以同时运行。实验结果表明,该机制在提高稳定性上有显著效果,同时还可以保持较好的处理速度,从而在综合性能上有着很优越的表现。
As a new born IT concept, cloud computing is developing rapidly in recent years. Deploying large scale services in cloud is becoming the major trend of IT industry. Moreover, as a result of multimedia technologies‘development, many new types of multimedia applications have merged on the Internet, which are already accepted by common people.
On the basis of the two points, using cloud for multimedia applications is considered as a new effective serving mode, which also meets the requirement of high computing intensity of multimedia applications. To solve the problem that current cloud computing platform lacks consideration for multimedia applications, in this paper the key technology in a multimedia cloud computing platform has been researched, aiming at solving the problems of deploying multimedia applications in cloud. The main content and achievements can be described as follows:
1. In this paper the current cloud computing platforms and multimedia applications have been researched and concluded, which gives a description on the characteristics of modern multimedia applications, and the problems of deploying these applications in the current cloud.
2. A novel architecture of multimedia cloud computing platform‘s network has been proposed in this paper. Aiming at solving the problem of lacking basic support for multimedia application, insufficient consideration for heterogeneous terminals and so on。This architecture uses a no center topology specially designed for the platform. Several key modules and technologies including the content and service oriented DHT management module, the RPC architecture, and the media content aware proxy cache technology are adopted. The experiment results indicate the rationality of platform network architecture design, and the effectiveness of the technologies applied.
3. A parallel traffic control mechanism for multimedia cloud computing platform has been proposed to manage the tremendous traffics in the platform effectively. Because cloud computing need to serve different levels of users with different qualities, in this paper HTB is proposed as the basic traffic management module for multimedia cloud, which can both provide service with different levels and make full use of resources. To break the bottleneck of HTB‘s processing speed and make it capable for cloud computing, the new algorithm of HTB has been designed. As a result, the concurrent region of traditional HTB can be reduced as much as possible, and a novel pipeline-based lock-free parallel HTB can be realized after applying of lock-free FIFOs. The experiments results show that the parallel HTB can make an obvious improvement on the traditional HTB, and keep stability in the mean time. So it is capable for multimedia cloud computing platform.
4. A novel parallel DPI mechanism has been proposed in this paper for the real-time security in multimedia cloud computing. To make DPI suitable for cloud computing, it is necessary to solve its problem on stability and speed. So it is analyzed that the main reason for DPI‘s instability is the length-restricted regular expressions. To solve the problem, a novel algorithm called BSPM has been proposed. Based on this, a parallel DPI mechanism consisting of pre-process module, DFA module and BPSM module has been designed to process different kinds of expressions simultaneously. From the experiment results it is shown that the new mechanism has notable improvement on the stability, and keeps a well performance in processing speed.
引文
[1] Wikipedia. Cloud Computing [EB/OL]. http://en.wikipedia.org/wiki/Cloud_computing.
[2]陈康,郑纬民. 2009.云计算:系统实例与研究现状[J].软件学报. 20(5):1337-1348.
[3] OpenNEbula Project [EB/OL]. http://www.opennebula.org/.
[4] 10gen [EB/OL]. http://www.10gen.com/.
[5] Cloud computing in the Enterprise [EB/OL]. http://csis.pace.edu/~marchese/CS865/Cloud comput- ing in the enterprise.ppt.
[6] Deelman E, Singh G, Livny M. 2008. The Cost of Doing Science on the Cloud: The Montage Example [C]. In Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis, 1-12. IEEE Austin, USA.
[7] Armbrust M, et al. 2009. Above the Clouds: A Berkeley View of Cloud Computing [R]. Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Report No. UCB/EECS-2009-28, CA, USA, 2009.
[8] Leavitt N. 2009. Is cloud computing really ready for prime time [J]. IEEE Computer Society 42, issue 1: 15-20.
[9] Rajiv Ranjan, Buyya R. 2008. Decentralized Overlay for Federation of Enterprise Clouds [EB/OL]. http://www.eucalyptus.com/enterprise/info/cloud-myths-dispelled
[10] Grossman R L, Gu Yunhong. 2009. On the Varieties of Clouds for Data Intensive Computing [J]. IEEE Data Engineering Bulletin, 32(1): 44-50.
[11]刘鹏. 2009.云计算发展现状[EB/OL]. http://www.chinacloud.cn/show.aspx?id=754&cid=11.
[12] Wang Lizhe, Tao Jie, Kunze M. 2008. Scientific Cloud Computing: Early Definition and Experience [C]. In the Proceedings of HPCC‘08, 825-830. IEEE Dalian, China.
[13] Google doc [EB/OL]. http://docs.google.com/.
[14] Windows Azure [EB/OL]. http://www.microsoft.com/windowsazure/.
[15] Amazon EC2 [EB/OL]. http://aws.amazon.com/ec2/.
[16] Buyya R, Yeo C S, Venugopal S, et al. 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility [J]. Future GenerationComputer Systems, 25, issue 6: 599-616.
[17] Buyya R, Yeo C S, Venugopal S. 2008. Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities [C]. In the 10th IEEE International Conference on High Performance Computing and Communications, 5-13. IEEE Dalian, China.
[18] Borthakur D. The hadoop distributed file system: Architecture and design [EB/OL]. http://hadoop.apache.org/common/docs/r0.18.3/hdfs_design.html.
[19] Enomalism [EB/OL]. http://www.enomalism.com/.
[20] Nurmi D, et.al. 2009. The eucalyptus open-source cloud-computing system [C]. In Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 124-131. IEEE Washington, DC, USA.
[21] Nimbus [EB/OL]. http://www.nimbusproject.org/.
[22] Ohlman B, Eriksson A, Rene Rembarz. 2009. What Networking of Information Can Do for Cloud Computing [C]. In Proceedings of the 18th IEEE International Workshops on Enabling Techonologies: Intrastructures for Collaborative Enterprises, 78-83. IEEE Groningen, Holland.
[23] Nair M K, Gopalakrishna V. 2009. ?CloudCop‘: Putting network-admin on cloud nine towards Cloud Computing for Network Monitoring [C]. In the IEEE International Conference on Internet Multimedia Services Architecture and Applications, 1-6. IEEE Bangalore, India.
[24] Hao Fang, Lakshman T V, Mukherjee S, et al. 2010. Secure cloud computing with a virtualized network infrastructure [C]. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing. Boston, USA.
[25] Wang Shu-Ching, Yan Kuo-Qin, Liao Wen-Pin, et al. 2010. Towards a Load Balancing in a Three-level Cloud Computing Network [C]. In the 3rd IEEE International Conference on Computer Science and Information Technology, 108-113. IEEE Chengdu, China.
