云计算环境中多核多进程负载均衡技术的研究与应用
|
摘要
可用性是网络安全的基本要素之一,也是衡量网络安全的基本指标之一。
负载均衡是保障大型网络应用系统高可用性的关键技术之一。
现代负载均衡已经发展成为云计算的重要组成部分。与早期相比,现代负载均衡所面临的外部环境发生了显著变化,其中最为重要的三个变化是：
1、云计算技术的蓬勃发展；
2、计算机处理器由单核发展为多核：
3、云计算时代适逢计算机处理器多核化。
在云计算环境中,基于多核的负载均衡技术面临着一些新问题,其中最为重要的问题是负载均衡在云计算环境中的弹性资源管理、基于多核的多进程负载均衡会话保持以及弹性资源管理和多核多进程负载均衡的安全集成问题。
在云计算环境中,负载均衡不再管理固定的物理机器,而转为管理云中的虚拟机,这使其成为云计算中虚拟资源管理和调度的核心。综合学术界和业界研究现状,该项研究尚存在如下不足：
1、灵活性不够,没有从面向服务的角度考虑提供资源的动态部署,不能充分体现云计算弹性、按需使用资源的特性;
2、不支持趋势预测,不同程度存在资源分配明显滞后的问题,影响用户体验,严重时甚至无法满足部分用户请求。
相较于传统的单进程负载均衡架构,基于多核的多进程架构可以充分利用处理器的并行处理能力以提高系统的整体性能。但多核多进程负载均衡架构需要解决的会话保持问题较单核环境中更为复杂,目前针对该问题主要采用共享内存式锁机制来解决。这种解决方案存在如下不足：
1、不管锁机制的设计如何精妙,由于需要频繁查询、修改会话表,都会或多或少地造成系统性能的降低；
2、使用共享内存的多进程负载均衡会话保持方案需要对原有单进程负载均衡程序进行大量结构上的修改。
云计算中弹性资源管理使得云计算具备大规模水平可扩展性。多核多进程负载均衡有助于提升云计算垂直扩展和并行化处理能力。如果云计算中弹性资源管理和多核多进程负载均衡能够有机集成,将有效提高负载均衡的整体性能,从而改善云计算技术的服务能力,从某种意义上促进了云计算的发展。该项集成优势明显,但仍需要解决许多问题,其中之一就是安全集成的方法问题。
本文专注于云计算中多核负载均衡的关键问题,重点研究云计算环境中的弹性负载均衡资源管理、基于多核的负载均衡会话保持以及弹性资源管理和多核多进程负载均衡的安全集成问题。主要工作和创新如下：
1、针对现有云计算弹性负载均衡存在的不足,提出了一种基于预测的弹性负载均衡资源管理算法(TeraPELB),不仅能更加灵活地动态调配资源,而且支持基于负载的趋势预测。算法分析和仿真实验表明,随着网络负载的变动,算法能根据负载变化情况动态地调整后端服务集群的处理能力,解决了从云中申请虚拟机具有延迟性而导致迟滞甚至无法满足用户请求的问题,相较于传统的弹性负载均衡算法效果更好,具有更高的可扩展性和可用性。
2、针对多核多进程负载均衡会话保持问题,面向Linux内核,基于Hash化管理内核网络数据包传递的思想,提出并实现了一种无锁的多进程负载均衡会话保持方案。该方案避免了锁的使用,而且不需要对原有单进程负载均衡程序进行结构上的修改,能够快速地将现有单进程负载均衡程序转变为多进程架构。算法分析和实验表明,该方案提高了基于多核的负载均衡系统效率。相较于传统的共享内存式锁机制会话保持解决方案,该方案性能更好、适用性更强。
3、针对云计算中弹性资源管理和多核多进程负载均衡的安全集成方法问题,采用软件构件化思想,将云计算环境中基于预测的弹性负载均衡资源管理算法与无锁的多核多进程负载均衡解决方案有机集成到一个负载均衡器中,采取相关的安全防范措施,相辅相成,有效地提高了负载均衡器的整体性能,弥补了云计算环境中现有负载均衡器的功能缺陷。此外,鉴于无线通信网中的负载均衡技术研究处于起步阶段,本文也对其进行了简要讨论。
Availability is one of the fundamental elements of network security, and one of the basic indicators to measure network security as well.
Load balancing is one of the key technologies to guarantee high availability for large global network application system.
Load balancing has become an important part of cloud computing. Compared with that of the earlier load balancing technology, the external environment that the modern load balancing technology faces has undergone significant changes. And the three most important changes are: firstly, cloud computing technology is flourish; secondly, the architecture of computer processor changes from single core to multiple cores; thirdly, cloud computing era coincides with the architecture of the computer processor multi-core development.
In cloud computing, load balancing technology based on multi-core processor is facing some new problems, among which the most important issue are elastic load balancing resource management in cloud computing environment, load balancing based on multi-core session persistence, and security integration between elastic resource management and multi-processing load balancing based on multi-core.
In cloud computing, the back-end server of load balancing management is a virtual machine in IaaS instead of an physical one, which makes load balancing the core of virtual resource management and scheduling in cloud computing. Integrated academic and industry research status, this study is still less than the following:1) Insufficient flexibility. Not be considered from the perspective of service-oriented dynamic deployment of resources, not fully reflect the "use on demand" feature of cloud computing.2) Does not support the trend prediction. Exist to varying degrees the allocation of resources has lagged far behind, that affect the user experience, serious and even unable to meet user requests.
Compared to traditional single-processing load balancing architecture, multi-processing architecture based on multi-core can take full advantage of the processor's parallel processing capabilities to improve overall system performance. Due to the fact that session persistence is more complicated for multi-processing load balancing in multi-core environment than that in single-core environment, session persistence solutions of shared-memory locking mechanism will lead to lower system performance. Moreover, considerable structural modifications are necessary for existing single-processing load balancing procedure.
Elastic load balancing resource management in the cloud computing environment makes cloud computing with large-scale horizontal scalability. Multi-processing load balancing based on multi-core helps to improve the vertical scalability and parallel processing capabilities of cloud computing. The integration of elastic resource management and multi-processing load balancing based on multi-core in cloud computing can effectively improve the overall performance of the load balancing, so as to improve the service capabilities of cloud computing technology, and to promote the development of cloud computing. The integration has obvious advantages, but still need to solve many problems, one of which is security integration methodological issues.
This dissertation focuses on the key issues of load balancing based on multi-core in cloud computing. The main issues of the dissertation are elastic load balancing resource management in cloud computing, load balancing based on multi-core session persistence, and the security integration of elastic resource management and multi-processing load balancing based on multi-core. The main contributions and innovation are as follows:
1) To overcome the drawbacks of the existing elastic load balancing in cloud computing, an algorithm of prediction-based elastic load balancing resource management (TeraPELB) is presented. Theoretical analysis and experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraPELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. It concludes that compared with the traditional elastic load balancing algorithm, it overcome the drawback that it might lead network service response to turn slow even no response as hysteresis of the applied virtual machine from the cloud, and TeraPELB is more reasonable for providing scalability and high availability.
