云存储多数据中心QoS保障机制研究
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摘要
云存储是云计算的核心基础组件,云存储的高可靠、高可用和高性能是云计算能够支撑各类云端业务的重要保证。基于多数据中心的云存储采用广域网分布式架构,通过遍布全球的数据中心实现数据的异地多副本存储,保证数据的高可靠和高可用；通过就近访问和数据并行下载,保证数据服务的高性能。云存储多数据中心在QoS保障与资源调度方面,和单数据中心有较大不同。目前业界尚无标准和规范,学术界也缺乏系统性研究。如何通过多数据中心之间相互协作进行资源分配,保障不同QoS级别业务并提高系统资源利用率,是当前云存储领域的研究重点。
本文以实现云存储多数据中心QoS保障,提高云存储系统整体资源利用率为目标,深入研究了如何通过优化云存储多数据中心资源调度,实现QoS保障的问题。论文结合云存储多数据中心特点,将云存储服务分解为4个主要子服务(负载均衡、存储分层、存储网关、网络传输),针对不同的子服务采用特定手段实现QoS保障与系统资源优化的双重目标。本文的主要成果和创新点包括以下5点：
(1)系统分析了云存储多数据中心资源管理的技术原理,总结了在多数据中心架构下,云存储在QoS保障和资源调度方面的研究现状和存在问题。本文对云存储在系统架构、负载均衡管理、存储分层管理、云存储网关管理、数据中心网络管理等方面的QOS保障机制和原理进行了综合分析,归纳出各自的工作思想和优缺点,指出了云存储多数据中心管理面临的挑战,并设计了一个云存储资源优化仿真平台。这是开展云存储多数据中心QoS保障和资源调度研究工作的理论和实验基础。
(2)提出QoS全局最优的云存储多数据中心负载均衡调度模型,并提出一种基于商空间的层次式负载均衡调度算法(QBHLBSA)。本文分析了云存储负载均衡机制和云存储多数据中心在负载均衡方面存在的问题,提出了一种QoS全局最优的云存储多数据中心负载均衡调度模型。模型的优化目标是保证不同QoS级别业务的性能需求,并使各个数据中心资源利用率最大化。本文结合云存储多数据中心层次化管理特点,提出了一种基于商空间的层次式负载衡调度算法。该算法可以在不同粒度上由粗至细地对云存储负载进行调度,具有更快的收敛速度、避免了传统算法极易陷入局部最优值问题。仿真结果表明,本算法可以提升云存储的系统整体资源利用率和吞吐率,并且保障高QoS优先级业务的读写性能要求。
(3)提出面向应用层QoS保障的对象分层存储系统模型,并提出一种基于定价策略的自动分层调度算法(PBACST)。为了保障云存储中高QoS优先级业务对存储性能的要求,本文建立了面向应用层QOS保障的对象分层存储系统模型。模型的优化目标是在充分考虑不同业务QoS需求、存储容量和吞吐率约束条件下最大化云存储高速缓存资源池的利用率。本文结合云存储多数据中心特点,提出了一种基于定价策略的自动分层调度算法。该算法具有分布式决策的特点,各组件相互协作地完成数据对象分层调度。仿真结果表明,本算法可以提升云存储中高速缓存资源池的利用率,并且保障高QoS优先级业务的读写性能要求。
(4)提出支持QOS的云存储多数据中心任务调度模型,并提出一种基于动态带宽分配的实时任务调度算法(DBABRTSA)。本文分析了云存储中不同QoS级别业务竞争有限的带宽资源时产生的拥塞问题,提出了一种支持QoS的多数据中心任务调度模型。模型的优化目标是保证高QoS级别实时业务的性能需求,并提升云存储系统整体吞吐性能。本文结合云存储多数据中心层次化管理特点,提出了一种基于动态带宽分配的实时任务调度算法。该算法可以按照业务类型优先级从高到低,动态分配任务流量带宽。仿真结果表明,本算法可以保障高QoS级别实时业务的读写性能要求,同时保证其它各级QoS业务对带宽使用的比例公平性,并能提升系统整体的吞吐率。
(5)提出面向QOS的数据中心间网络流量调度模型,并提出一种基于双层多粒子群的网络流量调度算法(BLMSPSOSA)。本文分析了云存储多数据中心间网络链路带宽利用不均衡的问题,提出了一种面向QOS的多数据中心间网络流量调度模型。模型的优化目标是保证不同QoS级别数据传输的性能需求,并使数据中心间网络链路带宽资源利用率最大化。本文结合云存储多数据中心层次化管理特点,提出了一种基于双层多粒子群的网络流量调度算法。该算法具有更快的收敛速度、避免了传统算法极易陷入局部最优值问题。仿真结果表明,本算法可以提升云存储多数据中心间网络链路带宽的利用率,并且保障高QoS优先级数据的传输需求。
综上所述,本文通过对不同子服务的资源优化调度实现了云存储多数据中心QoS保障,并有效提升了云存储整体资源利用率和吞吐率。
Cloud storage is the fundamental component of cloud computing, its high reliability, high availability and high performance are the key guarantee to supporting all types of cloud services. Multi-datacenter cloud storage is built on WAN based distributed architecture, it ensure the reliability and availability of data service by multiple offsite copies in data centers around the world and ensure the performance of data by visiting the nearest nodes and downloading parallelly. Multi-datacenter cloud storage differs greatly from single-datacenter one in QoS guarantee and resource scheduling. How to guarantee different levels of service QoS and improve the system resource utilization is the key research area in cloud storage.
In this dissertation, in order to guarantee the multi-datacenter QoS and improve the overall resource utilization of cloud storage, we studied the technique of optimizing multi-datacenter cloud storage resource scheduling to achieve QoS guarantee. According to the characteristics of multi-datacenter cloud storage, we divided the cloud storage service into four major sub-services which are load balancing, storage tiering, storage gateway and network transmission, and achieved the dual goals of QoS guarantees and system resource optimization for different sub-services by using specific means. The main achievements and innovations of this dissertation are in the following:
(1) Analyzed the technical principles of multi-datacenter cloud storage resource management, concluded the research status and problems of QoS guarantees and resource scheduling of cloud storage in multi-datacenter architecture. We conducted a comprehensive analysis of cloud storage architecture and QoS guarantee mechanism and principles in load balancing, storage tiering, storage gateway and data center network, summarized the advantages and disadvantages of each method, pointed out the challenges of multi-datacenter cloud storage management and designed a resource optimization simulation platform. It is the theoretical and experimental basis for studing the multi-datacenter QoS guarantee and resource scheduling technique of cloud storage.
(2) Proposed a QoS global optimal cloud storage multi-datacenter load balancing scheduling model and proposed a quotient space-based hierarchical load balancing scheduling algorithm (QBHLBSA). We analyzed the load balancing mechanism of cloud storage and its problems in multi-datacenter architecture, proposed a QoS global optimal cloud storage multi-datacenter load balancing scheduling model. The optimization objective is to guarantee the performance requirements of different QoS level applications and maximizing the resource utilization of each data center. According to the hierarchical management characteristics of multi-datacenter cloud storage, we proposed a quotient space-based hierarchical load balancing scheduling algorithm. The algorithm can schedule the storage load from coarse to fine granularity, with faster convergence time and avoid falling into local optimal values easily by using traditional algorithms. The simulation results showed that the algorithm can improve resource utilization and overall system throughput of cloud storage, guarantee the read and write performance requirements of high QoS level applications.
(3) Proposed an object tiering storage system model for application layer QoS guarantee and proposed a pricing based automated cloud storage tiering algorithm (PBACST). In order to protect the performance requirements of high QoS level applications in cloud storage, we build an application layer QoS guarantee oriented object tiering storage system model. The optimization objective of model is maximizing the utilization of cloud storage high speed cache resource pool under the constraints of different applications' QoS requirements, storage capacity and throughput. Based on the characteristics of multi-datacenter cloud storage, we proposed a pricing based automated tiering scheduling algorithm. The algorithm can make decision distributively, the components make the object tiering scheduling cooperatively. The simulation results showed that the algorithm can improve the utilization of high speed cache resource pool and guarantee the read and write performance requirements of high QoS level applications.