[26] Miyamoto T, Hayashi M, Tanaka H. 2009. Customizing Network Functions for High Performance Cloud Computing [C]. In the 8th IEEE International Symposium on Network Computing and Applications, 130-133. IEEE Cambridge, USA.
[27] Brodkin J. 2009. Gartner: Seven cloud-computing security risks [EB/OL]. http://www.infoworld.com/d/securitycentral/gartner-seven-cloud-computing-security-risks-853/.
[28] Zhuo H, Sheng Z, Nenghai Y. 2011. Time-Bound Ticket-Based Mutual Authentication Scheme for Cloud Computing [J]. International Journal of Computers, Communications & Control, 6, no.2: 227-235.
[29] Zhuo H, Nenghai Y. 2010. A Multiple-Replica Remote Data Possession Checking Protocol with Public Verifiability [C]. In Proceedings of ISDPE 2010, 84-89. IEEE Buffalo/Niagara Falls, USA.
[30] Zhuo H, Nenghai Y. 2010. A Security Enhanced Remote Password Authentication Scheme using Smart Card [C]. In Proceedings of ISDPE 2010, 56-60. IEEE Buffalo/Niagara Falls, USA.
[31] Zhuo H, Sheng Z, Nenghai Y. 2011. A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability [J]. IEEE Transactions on Knowledge and Data Engineering, 07 Mar.
[32] Wang C, Wang Q, Ren K, et al. 2009. Ensuring data storage security in cloud computing [C]. In Proceedings of IWQoS‘09, 1-9. IEEE Charleston, South Carolina, USA.
[33] Wang C, Wang Q, Ren K, et al. 2010. Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing [C]. In Proceedings of IEEE INFOCOM‘2010, 1-9. IEEE San Diego, CA, USA.
[34] Harauz J, Kaufman L M, Potter B. 2009. Data Security in the World of Cloud Computing [J]. IEEE Security & Privacy, 7, issue 4: 61-64.
[35] Pearson S. 2009. Taking account of privacy when designing cloud computing services [C]. In ICSE Workshop on Software Engineering Challenges of Cloud Computing, 44-52. IEEE Vancouver, Canada.
[36] Feng J, Chen Y, Liu P. 2010. Bridging the Missing Link of Cloud Data Storage Security in AWS [C]. In IEEE 7th Consumer Communications and Networking Conference (CCNC), 1-2. IEEE Las Vegas, USA.
[37] Jensen M, Schwenk J O, Gruschka N, et al. 2009. On Technical Security Issues in Cloud Computing [C]. In IEEE International Conference on Cloud Computing, 109-116. IEEE Bangalore, India.
[38] Yildiz M, Abawajy J, Ercan T, et al. 2009. A Layered Security Approach for Cloud Computing Infrastructure [C]. In the 10th International Symposium on Pervasive Systems,Algorithms, and Networks, 763-767. IEEE Kaohsiung, China.
[39] Arshad J, Townend P, Xu J. 2009. Quantification of Security for Compute Intensive Workloads in Clouds [C]. In the 15th International Conference on Parallel and Distributed Systems, 479-486. IEEE Shenzhen, China.
[40] Santos N, Gummadi K P, Rodrigues R. 2009. Towards trusted cloud computing [C]. In Proceedings of HotCloud 2009. San Diego, CA, US.
[41] Wei J, Zhang X, Ammons G, et al. 2009. Managing security of virtual machine images in a cloud environment [C]. In Proceedings of the ACM workshop on Cloud computing security 2009, 91-96. Chicago, IL, USA.
[42] Christodorescu M, Sailer R, Schales D L, et al. 2009. Cloud Security Is Not (Just) Virtualization Security [C]. In Proceedings of the ACM workshop on Cloud computing security 2009, 97-102. Chicago, IL, US.
[43] Lombardi F, Pietro R D. 2010. Transparent Security for Cloud [C]. In Proceedings of ACM SAC‘10, 414-415. ACM Sierre, Switzerland.
[44] Jung Y, Chung M. 2010. Adaptive Security Management Model in the Cloud Computing Environment [C]. In IEEE the 12th International Conference on Advanced Communication Technology, 1664-1669. IEEE Gangwon-Do, Korea.
[45] Chaves S A, Westphall C B, Lamin F R. 2010. SLA Perspective in Security Management for Cloud Computing [C]. In the 6th International Conference on Networking and Services, 201-217. IEEE Cancun, Mexico.
[46] Gruschka N, Iacono L L. 2009. Vulnerable Cloud: SOAP Message Security Validation Revisited [C]. In Proceedings of the IEEE International Conference on Web Services, 625-631. IEEE Los Angeles, USA.
[47] GIS [EB/OL]. http://en.wikipedia.org/wiki/GIS/.
[48] CBIR [EB/OL]. http://en.wikipedia.org/wiki/CBIR/.
[49] Augmented Reality [EB/OL]. http://en.wikipedia.org/wiki/Augmented_reality/.
[50] IBR [EB/OL]. http://en.wikipedia.org/wiki/Image-based_modeling_and_rendering/.
[51] Magnor M A. 2005. Vedio Based Rendering [M]. A K Peters Ltd, ISBN 1-56881-224-2.
[52] CDN [EB/OL]. http://en.wikipedia.org/wiki/CDN/.
[53] Dean J, Ghemawat S. 2008. MapReduce: simplified data processing on large clusters [J]. Communications of the ACM - 50th anniversary issue, 51, issue 1: 2008.
[54] DHT [EB/OL]. http://en.wikipedia.org/wiki/Dht/.
[55] Napster [EB/OL]. http://www.napster.com/.
[56] Freenet [EB/OL]. http://freenetproject.org/.
[57] Gnutella [EB/OL]. http://en.wikipedia.org/wiki/Gnutella.
[58] Ratnasamy S, Francis P, Handley M, et al. 2001. A scalable content-addressable network [C]. In Proceedings of ACM SIGCOMM 2001, 161-172. San Diego, USA.
[59] Balakrishnan H, Kaashoek M F, Karger D, et al. 2003. Looking up data in P2P systems [J]. Communications of the ACM, 46, no.2: 43-48.
[60] Rowstron A, Druschel P. 2001. Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems [C]. In Proceedings of the 18th IFIP/ACM International Conference on Distributed Systems Platforms. ACM Heidelberg, Germany, 2001.
[61] Hildrum K, Kubatowicz J D, Rao S, et al. 2002. Distributed Object Location in a Dynamic Network [J]. Theory of Computer Systems, 37, no.3: 405-440.
[62] Maymounkov P, Mazieres D. 2002. Kademlia: A Peer-to-Peer Information System Based on the XOR Metric [C]. In Proceedings of the 1st International Workshop on Peer-to-Peer Systems, 53-56. IEEE Cambridge, USA.