2) For the issue of multi-processing load balancing session maintenance in multi-core environment, the dissertation proposes and implements a lock-free multi-processing load balancing session maintenance solution in Linux kernel based on the idea of Hash management kernel network packet delivery. The given solution avoids the use of the lock, and is able to quickly transfer the load balancing of existing single-process architecture into that of multi-processing architecture without structural changes. The Theoretical analysis and experimental results show that the proposed architecture is able to efficiently improve the overall performance of load balancing in multi-core environment. Compared with the traditional session maintenance solution based on shared memory lock mechanism, the proposed solution is able to get better performance and has stronger applicability.
3) As for the issue of security integration for elastic resource management and multi-processing load balancing based on multi-core in cloud computing, the prediction-based elastic load balancing resource management algorithm and lock-free multi-processing load balancing based on multi-core solution are integrated into a load balancer using the software component ideological. The dissertation adopts the safety precautions, which are complement with each other, to effectively improve the overall performance of the load balancer, and make up for the drawbacks of the existing load balancer in cloud computing.
Besides, in the view that the research of load balancing in the wireless communication network is in its infancy, the dissertation also briefly discusses it.
负载均衡是保障大型网络应用系统高可用性的关键技术之一。
现代负载均衡已经发展成为云计算的重要组成部分。与早期相比,现代负载均衡所面临的外部环境发生了显著变化,其中最为重要的三个变化是：
1、云计算技术的蓬勃发展；
2、计算机处理器由单核发展为多核：
3、云计算时代适逢计算机处理器多核化。
在云计算环境中,基于多核的负载均衡技术面临着一些新问题,其中最为重要的问题是负载均衡在云计算环境中的弹性资源管理、基于多核的多进程负载均衡会话保持以及弹性资源管理和多核多进程负载均衡的安全集成问题。
在云计算环境中,负载均衡不再管理固定的物理机器,而转为管理云中的虚拟机,这使其成为云计算中虚拟资源管理和调度的核心。综合学术界和业界研究现状,该项研究尚存在如下不足：
1、灵活性不够,没有从面向服务的角度考虑提供资源的动态部署,不能充分体现云计算弹性、按需使用资源的特性;
2、不支持趋势预测,不同程度存在资源分配明显滞后的问题,影响用户体验,严重时甚至无法满足部分用户请求。
相较于传统的单进程负载均衡架构,基于多核的多进程架构可以充分利用处理器的并行处理能力以提高系统的整体性能。但多核多进程负载均衡架构需要解决的会话保持问题较单核环境中更为复杂,目前针对该问题主要采用共享内存式锁机制来解决。这种解决方案存在如下不足：
1、不管锁机制的设计如何精妙,由于需要频繁查询、修改会话表,都会或多或少地造成系统性能的降低；
2、使用共享内存的多进程负载均衡会话保持方案需要对原有单进程负载均衡程序进行大量结构上的修改。
云计算中弹性资源管理使得云计算具备大规模水平可扩展性。多核多进程负载均衡有助于提升云计算垂直扩展和并行化处理能力。如果云计算中弹性资源管理和多核多进程负载均衡能够有机集成,将有效提高负载均衡的整体性能,从而改善云计算技术的服务能力,从某种意义上促进了云计算的发展。该项集成优势明显,但仍需要解决许多问题,其中之一就是安全集成的方法问题。
本文专注于云计算中多核负载均衡的关键问题,重点研究云计算环境中的弹性负载均衡资源管理、基于多核的负载均衡会话保持以及弹性资源管理和多核多进程负载均衡的安全集成问题。主要工作和创新如下：
1、针对现有云计算弹性负载均衡存在的不足,提出了一种基于预测的弹性负载均衡资源管理算法(TeraPELB),不仅能更加灵活地动态调配资源,而且支持基于负载的趋势预测。算法分析和仿真实验表明,随着网络负载的变动,算法能根据负载变化情况动态地调整后端服务集群的处理能力,解决了从云中申请虚拟机具有延迟性而导致迟滞甚至无法满足用户请求的问题,相较于传统的弹性负载均衡算法效果更好,具有更高的可扩展性和可用性。
2、针对多核多进程负载均衡会话保持问题,面向Linux内核,基于Hash化管理内核网络数据包传递的思想,提出并实现了一种无锁的多进程负载均衡会话保持方案。该方案避免了锁的使用,而且不需要对原有单进程负载均衡程序进行结构上的修改,能够快速地将现有单进程负载均衡程序转变为多进程架构。算法分析和实验表明,该方案提高了基于多核的负载均衡系统效率。相较于传统的共享内存式锁机制会话保持解决方案,该方案性能更好、适用性更强。
3、针对云计算中弹性资源管理和多核多进程负载均衡的安全集成方法问题,采用软件构件化思想,将云计算环境中基于预测的弹性负载均衡资源管理算法与无锁的多核多进程负载均衡解决方案有机集成到一个负载均衡器中,采取相关的安全防范措施,相辅相成,有效地提高了负载均衡器的整体性能,弥补了云计算环境中现有负载均衡器的功能缺陷。此外,鉴于无线通信网中的负载均衡技术研究处于起步阶段,本文也对其进行了简要讨论。
Availability is one of the fundamental elements of network security, and one of the basic indicators to measure network security as well.
Load balancing is one of the key technologies to guarantee high availability for large global network application system.
Load balancing has become an important part of cloud computing. Compared with that of the earlier load balancing technology, the external environment that the modern load balancing technology faces has undergone significant changes. And the three most important changes are: firstly, cloud computing technology is flourish; secondly, the architecture of computer processor changes from single core to multiple cores; thirdly, cloud computing era coincides with the architecture of the computer processor multi-core development.
In cloud computing, load balancing technology based on multi-core processor is facing some new problems, among which the most important issue are elastic load balancing resource management in cloud computing environment, load balancing based on multi-core session persistence, and security integration between elastic resource management and multi-processing load balancing based on multi-core.
In cloud computing, the back-end server of load balancing management is a virtual machine in IaaS instead of an physical one, which makes load balancing the core of virtual resource management and scheduling in cloud computing. Integrated academic and industry research status, this study is still less than the following:1) Insufficient flexibility. Not be considered from the perspective of service-oriented dynamic deployment of resources, not fully reflect the "use on demand" feature of cloud computing.2) Does not support the trend prediction. Exist to varying degrees the allocation of resources has lagged far behind, that affect the user experience, serious and even unable to meet user requests.
Compared to traditional single-processing load balancing architecture, multi-processing architecture based on multi-core can take full advantage of the processor's parallel processing capabilities to improve overall system performance. Due to the fact that session persistence is more complicated for multi-processing load balancing in multi-core environment than that in single-core environment, session persistence solutions of shared-memory locking mechanism will lead to lower system performance. Moreover, considerable structural modifications are necessary for existing single-processing load balancing procedure.
Elastic load balancing resource management in the cloud computing environment makes cloud computing with large-scale horizontal scalability. Multi-processing load balancing based on multi-core helps to improve the vertical scalability and parallel processing capabilities of cloud computing. The integration of elastic resource management and multi-processing load balancing based on multi-core in cloud computing can effectively improve the overall performance of the load balancing, so as to improve the service capabilities of cloud computing technology, and to promote the development of cloud computing. The integration has obvious advantages, but still need to solve many problems, one of which is security integration methodological issues.