(4) Proposed a QoS guaranteed cloud storage multi-datacenter task scheduling model and proposed a dynamic bandwidth allocation based real-time task scheduling algorithm (DBABRTSA). We analyzed the congestion problem when different QoS level application competing with limited bandwidth resources, proposed a QoS guaranteed multi-datacenter task scheduling model. The optimization objective of model is guaranteeing the performence requirements of high QoS level real-time application and improving the overall thoughtput of cloud storage. According to the hierarchical management characteristics of multi-datacenter cloud storage, we proposed a dynamic bandwidth allocation based real-time task scheduling algorithm. The algorithm can dynamically allocate task bandwidth according to application priorities. The simulation results showed that the algorithm can guarantee the read and write performance requirements of high QoS level applications and the proportional usage fairness of other QoS level applications, meanwhile improve the overall system thoughtput.
(5) Proposed a QoS guaranteed inter-datacenter network traffic scheduling model and proposed a bi-level multi-swarm PSO based network traffic scheduling algorithm (BLMSPSOSA). We analyzed the uneven utilization problem of network link bandwidth in multi-datacenter cloud storage, proposed a QoS guaranteed inter-datacenter network traffic scheduling model. The optimization objective of model is guaranteeing the transmission performance of different QoS level data and maximizing the resource utilization of inter-datacenter network. According to the hierarchical management characteristics of multi-datacenter cloud storage, we proposed a bi-level multi-swarm PSO based network traffic scheduling algorithm. The algorithm has faster convergence time and can avoid falling into local optimal values easily by using traditional algorithms. The simulation results showed that the algorithm can improve the resource utilization of inter-datacenter network and guarantee the transmission requirements of high QoS level data.
In conclusion, by optimizing the resource scheduling of different sub-services, we achieved the multi-datacenter QoS guarantee and effectively improved the overall resource utilization and throughput of cloud storage.
本文以实现云存储多数据中心QoS保障,提高云存储系统整体资源利用率为目标,深入研究了如何通过优化云存储多数据中心资源调度,实现QoS保障的问题。论文结合云存储多数据中心特点,将云存储服务分解为4个主要子服务(负载均衡、存储分层、存储网关、网络传输),针对不同的子服务采用特定手段实现QoS保障与系统资源优化的双重目标。本文的主要成果和创新点包括以下5点：
(1)系统分析了云存储多数据中心资源管理的技术原理,总结了在多数据中心架构下,云存储在QoS保障和资源调度方面的研究现状和存在问题。本文对云存储在系统架构、负载均衡管理、存储分层管理、云存储网关管理、数据中心网络管理等方面的QOS保障机制和原理进行了综合分析,归纳出各自的工作思想和优缺点,指出了云存储多数据中心管理面临的挑战,并设计了一个云存储资源优化仿真平台。这是开展云存储多数据中心QoS保障和资源调度研究工作的理论和实验基础。
(2)提出QoS全局最优的云存储多数据中心负载均衡调度模型,并提出一种基于商空间的层次式负载均衡调度算法(QBHLBSA)。本文分析了云存储负载均衡机制和云存储多数据中心在负载均衡方面存在的问题,提出了一种QoS全局最优的云存储多数据中心负载均衡调度模型。模型的优化目标是保证不同QoS级别业务的性能需求,并使各个数据中心资源利用率最大化。本文结合云存储多数据中心层次化管理特点,提出了一种基于商空间的层次式负载衡调度算法。该算法可以在不同粒度上由粗至细地对云存储负载进行调度,具有更快的收敛速度、避免了传统算法极易陷入局部最优值问题。仿真结果表明,本算法可以提升云存储的系统整体资源利用率和吞吐率,并且保障高QoS优先级业务的读写性能要求。
(3)提出面向应用层QoS保障的对象分层存储系统模型,并提出一种基于定价策略的自动分层调度算法(PBACST)。为了保障云存储中高QoS优先级业务对存储性能的要求,本文建立了面向应用层QOS保障的对象分层存储系统模型。模型的优化目标是在充分考虑不同业务QoS需求、存储容量和吞吐率约束条件下最大化云存储高速缓存资源池的利用率。本文结合云存储多数据中心特点,提出了一种基于定价策略的自动分层调度算法。该算法具有分布式决策的特点,各组件相互协作地完成数据对象分层调度。仿真结果表明,本算法可以提升云存储中高速缓存资源池的利用率,并且保障高QoS优先级业务的读写性能要求。