[63] Barish G, Obraczke K. 2002. World Wide Web caching: trends and techniques [J]. IEEE Communications Magazine, 38, issue 5: 178-184.
[64] Michel S, Nguyen K, Rosenstein A, et al. 1998. Adaptive web caching: towards a new global caching architecture [J]. Computer Networks & ISDN Systems, 30, issue 22-23: 2169-2177.
[65] Gwertzman J, Seltzer M. 1995. The case for geographical push caching [C]. In Proceedings of the 5th Annual Workshop on Hot Operating Systems, 51-55. Orcas Island, USA.
[66] Cao P, Zhang J, Beach K. 1998. Active cache: Caching dynamic contents on the web [C]. In Proceedings of IFIP International Conference on Distributed Systems Platforms and Open Distributed Processing, 373-387. IEEE Lake District, England.
[67] Yu Fang, Zhang Qian. 2003. QoS-Adaptive Proxy Caching for Multimedia Streaming Over the Internet [J]. IEEE Transctions on Circuits and Systems for Video Technology, 13, no.3: 257-269.
[68] Shi Shu, Jeon W J, Nahrstedt K, et al. 2009. Real-Time Remote Rendering of 3D Video for Mobile Devices [C]. In Proceedings of ACM Internationl conference on Multimedia 2009: 391-400. ACM Beijing, China.
[69] IETF RFC 2475 [EB/OL]. http://www.ietf.org/rfc/rfc2475.txt.
[70] IETF RFC 2215 [EB/OL]. http://www.ietf.org/rfc/rfc2215.txt.
[71] Tanenbaum A S. 2003. Computer Networks [M]. 4th Edition. ISBN 0-13-166836-6, Prentice Hall PTR, 2003, 400.
[72] Turner J. 1986. New directions in communications [J]. IEEE Communications Magazine, 24, issue 10: 8-15.
[73] Tanenbaum A S. 2003. Computer Networks [M]. 4th Edition, ISBN 0-13-166836-6, Prentice Hall PTR, 2003, 401.
[74] ATM Forum. 1995. ISBN 0-13-393828-X. The User Network Interface (UNI) v. 3.1 [S]. Prentice Hall PTR.
[75] ITU-T. 2004. Recommendation I.371. Traffic control and congestion control in B ISDN [S]. 87.
[76] TC-pfifo [EB/OL]. http://linux.die.net/man/8/tc-pfifo.
[77] TC-sfq [EB/OL]. http://linux.die.net/man/8/tc-sfq.
[78] TC-tbf [EB/OL]. http://linux.die.net/man/8/tc-tbf.
[79] TC-prio [EB/OL]. http://linux.die.net/man/8/tc-prio.
[80] Floyd S, Jacobson V. 1995. Link-sharing and Resource Management Models for Packet Networks [J]. IEEE Transactions on Networking, 3, no. 4: 365-386.
[81] HTB home [EB/OL]. http:// luxik.cdi.cz/~devik/qos/htb/.
[82] CNET News.com. Intel pledges 80 cores in five years [EB/OL]. http://news.com.com/2100-1006_3-6119618.html.
[83] Gajinov V, Zyulkyarov F, Cristal A, et al. 2009. QuakeTM: Parallelizing a Complex Serial Application Using Transactional Memory [C]. In Proceedings of ACM ICS‘09, 126-135. ACM New York, USA.
[84] Zyulkyarov F, Gajinov V, Unsal O, et al. 2009. Atomic Quake: Using Transactional Memory in an Interactive Multiplayer Game Server [C]. In Proceedings of ACM PPoPP 2009, 25-34. ACM New York, USA.
[85] Calin Cascava, Blundell Colin, Maged Michael, et.al. 2008. SoftwareTransactional Memory: Why is it Only a Research Toy? [J]. CACM, 51, no.11: 40-46.
[86] Kulkarni M, Carribault P, Pingali K, et.al. 2008. Scheduling strategies for optimistic parallel execution of irregular programs [J]. In SPAA‘08, 217-228. ACM Munich, Germany.
[87] Wu Peng, et al. 2009. Compiler and Runtime Techniques for Software Transactional Memory Optimization [J]. Journal of Concurrency and Computation: Practice & Experience,. 21, issue 1: pp.7-23.
[88] Scherer W N, Lea D, Scott M L. 2009. Scalable Synchronous Queues [J]. CACM, 52, no.5: 100-111.
[89] Alexander Dokumentov. 2006. Lock-free Interprocess Communication [EB/OL]. http://www.ddj.com/cpp/189401457.
[90] Fraser K, Harris T. 2007. Concurrent programming without locks [J]. ACM Transactions on Computer Systems, 25 (2): 1-61.
[91] Maged Michael. 2002. High Performance Dynamic Lock-Free Hash Tables and List-Based Sets [C]. In SPAA‘02, 73-82. ACM Manitoba, Canada.
[92] Valois J D. 1995. Lock-free linked lists using compare-and-swap [C]. In Proceedings of the 14th ACM Symposium on Principles of Distributed Computing, 214-222.
[93] Doherty S, Herligy M, Luchangco V, et al. 2004. Bring Practical Lock-Free Synchronization to 64-Bit Applications [C]. In Proceedings of ACM PODC‘04, 31-39. ACM Newfoundland, Canada.
[94] Giacomoni J, Moseley T, Vachharajani M. 2008. Fastforward for efficient pipeline parallelism: A cache-optimized concurrent lock-free queue [C]. In PPoPP‘08, 43-52. ACM New York, NY, USA.
[95] LesLie Lamport. 1983. Specifying concurrent program modules [J]. ACM Transactions on Programming Languages and Systems, 5, no. 2: 190-222.
[96] Wang Junchang, Cheng Haipeng, Hua Bei, Tang Xinan. 2009. Practice of Parallelizing Network Applications on Multi-core Architectures [C]. In Proceedings of ACM ICS‘09, 204-213. ACM New York, USA.
[97] Maged Michael. 2004. ABA prevention using single-word instructions [R]. Technical Report RC 230089, IBM T. J. Watson Research Center, January 2004.
[98] Lever C, Borheham D. 2000. malloc() Performance in a Multithreaded Linux Environment [C]. In USENIX 2000 Annual Technical Conference: FREENIX Track.
[99] Maged Michael. 2004. Scalable Lock-Free Dynamic Memory Allocation [J], ACM SIGPLAN Notices, 39, issue 6: 35-46.
[100] Cheng Haipeng, Chen Zheng, Hua Bei, Tang Xina. 2008. Scalable Packet ClassificationUsing Interpreting—A Cross-platform Multi-core Solution [C]. In Proceedings of ACM PPoPP'08, 33-42. ACM Salt Lake City, USA.