This dissertation focuses on the key issues of load balancing based on multi-core in cloud computing. The main issues of the dissertation are elastic load balancing resource management in cloud computing, load balancing based on multi-core session persistence, and the security integration of elastic resource management and multi-processing load balancing based on multi-core. The main contributions and innovation are as follows:
1) To overcome the drawbacks of the existing elastic load balancing in cloud computing, an algorithm of prediction-based elastic load balancing resource management (TeraPELB) is presented. Theoretical analysis and experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraPELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. It concludes that compared with the traditional elastic load balancing algorithm, it overcome the drawback that it might lead network service response to turn slow even no response as hysteresis of the applied virtual machine from the cloud, and TeraPELB is more reasonable for providing scalability and high availability.
2) For the issue of multi-processing load balancing session maintenance in multi-core environment, the dissertation proposes and implements a lock-free multi-processing load balancing session maintenance solution in Linux kernel based on the idea of Hash management kernel network packet delivery. The given solution avoids the use of the lock, and is able to quickly transfer the load balancing of existing single-process architecture into that of multi-processing architecture without structural changes. The Theoretical analysis and experimental results show that the proposed architecture is able to efficiently improve the overall performance of load balancing in multi-core environment. Compared with the traditional session maintenance solution based on shared memory lock mechanism, the proposed solution is able to get better performance and has stronger applicability.
3) As for the issue of security integration for elastic resource management and multi-processing load balancing based on multi-core in cloud computing, the prediction-based elastic load balancing resource management algorithm and lock-free multi-processing load balancing based on multi-core solution are integrated into a load balancer using the software component ideological. The dissertation adopts the safety precautions, which are complement with each other, to effectively improve the overall performance of the load balancer, and make up for the drawbacks of the existing load balancer in cloud computing.
Besides, in the view that the research of load balancing in the wireless communication network is in its infancy, the dissertation also briefly discusses it.
引文
[1]Chiang M L, Yang C Y, Lien S L. Kernel support for fine-grained load balancing in a web cluster providing streaming service[M]//Algorithms and Architectures for Parallel Processing. Springer Berlin Heidelberg,2012:458-472.
[2]Gepner P, Kowalik M F. Multi-core processors:New way to achieve high system performance[C]//Parallel Computing in Electrical Engineering,2006. PAR ELEC 2006. International Symposium on. IEEE,2006:9-13.
[3]裴雪兵.新型无线网络的资源管理与负载均衡策略研究[D].华中科技大学,2009.
[4]郭成城,晏蒲柳.一种异构Web服务器集群动态负载均衡算法[J].计算机学报,2005,28(2)：179-184.
[5]陈伟,张玉芳,熊忠阳.动态反馈的异构集群负载均衡算法的实现[J].重庆大学学报,2010,33(2)：73～78.
[6]Syme M, Goldie P. Optimizing network performance with content switching:server, firewall, and cache load balancing[M]. Prentice Hall Professional,2004.
[7]Zhang W. Linux virtual server for scalable network services[C]//Ottawa Linux Symposium.2000,2000.
[8]Cao Z, Wang Z, Zegura E. Performance of hashing-based schemes for internet load balancing[C]//INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. IEEE,2000,1:332-341.
[9]Karger D, Lehman E, Leighton T, et al. Consistent hashing and random trees:Distributed caching protocols for relieving hot spots on the World Wide Web[C]//Proceedings of the twenty-ninth annual ACM symposium on Theory of computing. ACM,1997:654-663.
[10]Bryhni H, Klovning E, Kure O. A comparison of load balancing techniques for scalable web servers [J]. Network, IEEE,2000,14(4):58-64.
[11]Kenthapadi K, Manku G S. Decentralized algorithms using both local and random probes for P2P load balancing[C]//SPAA.2005,5:135-144.
[12]Karger D R, Ruhl M. Simple efficient load balancing algorithms for peer-to-peer systems[C]//Proceedings of the sixteenth annual ACM symposium on Parallelism in algorithms and architectures(SPAA). ACM,2004:36-43.
[13]Shen H, Xu C Z. Locality-aware and churn-resilient load-balancing algorithms in structured peer-to-peer networks[J]. Parallel and Distributed Systems, IEEE Transactions on,2007,18(6):849-862.
[14]Fu S, Xu C Z, Shen H. Random choices for churn resilient load balancing in peer-to-peer networks[C]//Parallel and Distributed Processing,2008. IPDPS 2008. IEEE International Symposium on. IEEE,2008:1-12.
[15]Brian H, Brunschwiler T, Dill H, et al. Cloud computing[J]. Communications of the ACM,2008,51(7): 9-11.
[16]Amazon. Amazon simple storage service [EB/OL]. http://www.amazon.com/gp/browse.html? node=16427261.2011.
[17]蒋江,张民选,廖湘科.基于多种资源的负载均衡算法的研究[J].电子学报,2002,30(8)：1148-1152.
[18]Zheng G, Kale L V. Achieving high performance on extremely large parallel machines:performance prediction and load balancing[M]. University of Illinois at Urbana-Champaign,2005.
[19]Wu X, Taylor V, Lively C, et al. Performance analysis and optimization of parallel scientific applications on CMP cluster systems[C]//Parallel Processing-Workshops,2008. ICPP-W'08. International Conference on. IEEE,2008:188-195.
[20]Savage J E, Zubair M. A unified model for multicore architectures[C]//Proceedings of the 1st international forum on Next-generation multicore/manycore technologies. ACM,2008:1-12.
[21]NERSC.2006 NERSC annual report[R]. Berkeley:NERSC,2007.
[22]Armbrust M, Fox A, Griffith R, et al. A view of cloud computing[J]. Communications of the ACM, 2010,53(4):50-58.
[23]Parkhill D F. The challenge of the computer utility[M]. Reading:Addison-Wesley Publishing Company, 1966.
[24]Amazon. Amazon elastic compute cloud (Amazon EC2) [EB/OL]. http://aws.amazon.com/ec2/.2011.
[25]Keleher P.CVM:The coherent virtual machine[J]. University of Maryland,0.9 Ed,1998.
[26]Google.com. Google app engine [EB/OL]. http://code.google.com/appengine/.2011.
[27]周洪波.云计算：技术、应用、标准和商业模式[M].电子工业出版社,2011：214-228.
[28]Bechtolsheim A. Cloud computing and cloud networking[J]. Talk at UC Berkeley,2008.
[29]Carr N. Rough Type [EB/OL]. http://www.roughtype.com.2008.
[30]Cheng D. PaaS-onomics:A CIO's guide to using platform-as-a-aervice to lower costs of application initiatives while improving the business value of IT[R]. Tech. rep., LongJump,2008.
[31]Hamilton J. Perspectives[EB/OL]. http://perspectives.mvdirona.com.2008.
[32]Rangan K, Cooke A, Post J, et al. The Cloud Wars:$100+billion at stake[R]. Tech. rep., Merrill Lynch, 2008.
[33]Siegele L. Let it rise:A special report on corporate IT[M]. Economist Newspaper,2008.
[34]Amazon.com CEO Jeff Bezos on Animoto [EB/OL]. http://blog.animoto.com/2008/04/21/amazon-ceo-jeff-bezos-on-animoto/.2008.
[35]Zhao W, Wang Z, Luo Y. Dynamic memory balancing for virtual machines[J]. ACM SIGOPS Operating Systems Review,2009,43(3):37-47.
[36]Gulati A, Merchant A, Varman P J. mClock:handling throughput variability for hypervisor IO scheduling[C]//Proceedings of the 9th USENIX conference on Operating systems design and implementation. USENIX Association,2010:1-7.