[101] Hu Xianghui, Tang Xinan, Hua Bei. 2006. High-Performance IPv6 Forwarding Algorithm for Multi-core and Multithreaded Network Processor [C]. In Proceedings of ACM PPoPP‘06, 168-177. ACM New York, USA.
[102] Clare B. 2007. Application-Level Abstractions for Lock-Free Data Sharing [EB/OL]. http://www.ddj.com/cpp/205200452?pgno=1.
[103] Friedl J. 2006. Mastering Regular Expressions [M], 3rd edition, O‘Reilly, 2006, ISBN: 0596528124.
[104] Lin P C, Lin Y D, Lai Y C. 2008. Using String Matching for Deep Packet Inspection [J]. Computer Practices, April 2008: 23-28.
[105] Knuth D, Morris J H, V. Pratt. 1977. Fast pattern matching in strings [J]. SIAM Journal on Computing, 6, no.2: 323–350.
[106] Boyer R S, Moore J S. 1977. A fast string searching algorithm [J]. Communications of the ACM, 20, issue 10: 762-772.
[107] Wu S, Manber U. 1994. A fast algorithm for multi-pattern searching [R]. Tech. R. TR-94-17, Dept. of Comp. Science, Univ of Arizona, 1994.
[108] Yao A C. 1979. The Complexity of Pattern Matching for a Random String [J]. SIAM J.Computing, 8: 368-387.
[109] Horspool N. 1980. Practical Fast Searching in Strings [J]. Software Practice and Experience, 10, issue 6: 501-506.
[110] Navarro G, Raffinot M. 2002. Flexible Pattern Matching in Strings [J]. Cambridge University Press 2002.
[111] Wu S, Manber U.1992. Agrep– a fast approximate pattern matching tool [C]. In Proceedings of USENIX Winter 1992 Techinacl Conference, 153-162.
[112] Manber U, Wu S. 1994. GLIMPSE: A tool to search through entire file systems [C]. In procedings of the USENIX Winter 1994 Technial Conference, 23-32.
[113] Liu R T, et al. 2004. A Fast Algorithm for Network Processor- Based Instrusion Detection System [J]. ACM Transaction on Embedded Computing Systems, 3, issue 3: 614-633.
[114] Dharmapurikar S, et al. 2004. Deep Packet Inspection using Parallel Bloom Filters [J].IEEE computer society, 4: 52-61.
[115] Clamav [EB/OL]. http://www.clamav.net/.
[116] Alfred V Aho, Margaret J Corasick. 1975. Efficient string matching: an aid to bibliographic search [J]. Communications of the ACM, 18, issue 6: 333-340.
[117] Snort: Lightweight Intrusion Detection for Networks [EB/OL]. http://www.Snort.org/.
[118] Tuck N, et al. 2004. Deterministic Memory-Efficient String Matching Algorithms for Intrusion Detection [C]. In Proceedings of IEEE Infocom 2004, 4, 2628-2639.
[119] Tan L, Sherwood T. 2005. A High Throughput String Matching Architecture for Intrusion Detection and Prevention [C]. In ICSA 2005, 112-122. Madison, USA.
[120] Hua N, Song H, Lakshman T V. 2009. Variable-Stride Multi-Pattern Matching for Scalable Deep Packet Inspection [C]. In Proceedings of IEEE Infocom 2009, 415-423. Rio de Janeiro, Brazil.
[121] Schleimer S, Wilkerson D S, A. Aiken. 2003. Winnowing: Local algorithms for document fingerprinting [C]. In ACM SIGMOD 2003, 76-85. San Diega, USA.
[122] Sommer R, Paxion V. 2003. Enhancing byte-level network intrusion detection signatures with context [C]. In 10th ACM conference on Computer and Communication Security, 262-271. ACM New York, USA.
[123] Bro [EB/OL]. http://bor-ids.org/.
[124] TippingPoint X505 [EB/OL]. http://www.tippingpoint.com/products_ips.html/.
[125] CCisco ASA 5505 Adaptive Security Appliance [EB/OL]. http://www.cisco.com/.
[126] Citrix Application Firewall [EB/OL]. http://www.citrix.com/.
[127] Yu Fang, et al. 2006. Fast and Memory-Efficient Regular Expression Matching for Deep Packet Inspection [C]. In ACM ANCS'06, 93-102. ACM San Jose, California, USA.
[128] Kumar S, et al. 2007. Curing Regualr Expressions Matching Algorithms from Insomnia, Amnesia, and Acalculia [C]. In ANCS‘2007, 155-164. ACM New Jearsy, USA.
[129] Smith R, et al. 2008. Deflating the Big Bang: Fast and Scalable Deep Packet Inspection with Extended Finit Automata [J]. Computer Communication Review, 38, no.4: 207-218.
[130] Kumar S, Turner J, Jonathan W. 2006. Advanced algorithms for fast and scalable deep packet inspection [C]. In Proceedings of ANCS '06, 81-92. ACM San Jose, California, USA.
[131] Michela B, Patrick C. 2007. A Hybrid Finite Automaton for Practical Deep Packet Inspection [C]. In ACM CoNEXT 2007, 1-12. ACM New York, U.S.A.
[132] Smith R, Estan C, Jha S, et al. 2008. Deflating the big bang: Fast and scalable deep packet inspection with extended finite automata [C]. In Proceedings of ACM SIGCOMM 2008, 207-218. ACM Seattle, USA.
[133] Cho Y, Smith M, William H. 2008. Deep network packet filter design for reconfigurable devices [J]. ACM Trasactions on Embedded Computing Systems, 7, no.2: 1-26.
[134] Hruby T, Reeuwijk K V, Bos H. 2007. Ruler: High-speed packet matching and rewriting on NPUs [C]. In Proceedings of ANCS'207, 1-10. ACM New Jearsy, USA.
[135] Alicherry M, Muthuprasanna M, Kumar V. 2006. High speed pattern matching for network IDS/IPS [C]. In Proceedings of ICNP 2006, 187-196. IEEE Santa Barbara, California, USA.
[136] Yu Fang, Katz R H, Lakshman T V. 2004. Gigabit rate packet pattern-matching using TCAM [C]. In Proceedings of ICNP 2004, 174-183. IEEE Berlin, Germany.
[137] Guo D, Liao G, Bhuyan L N, et al. 2008. A scalable multithreaded L7-filter design for multi-core servers [C]. In Proceedings of ANCS'08, 60-68. ACM New York, USA.
[138] Liu T, Sun Y, Guo L. 2010. Fast and memory-efficient traffic classification with deep packet inspection in CMP architecture [C]. In Proceedings of IEEE NAS 2010, 208-217. IEEE Dalian, China.
[139] Sun Y, Liu H, Valgenti V C, et al. 2010. Hybrid regular expression matching for deep packet inspection on multi-core architecture [C]. In Proceedings of ICCCN 2010, 1-7. IEEE ETH Zurich, Switzerland.
[140] Jiang J, Wang X, He K, et al. 2010. Parallel architecture for high throughput DFA-based deep packet inspection [C]. In IEEE ICC 2010, 1-5. IEEE Cape Town, South Africa.
[141] Green T J, Gupta A, Miklau G, et al. 2004. Processing XML Streams with Deterministic Automata and Stream Indexes [J]. ACM Transactions on Database Systems, 29, no. 4: 752-788.
[142] Sommer R and Paxson V. 2003. Enhancing Byte-Level Network Intrusion Detection Signatures with Context [C]. In Proceedings of the 10th ACM Conference on Computer and communications security, 262-271. ACM Wastington, USA.
[143] Thompson K. 1968. Programming Techniques: Regular expression search algorithm [J]. Communications of the ACM, 11, no.6: 419-422.
[144] Hopcroft J E. 1971. An n log n algorithm for minimizing states in a _nite automaton [R].Technical Report CS-71-190, Stanford University, January 1971
[145] Yang Li, Nenghai Yu, Zheng Li, et al. 2009. APFA: Asynchronous parallel finite automaton for deep packet inspection in cloud computing [C]. In Proceedings of CloudCom 2009, 529-540. Beijing, China.
[146] Internet traffic traces [EB/OL]. http://cctf.shmoo.com.
[2]陈康,郑纬民. 2009.云计算:系统实例与研究现状[J].软件学报. 20(5):1337-1348.
[3] OpenNEbula Project [EB/OL]. http://www.opennebula.org/.
[4] 10gen [EB/OL]. http://www.10gen.com/.
[5] Cloud computing in the Enterprise [EB/OL]. http://csis.pace.edu/~marchese/CS865/Cloud comput- ing in the enterprise.ppt.
[6] Deelman E, Singh G, Livny M. 2008. The Cost of Doing Science on the Cloud: The Montage Example [C]. In Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis, 1-12. IEEE Austin, USA.
[7] Armbrust M, et al. 2009. Above the Clouds: A Berkeley View of Cloud Computing [R]. Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Report No. UCB/EECS-2009-28, CA, USA, 2009.
[8] Leavitt N. 2009. Is cloud computing really ready for prime time [J]. IEEE Computer Society 42, issue 1: 15-20.
[9] Rajiv Ranjan, Buyya R. 2008. Decentralized Overlay for Federation of Enterprise Clouds [EB/OL]. http://www.eucalyptus.com/enterprise/info/cloud-myths-dispelled
[10] Grossman R L, Gu Yunhong. 2009. On the Varieties of Clouds for Data Intensive Computing [J]. IEEE Data Engineering Bulletin, 32(1): 44-50.
[11]刘鹏. 2009.云计算发展现状[EB/OL]. http://www.chinacloud.cn/show.aspx?id=754&cid=11.
[12] Wang Lizhe, Tao Jie, Kunze M. 2008. Scientific Cloud Computing: Early Definition and Experience [C]. In the Proceedings of HPCC‘08, 825-830. IEEE Dalian, China.
[13] Google doc [EB/OL]. http://docs.google.com/.
[14] Windows Azure [EB/OL]. http://www.microsoft.com/windowsazure/.
[15] Amazon EC2 [EB/OL]. http://aws.amazon.com/ec2/.
[16] Buyya R, Yeo C S, Venugopal S, et al. 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility [J]. Future GenerationComputer Systems, 25, issue 6: 599-616.
[17] Buyya R, Yeo C S, Venugopal S. 2008. Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities [C]. In the 10th IEEE International Conference on High Performance Computing and Communications, 5-13. IEEE Dalian, China.
[18] Borthakur D. The hadoop distributed file system: Architecture and design [EB/OL]. http://hadoop.apache.org/common/docs/r0.18.3/hdfs_design.html.
[19] Enomalism [EB/OL]. http://www.enomalism.com/.
[20] Nurmi D, et.al. 2009. The eucalyptus open-source cloud-computing system [C]. In Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 124-131. IEEE Washington, DC, USA.
[21] Nimbus [EB/OL]. http://www.nimbusproject.org/.
[22] Ohlman B, Eriksson A, Rene Rembarz. 2009. What Networking of Information Can Do for Cloud Computing [C]. In Proceedings of the 18th IEEE International Workshops on Enabling Techonologies: Intrastructures for Collaborative Enterprises, 78-83. IEEE Groningen, Holland.
[23] Nair M K, Gopalakrishna V. 2009. ?CloudCop‘: Putting network-admin on cloud nine towards Cloud Computing for Network Monitoring [C]. In the IEEE International Conference on Internet Multimedia Services Architecture and Applications, 1-6. IEEE Bangalore, India.
[24] Hao Fang, Lakshman T V, Mukherjee S, et al. 2010. Secure cloud computing with a virtualized network infrastructure [C]. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing. Boston, USA.
[25] Wang Shu-Ching, Yan Kuo-Qin, Liao Wen-Pin, et al. 2010. Towards a Load Balancing in a Three-level Cloud Computing Network [C]. In the 3rd IEEE International Conference on Computer Science and Information Technology, 108-113. IEEE Chengdu, China.
[26] Miyamoto T, Hayashi M, Tanaka H. 2009. Customizing Network Functions for High Performance Cloud Computing [C]. In the 8th IEEE International Symposium on Network Computing and Applications, 130-133. IEEE Cambridge, USA.
[27] Brodkin J. 2009. Gartner: Seven cloud-computing security risks [EB/OL]. http://www.infoworld.com/d/securitycentral/gartner-seven-cloud-computing-security-risks-853/.
[28] Zhuo H, Sheng Z, Nenghai Y. 2011. Time-Bound Ticket-Based Mutual Authentication Scheme for Cloud Computing [J]. International Journal of Computers, Communications & Control, 6, no.2: 227-235.
[29] Zhuo H, Nenghai Y. 2010. A Multiple-Replica Remote Data Possession Checking Protocol with Public Verifiability [C]. In Proceedings of ISDPE 2010, 84-89. IEEE Buffalo/Niagara Falls, USA.
[30] Zhuo H, Nenghai Y. 2010. A Security Enhanced Remote Password Authentication Scheme using Smart Card [C]. In Proceedings of ISDPE 2010, 56-60. IEEE Buffalo/Niagara Falls, USA.
[31] Zhuo H, Sheng Z, Nenghai Y. 2011. A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability [J]. IEEE Transactions on Knowledge and Data Engineering, 07 Mar.
[32] Wang C, Wang Q, Ren K, et al. 2009. Ensuring data storage security in cloud computing [C]. In Proceedings of IWQoS‘09, 1-9. IEEE Charleston, South Carolina, USA.