[37]Waldspurger C A. Memory resource management in VMware ESX server[J]. ACM SIGOPS Operating Systems Review,2002,36(SI):181-194.
[38]黄国睿,张平,魏广博.多核处理器的关键技术及其发展趋势[J].计算机工程与设计,2009,30(10)：2414-2418.
[39]Xu Y, Wu L, Guo L, et al. An intelligent load balancing algorithm towards efficient cloud computing[C]//AI for Data Center Management and Cloud Computing.2011:27-32.
[40]Nitika M, Shaveta M, Raj G. Comparative analysis of load balancing algorithms in cloud computing[J]. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),2012, 1(3),120-124.
[41]Kaur J. Comparison of load balancing algorithms in a cloud[J]. International Journal of Engineering Research and Applications (IJERA) ISSN:2248-9622,2012,2(3),1169-1173.
[42]Fang Y.Wang F,Ge J. A task scheduling algorithm based on load balancing in cloud computing[C]// Web Information Systems and Mining, Lecture Notes in Computer Science Volume 6318.2010. 271-277.
[43]Ye K,Jiang X,Huang D, et al. Live migration of multiple virtual machines with resource reservation in cloud computing environments[C]//2011 IEEE 4th International Conference on Cloud Computing. 2011.267-274.
[44]James J, Verma B. Efficient VM load balancing algorithm for a cloud computing environment [J]. International Journal on Computer Science and Engineering (IJCSE),2012,4(9):1658-1663.
[45]Mishra M, Das A, Kulkarni P, et al. Dynamic resource management using virtual machine migrations[J]. IEEE Communications Magazine,2012,50(9):34-40.
[46]Bobroff N, Kochut A, Beaty K A. Dynamic placement of virtual machines for managing SLA violations[J]. Integrated Network Management,2007:119-128.
[47]Das R, Kephart J O, Lefurgy C, et al. Autonomic multi-agent management of power and performance in data centers[C]//In:Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS). Estoril, Portugal.2008.
[48]Kusic D, Kephart J O, Hanson J E, et al. Power and performance management of virtualized computing environments via lookahead control[C]//Autonomic computing,2008. ICAC '08. International Conference on.2008:3-12.
[49]Beloglazov A, Abawajy J H, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing[J]. Future Generation Comp. Syst.2012,28(5): 755-768.
[50]Amazon. Amazon elastic load balancing[EB/OL]. http://aws.amazon.com/elasticloadbalancing/.2012.
[51]Eucalyptus. Amazon Web Services (AWS) and Eucalyptus[EB/OL]. http://www.eucalyptus.com/learn/amazon-aws-compatibility/.2012.
[52]Citrix. XenServer:Integrate,manage and automate a virtual datacenter[EB/OL]. http://www.citrix.com/products/xenserver/overview.html.2013.
[53]Laszewski T. A Brief Introduction on Migrating to an Oracle-based Cloud Environment[EB/OL].http://syngress.com/phishwrap/phishwrap-102011/a-brief-introduction-on-migrati ng-to-an-oracle-based-cloud-environment/.2011.
[54]Openstack. Open source software for building private and public clouds[EB/OL]. http://www.openstack.org/.2012.
[55]Opennebula. The Cloud Data Center Management Solution[EB/OL]. http://www.opennebula.org/.2012.
[56]F5. Cloud Computing Solutions[EB/OL]. http://www.f5.com/solutions/cloud-computing/.2012.
[57]Arraynetworks. Cloud Service Delivery Solutions[EB/OL]. http://www.arraynetworks.com/solutions-cloud-service-delivery.html.2012.
[58]A10networks. AX Series:Cloud Computing[EB/OL]. http://www.alOnetworks.com/products/axseries-cloud_computing.php.2012.
[59]Hazelhurst S. Scientific computing using virtual high-performance computing:a case study using the Amazon elastic computing cloud[C]//Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries:riding the wave of technology. ACM,2008:94-103.
[60]Tsai W T, Sun X, Balasooriya J. Service-oriented cloud computing architecture[C]//Information Technology:New Generations (ITNG),2010 Seventh International Conference on. IEEE,2010: 684-689.
[61]Barabasi A L. The origin of bursts and heavy tails in human dynamics[J]. Nature,2005,435(7039): 207-211.
[62]Barabasi A L. Bursts:the hidden patterns behind everything we do, from your e-mail to bloody crusades[M]. Plume Books, NewYork,2011.
[63]米雪,张宁.基于网页浏览行为的分析.上海理工大学学报[J].2012,34(4)：343-346.
[64]罗芳,杨建梅,李志宏.QQ群消息中的人类行为动力学研究[J].华南理工大学学报(社会科学版).2011,13(4)：14-19.
[65]Tarreau W. Haproxy:the reliable, high performance TCP/HTTP load balancer[EB/OL]. http://haproxy.1wt.eu/.2013.
[66]Joedog. Siege Home[EB/OL]. http://www.joedog.org/index/siege-home.2012.
[67]Xen. Xen Projects[EB/OL]. http://www.xen.org/.2012.
[68]Umass. YouTube traces from the campus network[EB/OL]. http://traces.cs.umass.edu/index.php/ Network/Network.2012.
[69]Linuxvirtualserver.org. Applications of the Linux Virtual Server[EB/OL].http://www.Linuxvirtualserver. org/.2011.
[70]Stevens W R. UNIX network programming[M]. Addison-Wesley Professional,2004.
[71]Stevans W R. Advanced programming in the UNIX environment[M]. Pearson Education India,2011.
[72]Torvalds L. The Linux kernel archives[EB/OL]. https://www.kernel.org/.2012.
[73]Almesberger W. Linux network traffic control-implementation overview[EB/OL]. http://lrcwww. epfl.ch/Linux-diffserv/.2011.
[74]Wehrle K, Pahlke F, Ritter H, et al. The linux networking architecture[J]. Design and Implementation of Network Protocols in the Linux Kernel,2005.
[75]Love R. Linux kernel development[M]. Pearson Education,2010.
[76]毛德操,胡希明.Linux内核源代码情景分析[M].浙江大学出版社.2002.
[77]Bovet D P. Understanding the Linux kernel[M]. O'reilly,2007.
[78]Kim N U, Park M W, Park S H, et al. A study on effective hash-based load balancing scheme for parallel NIDS[C]//13th International Conference on Advanced Communication Technology (ICACT), 2011. Phoenix Park, Korea (South):IEEE CONFERENCE PUBLICATIONS,2011:886-890.
[79]Jiang H, Iyengar A.Nahum E,et al. Design, Implementation, and Performance of a Load Balancer for SIP Server Clusters[J]. Networking, IEEE/ACM Transactions on,2012,20(4):1190-1202.
[80]Guo D H, Bhuyan L N, Liu B. An efficient parallelized L7-filter design for multicore servers[J]. Networking, IEEE/ACM Transactions on,2012,20(5):1426-1439.
[81]Mansour Y, Nisan N, Tiwari P. The computational complexity of universal hashing[C]//Proceedings of the 22nd annual ACM symposium on theory of computing, Baltimore, USA,1990:235-243.
[82]Microsoft Corporation. Receive-side scaling enhancements in Windows Server 2008 [EB/OL]. http://www.microsoft.com/whdc/device/network/ndis_rss.mspx.2012.
[83]Krawczyk H. New hash functions for message authentication[C]//Lecture Notes in Computer Science Volume 921/1995:Advances in Cryptology-Eurocrypt'95, Saint-Malo, France,1995:301-310.
[84]Ziehau S.FreeBSD/Linux Kernel Cross Reference:sys/net/toeplitz.c[EB/OL]. http://fxr.watson.org/fxr/source/net/toeplitz.c?v=DFBSD.2012.
[85]程光,龚俭,丁伟,等.面向IP流测量的哈希算法研究[J].软件学报,2005,16(5)：652-658.
[86]Etsion Y, Tsafrir D, Kirkpatrick S, et al. Fine grained kernel logging with klogger:Experience and insights[C]//ACM SIGOPS Operating Systems Review. ACM,2007,41(3):259-272.
[87]袁清波,赵健博,陈明宇,等.多核平台共享内存操作系统性能瓶颈分析及解决[J].计算机研究与发展,2011,48(12)：2268-2276.
[88]Vmware. Vmware[EB/OL]. http://www.vmware.com/.2012.
[89]杨芙清.软件工程技术发展思索[J].软件学报,2005,16(1)：1-7.
[90]杨芙清,梅宏,李克勤.软件复用与软件构件技术[J].电子学报,1999,27(2)：68-75.
[91]杨芙清.软件复用及相关技术[J].计算机科学,1999,26(5)：1-4.
[92]杨芙清,王千祥,梅宏,陈兆良.基于复用的软件生产技术[J].中国科学(E辑),2001,31(4)：363-371.
[93]吴和生,王崇骏,谢俊元.CAN2：构件组合式神经网络[J].山东大学学报(工学版),2010,40(5)：171-178.
[94]张世琨,张文娟,常欣,王立福,杨芙清.基于软件体系结构的可复用构件制作和组装[J].软件学报,2001,12(9)：1351-1359.
[95]徐新坤,王志坚,叶枫,岳振瑜.一个基于弹性云的负载均衡方法[J].微电子学与计算机,2012,29(11)：29-32.
[96]梅宏,曹东刚.ABC-S2C:一种面向贯穿特性的构件化软件关注点分离技术[J].计算机学报,2005,28(12)：2036-2044.
[97]Tracz W. Implementation working group summary[C]//Reuse in Practice Workshop. Pittsburgh,Pennsylvania,1989:256-261.
[98]吴和生,范训礼,谢俊元.网络环境下一次性口令身份认证的研究与实现[J].计算机科学,2003,30(5)：153-156.
[99]吴和生,伍卫民,蔡圣闻,黄皓,谢俊元.分布式环境下RBAC的高效实现[J].计算机工程,2003,29(6)：134-136.
[100]Wu H S.Wang C J,Xie J Y. TeraScaler ELB-an algorithm of prediction-based elastic load balancing resource management in cloud computing[C]//The 27th IEEE International Conference on Advanced Information Networking and Applications Workshops (AINA 2013). Barcelona, Spain, March 25-28, 2013:649-654.
[101]吴和生,王崇骏,谢俊元.TeraPELB云计算中基于预测的弹性负载均衡算法.系统仿真学报[J].2013,25(8)：1751-1760转1765.
[102]吴和生,王崇骏,谢俊元.一种多核环境中无锁的多进程负载均衡会话保持方案[J].电子与信息学报,2013,35(4):982-987.
[103]Yang H, Dasdan A, Hsiao R L, et al. Map-reduce-merge:simplified relational data processing on large clusters[C]//Proceedings of the 2007 ACM SIGMOD international conference on Management of data. ACM,2007:1029-1040.
[104]杨际详,谭国真,王荣生.并行与分布式计算动态负载均衡策略综述[J].电子学报,2010,38(5)：1122-1130.
[105]Zhang Y X, Zhou Y Z. Transparent Computing:A new paradigm for pervasive computing[C]//In: Proc. of the 3rd International Conference on Ubiquitous Intelligence and Computing (UIC 2006). Springer Berlin Heidelberg,2006:1-11.
[106]Zhang Y X, Zhou Y Z.4VP+:A novel meta OS approach for streaming programs in ubiquitous computing[C]//In:Proc. of IEEE the 21st International Conference on Advanced Information Networking and Applications (AINA 2007). Los Alamitos:IEEE Computer Society,2007:394-403.
[107]Sims K. IBM introduces ready-to-use cloud computing collaboration services get clients started with cloud computing[EB/OL]. http://www-03.ibm.com/press/us/en/pressrelease/22613.wss.2007.
[108]Smith J E, Nair R. Virtual machines:versatile platforms for systems and processes[M]. Elsevier, 2005.
[109]Ranger C, Raghuraman R, Penmetsa A, et al. Evaluating MapReduce for multi-core and multiprocessor systems[C]//In:Proc. of the 13th Int'l Symp. on High-Performance Computer Architecture. Los Alamitos:IEEE Computer Society,2007:13-24.
[110]Pike R, Dorward S, Griesemer R, Quinlan S. Interpreting the data:parallel analysis with sawzall[J]. Scientific Programming Journal,2005,13(4):277-298.
[111]Nelson M, Lim B H, Hutchins G. Fast transparent migration for virtual machines[C]//In:Proc. of the USENIX 2005 Annual Technical Conf. Berkeley:USENIX Association,2005:391-394.
[112]IBM. IBM virtualization[EB/OL]. http://www.ibm.com/virtualization.2009.
[113]Isard M, Budiu M, Yu Y, et al. Dryad:distributed data-parallel programs from sequential building blocks[J]. ACM SIGOPS Operating Systems Review,2007,41(3):59-72.
[114]Geer D. Chip makers turn to multicore processors[J].Computer,2005,38(7):11-13.
[115]Galen G. What cloud computing really means. InfoWorld[EB/OL]. http://www.infoworld.com/d/cloud-computing/what-cloud-computing-really-means-031.2008.
[116]Ghemawat S, Gobioff H, Leung S T. The Google file system[J]. ACM SIGOPS Operating Systems Review. ACM,2003,37(5):29-43.
[117]Dean J, Ghemawat S. Distributed programming with Mapreduce[J]. Beautiful Code. Sebastopol: O'Reilly Media, Inc,2007:371-384.
[118]DeCandia G, Hastorun D, Jampani M, et al. Dynamo:Amazon's highly available key-value store[C]//In:Proc. of the 21st ACM Symp. on Operating Systems Principles. New York:ACM Press, 2007:205-220.
[119]Danielson K. Distinguishing cloud computing from utility computing[EB/OL]. http://www.ebizq.net/blogs/saasweek/2008/03/distinguishing_cloud_computing/.2013.
[120]陈康,郑纬民.云计算：系统实例与研究现状[J].软件学报,2009,20(5)：1337-1348.
[121]Chu L K, Tang H, Yang T, Shen K. Optimizing data aggregation for cluster-based Internet services[C]//In:Proc. of the ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming. New York:ACM Press,2003:119-130.
[122]Chai L,Gao Q,Panda D K. Understanding the impact of multi-core architecture in cluster computing: a case study with Intel dual-core system[C]//In Proc.7th IEEE CCGRID. Washington:IEEE CS Press, 2007:471-478.
[123]Chang F, Dean J, Ghemawat S, et al. Bigtable:A distributed storage system for structured data[C]// In:Proc. of the 7th USENIX Symp. on Operating Systems Design and Implementation. Berkeley: USENIX Association,2006:205-218.