[33] Wang C, Wang Q, Ren K, et al. 2010. Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing [C]. In Proceedings of IEEE INFOCOM‘2010, 1-9. IEEE San Diego, CA, USA.
[34] Harauz J, Kaufman L M, Potter B. 2009. Data Security in the World of Cloud Computing [J]. IEEE Security & Privacy, 7, issue 4: 61-64.
[35] Pearson S. 2009. Taking account of privacy when designing cloud computing services [C]. In ICSE Workshop on Software Engineering Challenges of Cloud Computing, 44-52. IEEE Vancouver, Canada.
[36] Feng J, Chen Y, Liu P. 2010. Bridging the Missing Link of Cloud Data Storage Security in AWS [C]. In IEEE 7th Consumer Communications and Networking Conference (CCNC), 1-2. IEEE Las Vegas, USA.
[37] Jensen M, Schwenk J O, Gruschka N, et al. 2009. On Technical Security Issues in Cloud Computing [C]. In IEEE International Conference on Cloud Computing, 109-116. IEEE Bangalore, India.
[38] Yildiz M, Abawajy J, Ercan T, et al. 2009. A Layered Security Approach for Cloud Computing Infrastructure [C]. In the 10th International Symposium on Pervasive Systems,Algorithms, and Networks, 763-767. IEEE Kaohsiung, China.
[39] Arshad J, Townend P, Xu J. 2009. Quantification of Security for Compute Intensive Workloads in Clouds [C]. In the 15th International Conference on Parallel and Distributed Systems, 479-486. IEEE Shenzhen, China.
[40] Santos N, Gummadi K P, Rodrigues R. 2009. Towards trusted cloud computing [C]. In Proceedings of HotCloud 2009. San Diego, CA, US.
[41] Wei J, Zhang X, Ammons G, et al. 2009. Managing security of virtual machine images in a cloud environment [C]. In Proceedings of the ACM workshop on Cloud computing security 2009, 91-96. Chicago, IL, USA.
[42] Christodorescu M, Sailer R, Schales D L, et al. 2009. Cloud Security Is Not (Just) Virtualization Security [C]. In Proceedings of the ACM workshop on Cloud computing security 2009, 97-102. Chicago, IL, US.
[43] Lombardi F, Pietro R D. 2010. Transparent Security for Cloud [C]. In Proceedings of ACM SAC‘10, 414-415. ACM Sierre, Switzerland.
[44] Jung Y, Chung M. 2010. Adaptive Security Management Model in the Cloud Computing Environment [C]. In IEEE the 12th International Conference on Advanced Communication Technology, 1664-1669. IEEE Gangwon-Do, Korea.
[45] Chaves S A, Westphall C B, Lamin F R. 2010. SLA Perspective in Security Management for Cloud Computing [C]. In the 6th International Conference on Networking and Services, 201-217. IEEE Cancun, Mexico.
[46] Gruschka N, Iacono L L. 2009. Vulnerable Cloud: SOAP Message Security Validation Revisited [C]. In Proceedings of the IEEE International Conference on Web Services, 625-631. IEEE Los Angeles, USA.
[47] GIS [EB/OL]. http://en.wikipedia.org/wiki/GIS/.
[48] CBIR [EB/OL]. http://en.wikipedia.org/wiki/CBIR/.
[49] Augmented Reality [EB/OL]. http://en.wikipedia.org/wiki/Augmented_reality/.
[50] IBR [EB/OL]. http://en.wikipedia.org/wiki/Image-based_modeling_and_rendering/.
[51] Magnor M A. 2005. Vedio Based Rendering [M]. A K Peters Ltd, ISBN 1-56881-224-2.
[52] CDN [EB/OL]. http://en.wikipedia.org/wiki/CDN/.
[53] Dean J, Ghemawat S. 2008. MapReduce: simplified data processing on large clusters [J]. Communications of the ACM - 50th anniversary issue, 51, issue 1: 2008.
[54] DHT [EB/OL]. http://en.wikipedia.org/wiki/Dht/.
[55] Napster [EB/OL]. http://www.napster.com/.
[56] Freenet [EB/OL]. http://freenetproject.org/.
[57] Gnutella [EB/OL]. http://en.wikipedia.org/wiki/Gnutella.
[58] Ratnasamy S, Francis P, Handley M, et al. 2001. A scalable content-addressable network [C]. In Proceedings of ACM SIGCOMM 2001, 161-172. San Diego, USA.
[59] Balakrishnan H, Kaashoek M F, Karger D, et al. 2003. Looking up data in P2P systems [J]. Communications of the ACM, 46, no.2: 43-48.
[60] Rowstron A, Druschel P. 2001. Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems [C]. In Proceedings of the 18th IFIP/ACM International Conference on Distributed Systems Platforms. ACM Heidelberg, Germany, 2001.
[61] Hildrum K, Kubatowicz J D, Rao S, et al. 2002. Distributed Object Location in a Dynamic Network [J]. Theory of Computer Systems, 37, no.3: 405-440.
[62] Maymounkov P, Mazieres D. 2002. Kademlia: A Peer-to-Peer Information System Based on the XOR Metric [C]. In Proceedings of the 1st International Workshop on Peer-to-Peer Systems, 53-56. IEEE Cambridge, USA.
[63] Barish G, Obraczke K. 2002. World Wide Web caching: trends and techniques [J]. IEEE Communications Magazine, 38, issue 5: 178-184.
[64] Michel S, Nguyen K, Rosenstein A, et al. 1998. Adaptive web caching: towards a new global caching architecture [J]. Computer Networks & ISDN Systems, 30, issue 22-23: 2169-2177.
[65] Gwertzman J, Seltzer M. 1995. The case for geographical push caching [C]. In Proceedings of the 5th Annual Workshop on Hot Operating Systems, 51-55. Orcas Island, USA.
[66] Cao P, Zhang J, Beach K. 1998. Active cache: Caching dynamic contents on the web [C]. In Proceedings of IFIP International Conference on Distributed Systems Platforms and Open Distributed Processing, 373-387. IEEE Lake District, England.
[67] Yu Fang, Zhang Qian. 2003. QoS-Adaptive Proxy Caching for Multimedia Streaming Over the Internet [J]. IEEE Transctions on Circuits and Systems for Video Technology, 13, no.3: 257-269.
[68] Shi Shu, Jeon W J, Nahrstedt K, et al. 2009. Real-Time Remote Rendering of 3D Video for Mobile Devices [C]. In Proceedings of ACM Internationl conference on Multimedia 2009: 391-400. ACM Beijing, China.
[69] IETF RFC 2475 [EB/OL]. http://www.ietf.org/rfc/rfc2475.txt.