[124]Clark C, Fraser K, Hansen J G,et al. Live migration of virtual machines[C]//In:Proc. of the 2nd Symp. on Networked Systems Design and Implementation. Berkeley:USENIX Association,2005: 273-286.
[125]Barham P, Dragovic B, Fraser K, et al. Xen and the art of virtualization[C]//In:Proc. of the 9th ACM Symp. on Operating Systems Principles. New York:Bolton Landing,2003:164-177.
[126]Burrows M. The chubby lock service for loosely-coupled distributed systems[C]//In:Proc. of the 7th USENIX symposium on Operating Systems Design and Implementation. Berkeley:USENIX Association,2006:335-350.
[127]Apache. Apache hadoop[EB/OL]. http://hadoop.apache.org/core/.2012.
[128]Aguilera M K, Merchant A, Shah M, et al. Sinfonia:a new paradigm for building scalable distributed systems[J].ACM SIGOPS Operating Systems Review. ACM,2007,41(6):159-174.
[2]Gepner P, Kowalik M F. Multi-core processors:New way to achieve high system performance[C]//Parallel Computing in Electrical Engineering,2006. PAR ELEC 2006. International Symposium on. IEEE,2006:9-13.
[3]裴雪兵.新型无线网络的资源管理与负载均衡策略研究[D].华中科技大学,2009.
[4]郭成城,晏蒲柳.一种异构Web服务器集群动态负载均衡算法[J].计算机学报,2005,28(2)：179-184.
[5]陈伟,张玉芳,熊忠阳.动态反馈的异构集群负载均衡算法的实现[J].重庆大学学报,2010,33(2)：73～78.
[6]Syme M, Goldie P. Optimizing network performance with content switching:server, firewall, and cache load balancing[M]. Prentice Hall Professional,2004.
[7]Zhang W. Linux virtual server for scalable network services[C]//Ottawa Linux Symposium.2000,2000.
[8]Cao Z, Wang Z, Zegura E. Performance of hashing-based schemes for internet load balancing[C]//INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. IEEE,2000,1:332-341.
[9]Karger D, Lehman E, Leighton T, et al. Consistent hashing and random trees:Distributed caching protocols for relieving hot spots on the World Wide Web[C]//Proceedings of the twenty-ninth annual ACM symposium on Theory of computing. ACM,1997:654-663.
[10]Bryhni H, Klovning E, Kure O. A comparison of load balancing techniques for scalable web servers [J]. Network, IEEE,2000,14(4):58-64.
[11]Kenthapadi K, Manku G S. Decentralized algorithms using both local and random probes for P2P load balancing[C]//SPAA.2005,5:135-144.
[12]Karger D R, Ruhl M. Simple efficient load balancing algorithms for peer-to-peer systems[C]//Proceedings of the sixteenth annual ACM symposium on Parallelism in algorithms and architectures(SPAA). ACM,2004:36-43.
[13]Shen H, Xu C Z. Locality-aware and churn-resilient load-balancing algorithms in structured peer-to-peer networks[J]. Parallel and Distributed Systems, IEEE Transactions on,2007,18(6):849-862.
[14]Fu S, Xu C Z, Shen H. Random choices for churn resilient load balancing in peer-to-peer networks[C]//Parallel and Distributed Processing,2008. IPDPS 2008. IEEE International Symposium on. IEEE,2008:1-12.
[15]Brian H, Brunschwiler T, Dill H, et al. Cloud computing[J]. Communications of the ACM,2008,51(7): 9-11.
[16]Amazon. Amazon simple storage service [EB/OL]. http://www.amazon.com/gp/browse.html? node=16427261.2011.
[17]蒋江,张民选,廖湘科.基于多种资源的负载均衡算法的研究[J].电子学报,2002,30(8)：1148-1152.
[18]Zheng G, Kale L V. Achieving high performance on extremely large parallel machines:performance prediction and load balancing[M]. University of Illinois at Urbana-Champaign,2005.
[19]Wu X, Taylor V, Lively C, et al. Performance analysis and optimization of parallel scientific applications on CMP cluster systems[C]//Parallel Processing-Workshops,2008. ICPP-W'08. International Conference on. IEEE,2008:188-195.
[20]Savage J E, Zubair M. A unified model for multicore architectures[C]//Proceedings of the 1st international forum on Next-generation multicore/manycore technologies. ACM,2008:1-12.
[21]NERSC.2006 NERSC annual report[R]. Berkeley:NERSC,2007.
[22]Armbrust M, Fox A, Griffith R, et al. A view of cloud computing[J]. Communications of the ACM, 2010,53(4):50-58.
[23]Parkhill D F. The challenge of the computer utility[M]. Reading:Addison-Wesley Publishing Company, 1966.
[24]Amazon. Amazon elastic compute cloud (Amazon EC2) [EB/OL]. http://aws.amazon.com/ec2/.2011.
[25]Keleher P.CVM:The coherent virtual machine[J]. University of Maryland,0.9 Ed,1998.
[26]Google.com. Google app engine [EB/OL]. http://code.google.com/appengine/.2011.
[27]周洪波.云计算：技术、应用、标准和商业模式[M].电子工业出版社,2011：214-228.
[28]Bechtolsheim A. Cloud computing and cloud networking[J]. Talk at UC Berkeley,2008.
[29]Carr N. Rough Type [EB/OL]. http://www.roughtype.com.2008.
[30]Cheng D. PaaS-onomics:A CIO's guide to using platform-as-a-aervice to lower costs of application initiatives while improving the business value of IT[R]. Tech. rep., LongJump,2008.
[31]Hamilton J. Perspectives[EB/OL]. http://perspectives.mvdirona.com.2008.
[32]Rangan K, Cooke A, Post J, et al. The Cloud Wars:$100+billion at stake[R]. Tech. rep., Merrill Lynch, 2008.
[33]Siegele L. Let it rise:A special report on corporate IT[M]. Economist Newspaper,2008.
[34]Amazon.com CEO Jeff Bezos on Animoto [EB/OL]. http://blog.animoto.com/2008/04/21/amazon-ceo-jeff-bezos-on-animoto/.2008.
[35]Zhao W, Wang Z, Luo Y. Dynamic memory balancing for virtual machines[J]. ACM SIGOPS Operating Systems Review,2009,43(3):37-47.
[36]Gulati A, Merchant A, Varman P J. mClock:handling throughput variability for hypervisor IO scheduling[C]//Proceedings of the 9th USENIX conference on Operating systems design and implementation. USENIX Association,2010:1-7.
[37]Waldspurger C A. Memory resource management in VMware ESX server[J]. ACM SIGOPS Operating Systems Review,2002,36(SI):181-194.
[38]黄国睿,张平,魏广博.多核处理器的关键技术及其发展趋势[J].计算机工程与设计,2009,30(10)：2414-2418.
[39]Xu Y, Wu L, Guo L, et al. An intelligent load balancing algorithm towards efficient cloud computing[C]//AI for Data Center Management and Cloud Computing.2011:27-32.
[40]Nitika M, Shaveta M, Raj G. Comparative analysis of load balancing algorithms in cloud computing[J]. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),2012, 1(3),120-124.
[41]Kaur J. Comparison of load balancing algorithms in a cloud[J]. International Journal of Engineering Research and Applications (IJERA) ISSN:2248-9622,2012,2(3),1169-1173.
[42]Fang Y.Wang F,Ge J. A task scheduling algorithm based on load balancing in cloud computing[C]// Web Information Systems and Mining, Lecture Notes in Computer Science Volume 6318.2010. 271-277.
[43]Ye K,Jiang X,Huang D, et al. Live migration of multiple virtual machines with resource reservation in cloud computing environments[C]//2011 IEEE 4th International Conference on Cloud Computing. 2011.267-274.
[44]James J, Verma B. Efficient VM load balancing algorithm for a cloud computing environment [J]. International Journal on Computer Science and Engineering (IJCSE),2012,4(9):1658-1663.
[45]Mishra M, Das A, Kulkarni P, et al. Dynamic resource management using virtual machine migrations[J]. IEEE Communications Magazine,2012,50(9):34-40.
[46]Bobroff N, Kochut A, Beaty K A. Dynamic placement of virtual machines for managing SLA violations[J]. Integrated Network Management,2007:119-128.
[47]Das R, Kephart J O, Lefurgy C, et al. Autonomic multi-agent management of power and performance in data centers[C]//In:Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS). Estoril, Portugal.2008.
[48]Kusic D, Kephart J O, Hanson J E, et al. Power and performance management of virtualized computing environments via lookahead control[C]//Autonomic computing,2008. ICAC '08. International Conference on.2008:3-12.
[49]Beloglazov A, Abawajy J H, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing[J]. Future Generation Comp. Syst.2012,28(5): 755-768.
[50]Amazon. Amazon elastic load balancing[EB/OL]. http://aws.amazon.com/elasticloadbalancing/.2012.
[51]Eucalyptus. Amazon Web Services (AWS) and Eucalyptus[EB/OL]. http://www.eucalyptus.com/learn/amazon-aws-compatibility/.2012.
[52]Citrix. XenServer:Integrate,manage and automate a virtual datacenter[EB/OL]. http://www.citrix.com/products/xenserver/overview.html.2013.
[53]Laszewski T. A Brief Introduction on Migrating to an Oracle-based Cloud Environment[EB/OL].http://syngress.com/phishwrap/phishwrap-102011/a-brief-introduction-on-migrati ng-to-an-oracle-based-cloud-environment/.2011.
[54]Openstack. Open source software for building private and public clouds[EB/OL]. http://www.openstack.org/.2012.
[55]Opennebula. The Cloud Data Center Management Solution[EB/OL]. http://www.opennebula.org/.2012.
[56]F5. Cloud Computing Solutions[EB/OL]. http://www.f5.com/solutions/cloud-computing/.2012.
[57]Arraynetworks. Cloud Service Delivery Solutions[EB/OL]. http://www.arraynetworks.com/solutions-cloud-service-delivery.html.2012.
[58]A10networks. AX Series:Cloud Computing[EB/OL]. http://www.alOnetworks.com/products/axseries-cloud_computing.php.2012.
[59]Hazelhurst S. Scientific computing using virtual high-performance computing:a case study using the Amazon elastic computing cloud[C]//Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries:riding the wave of technology. ACM,2008:94-103.
[60]Tsai W T, Sun X, Balasooriya J. Service-oriented cloud computing architecture[C]//Information Technology:New Generations (ITNG),2010 Seventh International Conference on. IEEE,2010: 684-689.
[61]Barabasi A L. The origin of bursts and heavy tails in human dynamics[J]. Nature,2005,435(7039): 207-211.
[62]Barabasi A L. Bursts:the hidden patterns behind everything we do, from your e-mail to bloody crusades[M]. Plume Books, NewYork,2011.
[63]米雪,张宁.基于网页浏览行为的分析.上海理工大学学报[J].2012,34(4)：343-346.
[64]罗芳,杨建梅,李志宏.QQ群消息中的人类行为动力学研究[J].华南理工大学学报(社会科学版).2011,13(4)：14-19.
[65]Tarreau W. Haproxy:the reliable, high performance TCP/HTTP load balancer[EB/OL]. http://haproxy.1wt.eu/.2013.
[66]Joedog. Siege Home[EB/OL]. http://www.joedog.org/index/siege-home.2012.
[67]Xen. Xen Projects[EB/OL]. http://www.xen.org/.2012.
[68]Umass. YouTube traces from the campus network[EB/OL]. http://traces.cs.umass.edu/index.php/ Network/Network.2012.
[69]Linuxvirtualserver.org. Applications of the Linux Virtual Server[EB/OL].http://www.Linuxvirtualserver. org/.2011.
[70]Stevens W R. UNIX network programming[M]. Addison-Wesley Professional,2004.
[71]Stevans W R. Advanced programming in the UNIX environment[M]. Pearson Education India,2011.
[72]Torvalds L. The Linux kernel archives[EB/OL]. https://www.kernel.org/.2012.
[73]Almesberger W. Linux network traffic control-implementation overview[EB/OL]. http://lrcwww. epfl.ch/Linux-diffserv/.2011.
[74]Wehrle K, Pahlke F, Ritter H, et al. The linux networking architecture[J]. Design and Implementation of Network Protocols in the Linux Kernel,2005.
[75]Love R. Linux kernel development[M]. Pearson Education,2010.
[76]毛德操,胡希明.Linux内核源代码情景分析[M].浙江大学出版社.2002.
[77]Bovet D P. Understanding the Linux kernel[M]. O'reilly,2007.
[78]Kim N U, Park M W, Park S H, et al. A study on effective hash-based load balancing scheme for parallel NIDS[C]//13th International Conference on Advanced Communication Technology (ICACT), 2011. Phoenix Park, Korea (South):IEEE CONFERENCE PUBLICATIONS,2011:886-890.
[79]Jiang H, Iyengar A.Nahum E,et al. Design, Implementation, and Performance of a Load Balancer for SIP Server Clusters[J]. Networking, IEEE/ACM Transactions on,2012,20(4):1190-1202.
[80]Guo D H, Bhuyan L N, Liu B. An efficient parallelized L7-filter design for multicore servers[J]. Networking, IEEE/ACM Transactions on,2012,20(5):1426-1439.
[81]Mansour Y, Nisan N, Tiwari P. The computational complexity of universal hashing[C]//Proceedings of the 22nd annual ACM symposium on theory of computing, Baltimore, USA,1990:235-243.
[82]Microsoft Corporation. Receive-side scaling enhancements in Windows Server 2008 [EB/OL]. http://www.microsoft.com/whdc/device/network/ndis_rss.mspx.2012.
[83]Krawczyk H. New hash functions for message authentication[C]//Lecture Notes in Computer Science Volume 921/1995:Advances in Cryptology-Eurocrypt'95, Saint-Malo, France,1995:301-310.
[84]Ziehau S.FreeBSD/Linux Kernel Cross Reference:sys/net/toeplitz.c[EB/OL]. http://fxr.watson.org/fxr/source/net/toeplitz.c?v=DFBSD.2012.
[85]程光,龚俭,丁伟,等.面向IP流测量的哈希算法研究[J].软件学报,2005,16(5)：652-658.
[86]Etsion Y, Tsafrir D, Kirkpatrick S, et al. Fine grained kernel logging with klogger:Experience and insights[C]//ACM SIGOPS Operating Systems Review. ACM,2007,41(3):259-272.
[87]袁清波,赵健博,陈明宇,等.多核平台共享内存操作系统性能瓶颈分析及解决[J].计算机研究与发展,2011,48(12)：2268-2276.
[88]Vmware. Vmware[EB/OL]. http://www.vmware.com/.2012.