[70] IETF RFC 2215 [EB/OL]. http://www.ietf.org/rfc/rfc2215.txt.
[71] Tanenbaum A S. 2003. Computer Networks [M]. 4th Edition. ISBN 0-13-166836-6, Prentice Hall PTR, 2003, 400.
[72] Turner J. 1986. New directions in communications [J]. IEEE Communications Magazine, 24, issue 10: 8-15.
[73] Tanenbaum A S. 2003. Computer Networks [M]. 4th Edition, ISBN 0-13-166836-6, Prentice Hall PTR, 2003, 401.
[74] ATM Forum. 1995. ISBN 0-13-393828-X. The User Network Interface (UNI) v. 3.1 [S]. Prentice Hall PTR.
[75] ITU-T. 2004. Recommendation I.371. Traffic control and congestion control in B ISDN [S]. 87.
[76] TC-pfifo [EB/OL]. http://linux.die.net/man/8/tc-pfifo.
[77] TC-sfq [EB/OL]. http://linux.die.net/man/8/tc-sfq.
[78] TC-tbf [EB/OL]. http://linux.die.net/man/8/tc-tbf.
[79] TC-prio [EB/OL]. http://linux.die.net/man/8/tc-prio.
[80] Floyd S, Jacobson V. 1995. Link-sharing and Resource Management Models for Packet Networks [J]. IEEE Transactions on Networking, 3, no. 4: 365-386.
[81] HTB home [EB/OL]. http:// luxik.cdi.cz/~devik/qos/htb/.
[82] CNET News.com. Intel pledges 80 cores in five years [EB/OL]. http://news.com.com/2100-1006_3-6119618.html.
[83] Gajinov V, Zyulkyarov F, Cristal A, et al. 2009. QuakeTM: Parallelizing a Complex Serial Application Using Transactional Memory [C]. In Proceedings of ACM ICS‘09, 126-135. ACM New York, USA.
[84] Zyulkyarov F, Gajinov V, Unsal O, et al. 2009. Atomic Quake: Using Transactional Memory in an Interactive Multiplayer Game Server [C]. In Proceedings of ACM PPoPP 2009, 25-34. ACM New York, USA.
[85] Calin Cascava, Blundell Colin, Maged Michael, et.al. 2008. SoftwareTransactional Memory: Why is it Only a Research Toy? [J]. CACM, 51, no.11: 40-46.
[86] Kulkarni M, Carribault P, Pingali K, et.al. 2008. Scheduling strategies for optimistic parallel execution of irregular programs [J]. In SPAA‘08, 217-228. ACM Munich, Germany.
[87] Wu Peng, et al. 2009. Compiler and Runtime Techniques for Software Transactional Memory Optimization [J]. Journal of Concurrency and Computation: Practice & Experience,. 21, issue 1: pp.7-23.
[88] Scherer W N, Lea D, Scott M L. 2009. Scalable Synchronous Queues [J]. CACM, 52, no.5: 100-111.
[89] Alexander Dokumentov. 2006. Lock-free Interprocess Communication [EB/OL]. http://www.ddj.com/cpp/189401457.
[90] Fraser K, Harris T. 2007. Concurrent programming without locks [J]. ACM Transactions on Computer Systems, 25 (2): 1-61.
[91] Maged Michael. 2002. High Performance Dynamic Lock-Free Hash Tables and List-Based Sets [C]. In SPAA‘02, 73-82. ACM Manitoba, Canada.
[92] Valois J D. 1995. Lock-free linked lists using compare-and-swap [C]. In Proceedings of the 14th ACM Symposium on Principles of Distributed Computing, 214-222.
[93] Doherty S, Herligy M, Luchangco V, et al. 2004. Bring Practical Lock-Free Synchronization to 64-Bit Applications [C]. In Proceedings of ACM PODC‘04, 31-39. ACM Newfoundland, Canada.
[94] Giacomoni J, Moseley T, Vachharajani M. 2008. Fastforward for efficient pipeline parallelism: A cache-optimized concurrent lock-free queue [C]. In PPoPP‘08, 43-52. ACM New York, NY, USA.
[95] LesLie Lamport. 1983. Specifying concurrent program modules [J]. ACM Transactions on Programming Languages and Systems, 5, no. 2: 190-222.
[96] Wang Junchang, Cheng Haipeng, Hua Bei, Tang Xinan. 2009. Practice of Parallelizing Network Applications on Multi-core Architectures [C]. In Proceedings of ACM ICS‘09, 204-213. ACM New York, USA.
[97] Maged Michael. 2004. ABA prevention using single-word instructions [R]. Technical Report RC 230089, IBM T. J. Watson Research Center, January 2004.
[98] Lever C, Borheham D. 2000. malloc() Performance in a Multithreaded Linux Environment [C]. In USENIX 2000 Annual Technical Conference: FREENIX Track.
[99] Maged Michael. 2004. Scalable Lock-Free Dynamic Memory Allocation [J], ACM SIGPLAN Notices, 39, issue 6: 35-46.
[100] Cheng Haipeng, Chen Zheng, Hua Bei, Tang Xina. 2008. Scalable Packet ClassificationUsing Interpreting—A Cross-platform Multi-core Solution [C]. In Proceedings of ACM PPoPP'08, 33-42. ACM Salt Lake City, USA.
[101] Hu Xianghui, Tang Xinan, Hua Bei. 2006. High-Performance IPv6 Forwarding Algorithm for Multi-core and Multithreaded Network Processor [C]. In Proceedings of ACM PPoPP‘06, 168-177. ACM New York, USA.
[102] Clare B. 2007. Application-Level Abstractions for Lock-Free Data Sharing [EB/OL]. http://www.ddj.com/cpp/205200452?pgno=1.
[103] Friedl J. 2006. Mastering Regular Expressions [M], 3rd edition, O‘Reilly, 2006, ISBN: 0596528124.
[104] Lin P C, Lin Y D, Lai Y C. 2008. Using String Matching for Deep Packet Inspection [J]. Computer Practices, April 2008: 23-28.
[105] Knuth D, Morris J H, V. Pratt. 1977. Fast pattern matching in strings [J]. SIAM Journal on Computing, 6, no.2: 323–350.
[106] Boyer R S, Moore J S. 1977. A fast string searching algorithm [J]. Communications of the ACM, 20, issue 10: 762-772.
[107] Wu S, Manber U. 1994. A fast algorithm for multi-pattern searching [R]. Tech. R. TR-94-17, Dept. of Comp. Science, Univ of Arizona, 1994.
[108] Yao A C. 1979. The Complexity of Pattern Matching for a Random String [J]. SIAM J.Computing, 8: 368-387.
[109] Horspool N. 1980. Practical Fast Searching in Strings [J]. Software Practice and Experience, 10, issue 6: 501-506.