[89]杨芙清.软件工程技术发展思索[J].软件学报,2005,16(1)：1-7.
[90]杨芙清,梅宏,李克勤.软件复用与软件构件技术[J].电子学报,1999,27(2)：68-75.
[91]杨芙清.软件复用及相关技术[J].计算机科学,1999,26(5)：1-4.
[92]杨芙清,王千祥,梅宏,陈兆良.基于复用的软件生产技术[J].中国科学(E辑),2001,31(4)：363-371.
[93]吴和生,王崇骏,谢俊元.CAN2：构件组合式神经网络[J].山东大学学报(工学版),2010,40(5)：171-178.
[94]张世琨,张文娟,常欣,王立福,杨芙清.基于软件体系结构的可复用构件制作和组装[J].软件学报,2001,12(9)：1351-1359.
[95]徐新坤,王志坚,叶枫,岳振瑜.一个基于弹性云的负载均衡方法[J].微电子学与计算机,2012,29(11)：29-32.
[96]梅宏,曹东刚.ABC-S2C:一种面向贯穿特性的构件化软件关注点分离技术[J].计算机学报,2005,28(12)：2036-2044.
[97]Tracz W. Implementation working group summary[C]//Reuse in Practice Workshop. Pittsburgh,Pennsylvania,1989:256-261.
[98]吴和生,范训礼,谢俊元.网络环境下一次性口令身份认证的研究与实现[J].计算机科学,2003,30(5)：153-156.
[99]吴和生,伍卫民,蔡圣闻,黄皓,谢俊元.分布式环境下RBAC的高效实现[J].计算机工程,2003,29(6)：134-136.
[100]Wu H S.Wang C J,Xie J Y. TeraScaler ELB-an algorithm of prediction-based elastic load balancing resource management in cloud computing[C]//The 27th IEEE International Conference on Advanced Information Networking and Applications Workshops (AINA 2013). Barcelona, Spain, March 25-28, 2013:649-654.
[101]吴和生,王崇骏,谢俊元.TeraPELB云计算中基于预测的弹性负载均衡算法.系统仿真学报[J].2013,25(8)：1751-1760转1765.
[102]吴和生,王崇骏,谢俊元.一种多核环境中无锁的多进程负载均衡会话保持方案[J].电子与信息学报,2013,35(4):982-987.
[103]Yang H, Dasdan A, Hsiao R L, et al. Map-reduce-merge:simplified relational data processing on large clusters[C]//Proceedings of the 2007 ACM SIGMOD international conference on Management of data. ACM,2007:1029-1040.
[104]杨际详,谭国真,王荣生.并行与分布式计算动态负载均衡策略综述[J].电子学报,2010,38(5)：1122-1130.
[105]Zhang Y X, Zhou Y Z. Transparent Computing:A new paradigm for pervasive computing[C]//In: Proc. of the 3rd International Conference on Ubiquitous Intelligence and Computing (UIC 2006). Springer Berlin Heidelberg,2006:1-11.
[106]Zhang Y X, Zhou Y Z.4VP+:A novel meta OS approach for streaming programs in ubiquitous computing[C]//In:Proc. of IEEE the 21st International Conference on Advanced Information Networking and Applications (AINA 2007). Los Alamitos:IEEE Computer Society,2007:394-403.
[107]Sims K. IBM introduces ready-to-use cloud computing collaboration services get clients started with cloud computing[EB/OL]. http://www-03.ibm.com/press/us/en/pressrelease/22613.wss.2007.
[108]Smith J E, Nair R. Virtual machines:versatile platforms for systems and processes[M]. Elsevier, 2005.
[109]Ranger C, Raghuraman R, Penmetsa A, et al. Evaluating MapReduce for multi-core and multiprocessor systems[C]//In:Proc. of the 13th Int'l Symp. on High-Performance Computer Architecture. Los Alamitos:IEEE Computer Society,2007:13-24.
[110]Pike R, Dorward S, Griesemer R, Quinlan S. Interpreting the data:parallel analysis with sawzall[J]. Scientific Programming Journal,2005,13(4):277-298.
[111]Nelson M, Lim B H, Hutchins G. Fast transparent migration for virtual machines[C]//In:Proc. of the USENIX 2005 Annual Technical Conf. Berkeley:USENIX Association,2005:391-394.
[112]IBM. IBM virtualization[EB/OL]. http://www.ibm.com/virtualization.2009.
[113]Isard M, Budiu M, Yu Y, et al. Dryad:distributed data-parallel programs from sequential building blocks[J]. ACM SIGOPS Operating Systems Review,2007,41(3):59-72.
[114]Geer D. Chip makers turn to multicore processors[J].Computer,2005,38(7):11-13.
[115]Galen G. What cloud computing really means. InfoWorld[EB/OL]. http://www.infoworld.com/d/cloud-computing/what-cloud-computing-really-means-031.2008.
[116]Ghemawat S, Gobioff H, Leung S T. The Google file system[J]. ACM SIGOPS Operating Systems Review. ACM,2003,37(5):29-43.
[117]Dean J, Ghemawat S. Distributed programming with Mapreduce[J]. Beautiful Code. Sebastopol: O'Reilly Media, Inc,2007:371-384.
[118]DeCandia G, Hastorun D, Jampani M, et al. Dynamo:Amazon's highly available key-value store[C]//In:Proc. of the 21st ACM Symp. on Operating Systems Principles. New York:ACM Press, 2007:205-220.
[119]Danielson K. Distinguishing cloud computing from utility computing[EB/OL]. http://www.ebizq.net/blogs/saasweek/2008/03/distinguishing_cloud_computing/.2013.
[120]陈康,郑纬民.云计算：系统实例与研究现状[J].软件学报,2009,20(5)：1337-1348.
[121]Chu L K, Tang H, Yang T, Shen K. Optimizing data aggregation for cluster-based Internet services[C]//In:Proc. of the ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming. New York:ACM Press,2003:119-130.
[122]Chai L,Gao Q,Panda D K. Understanding the impact of multi-core architecture in cluster computing: a case study with Intel dual-core system[C]//In Proc.7th IEEE CCGRID. Washington:IEEE CS Press, 2007:471-478.
[123]Chang F, Dean J, Ghemawat S, et al. Bigtable:A distributed storage system for structured data[C]// In:Proc. of the 7th USENIX Symp. on Operating Systems Design and Implementation. Berkeley: USENIX Association,2006:205-218.
[124]Clark C, Fraser K, Hansen J G,et al. Live migration of virtual machines[C]//In:Proc. of the 2nd Symp. on Networked Systems Design and Implementation. Berkeley:USENIX Association,2005: 273-286.
[125]Barham P, Dragovic B, Fraser K, et al. Xen and the art of virtualization[C]//In:Proc. of the 9th ACM Symp. on Operating Systems Principles. New York:Bolton Landing,2003:164-177.
[126]Burrows M. The chubby lock service for loosely-coupled distributed systems[C]//In:Proc. of the 7th USENIX symposium on Operating Systems Design and Implementation. Berkeley:USENIX Association,2006:335-350.
[127]Apache. Apache hadoop[EB/OL]. http://hadoop.apache.org/core/.2012.
[128]Aguilera M K, Merchant A, Shah M, et al. Sinfonia:a new paradigm for building scalable distributed systems[J].ACM SIGOPS Operating Systems Review. ACM,2007,41(6):159-174.