[110] Navarro G, Raffinot M. 2002. Flexible Pattern Matching in Strings [J]. Cambridge University Press 2002.
[111] Wu S, Manber U.1992. Agrep– a fast approximate pattern matching tool [C]. In Proceedings of USENIX Winter 1992 Techinacl Conference, 153-162.
[112] Manber U, Wu S. 1994. GLIMPSE: A tool to search through entire file systems [C]. In procedings of the USENIX Winter 1994 Technial Conference, 23-32.
[113] Liu R T, et al. 2004. A Fast Algorithm for Network Processor- Based Instrusion Detection System [J]. ACM Transaction on Embedded Computing Systems, 3, issue 3: 614-633.
[114] Dharmapurikar S, et al. 2004. Deep Packet Inspection using Parallel Bloom Filters [J].IEEE computer society, 4: 52-61.
[115] Clamav [EB/OL]. http://www.clamav.net/.
[116] Alfred V Aho, Margaret J Corasick. 1975. Efficient string matching: an aid to bibliographic search [J]. Communications of the ACM, 18, issue 6: 333-340.
[117] Snort: Lightweight Intrusion Detection for Networks [EB/OL]. http://www.Snort.org/.
[118] Tuck N, et al. 2004. Deterministic Memory-Efficient String Matching Algorithms for Intrusion Detection [C]. In Proceedings of IEEE Infocom 2004, 4, 2628-2639.
[119] Tan L, Sherwood T. 2005. A High Throughput String Matching Architecture for Intrusion Detection and Prevention [C]. In ICSA 2005, 112-122. Madison, USA.
[120] Hua N, Song H, Lakshman T V. 2009. Variable-Stride Multi-Pattern Matching for Scalable Deep Packet Inspection [C]. In Proceedings of IEEE Infocom 2009, 415-423. Rio de Janeiro, Brazil.
[121] Schleimer S, Wilkerson D S, A. Aiken. 2003. Winnowing: Local algorithms for document fingerprinting [C]. In ACM SIGMOD 2003, 76-85. San Diega, USA.
[122] Sommer R, Paxion V. 2003. Enhancing byte-level network intrusion detection signatures with context [C]. In 10th ACM conference on Computer and Communication Security, 262-271. ACM New York, USA.
[123] Bro [EB/OL]. http://bor-ids.org/.
[124] TippingPoint X505 [EB/OL]. http://www.tippingpoint.com/products_ips.html/.
[125] CCisco ASA 5505 Adaptive Security Appliance [EB/OL]. http://www.cisco.com/.
[126] Citrix Application Firewall [EB/OL]. http://www.citrix.com/.
[127] Yu Fang, et al. 2006. Fast and Memory-Efficient Regular Expression Matching for Deep Packet Inspection [C]. In ACM ANCS'06, 93-102. ACM San Jose, California, USA.
[128] Kumar S, et al. 2007. Curing Regualr Expressions Matching Algorithms from Insomnia, Amnesia, and Acalculia [C]. In ANCS‘2007, 155-164. ACM New Jearsy, USA.
[129] Smith R, et al. 2008. Deflating the Big Bang: Fast and Scalable Deep Packet Inspection with Extended Finit Automata [J]. Computer Communication Review, 38, no.4: 207-218.
[130] Kumar S, Turner J, Jonathan W. 2006. Advanced algorithms for fast and scalable deep packet inspection [C]. In Proceedings of ANCS '06, 81-92. ACM San Jose, California, USA.
[131] Michela B, Patrick C. 2007. A Hybrid Finite Automaton for Practical Deep Packet Inspection [C]. In ACM CoNEXT 2007, 1-12. ACM New York, U.S.A.
[132] Smith R, Estan C, Jha S, et al. 2008. Deflating the big bang: Fast and scalable deep packet inspection with extended finite automata [C]. In Proceedings of ACM SIGCOMM 2008, 207-218. ACM Seattle, USA.
[133] Cho Y, Smith M, William H. 2008. Deep network packet filter design for reconfigurable devices [J]. ACM Trasactions on Embedded Computing Systems, 7, no.2: 1-26.
[134] Hruby T, Reeuwijk K V, Bos H. 2007. Ruler: High-speed packet matching and rewriting on NPUs [C]. In Proceedings of ANCS'207, 1-10. ACM New Jearsy, USA.
[135] Alicherry M, Muthuprasanna M, Kumar V. 2006. High speed pattern matching for network IDS/IPS [C]. In Proceedings of ICNP 2006, 187-196. IEEE Santa Barbara, California, USA.
[136] Yu Fang, Katz R H, Lakshman T V. 2004. Gigabit rate packet pattern-matching using TCAM [C]. In Proceedings of ICNP 2004, 174-183. IEEE Berlin, Germany.
[137] Guo D, Liao G, Bhuyan L N, et al. 2008. A scalable multithreaded L7-filter design for multi-core servers [C]. In Proceedings of ANCS'08, 60-68. ACM New York, USA.
[138] Liu T, Sun Y, Guo L. 2010. Fast and memory-efficient traffic classification with deep packet inspection in CMP architecture [C]. In Proceedings of IEEE NAS 2010, 208-217. IEEE Dalian, China.
[139] Sun Y, Liu H, Valgenti V C, et al. 2010. Hybrid regular expression matching for deep packet inspection on multi-core architecture [C]. In Proceedings of ICCCN 2010, 1-7. IEEE ETH Zurich, Switzerland.
[140] Jiang J, Wang X, He K, et al. 2010. Parallel architecture for high throughput DFA-based deep packet inspection [C]. In IEEE ICC 2010, 1-5. IEEE Cape Town, South Africa.
[141] Green T J, Gupta A, Miklau G, et al. 2004. Processing XML Streams with Deterministic Automata and Stream Indexes [J]. ACM Transactions on Database Systems, 29, no. 4: 752-788.
[142] Sommer R and Paxson V. 2003. Enhancing Byte-Level Network Intrusion Detection Signatures with Context [C]. In Proceedings of the 10th ACM Conference on Computer and communications security, 262-271. ACM Wastington, USA.
[143] Thompson K. 1968. Programming Techniques: Regular expression search algorithm [J]. Communications of the ACM, 11, no.6: 419-422.
[144] Hopcroft J E. 1971. An n log n algorithm for minimizing states in a _nite automaton [R].Technical Report CS-71-190, Stanford University, January 1971
[145] Yang Li, Nenghai Yu, Zheng Li, et al. 2009. APFA: Asynchronous parallel finite automaton for deep packet inspection in cloud computing [C]. In Proceedings of CloudCom 2009, 529-540. Beijing, China.
[146] Internet traffic traces [EB/OL]. http://cctf.shmoo.com.