(4)提出支持QOS的云存储多数据中心任务调度模型,并提出一种基于动态带宽分配的实时任务调度算法(DBABRTSA)。本文分析了云存储中不同QoS级别业务竞争有限的带宽资源时产生的拥塞问题,提出了一种支持QoS的多数据中心任务调度模型。模型的优化目标是保证高QoS级别实时业务的性能需求,并提升云存储系统整体吞吐性能。本文结合云存储多数据中心层次化管理特点,提出了一种基于动态带宽分配的实时任务调度算法。该算法可以按照业务类型优先级从高到低,动态分配任务流量带宽。仿真结果表明,本算法可以保障高QoS级别实时业务的读写性能要求,同时保证其它各级QoS业务对带宽使用的比例公平性,并能提升系统整体的吞吐率。
(5)提出面向QOS的数据中心间网络流量调度模型,并提出一种基于双层多粒子群的网络流量调度算法(BLMSPSOSA)。本文分析了云存储多数据中心间网络链路带宽利用不均衡的问题,提出了一种面向QOS的多数据中心间网络流量调度模型。模型的优化目标是保证不同QoS级别数据传输的性能需求,并使数据中心间网络链路带宽资源利用率最大化。本文结合云存储多数据中心层次化管理特点,提出了一种基于双层多粒子群的网络流量调度算法。该算法具有更快的收敛速度、避免了传统算法极易陷入局部最优值问题。仿真结果表明,本算法可以提升云存储多数据中心间网络链路带宽的利用率,并且保障高QoS优先级数据的传输需求。
综上所述,本文通过对不同子服务的资源优化调度实现了云存储多数据中心QoS保障,并有效提升了云存储整体资源利用率和吞吐率。
Cloud storage is the fundamental component of cloud computing, its high reliability, high availability and high performance are the key guarantee to supporting all types of cloud services. Multi-datacenter cloud storage is built on WAN based distributed architecture, it ensure the reliability and availability of data service by multiple offsite copies in data centers around the world and ensure the performance of data by visiting the nearest nodes and downloading parallelly. Multi-datacenter cloud storage differs greatly from single-datacenter one in QoS guarantee and resource scheduling. How to guarantee different levels of service QoS and improve the system resource utilization is the key research area in cloud storage.
In this dissertation, in order to guarantee the multi-datacenter QoS and improve the overall resource utilization of cloud storage, we studied the technique of optimizing multi-datacenter cloud storage resource scheduling to achieve QoS guarantee. According to the characteristics of multi-datacenter cloud storage, we divided the cloud storage service into four major sub-services which are load balancing, storage tiering, storage gateway and network transmission, and achieved the dual goals of QoS guarantees and system resource optimization for different sub-services by using specific means. The main achievements and innovations of this dissertation are in the following:
(1) Analyzed the technical principles of multi-datacenter cloud storage resource management, concluded the research status and problems of QoS guarantees and resource scheduling of cloud storage in multi-datacenter architecture. We conducted a comprehensive analysis of cloud storage architecture and QoS guarantee mechanism and principles in load balancing, storage tiering, storage gateway and data center network, summarized the advantages and disadvantages of each method, pointed out the challenges of multi-datacenter cloud storage management and designed a resource optimization simulation platform. It is the theoretical and experimental basis for studing the multi-datacenter QoS guarantee and resource scheduling technique of cloud storage.
(2) Proposed a QoS global optimal cloud storage multi-datacenter load balancing scheduling model and proposed a quotient space-based hierarchical load balancing scheduling algorithm (QBHLBSA). We analyzed the load balancing mechanism of cloud storage and its problems in multi-datacenter architecture, proposed a QoS global optimal cloud storage multi-datacenter load balancing scheduling model. The optimization objective is to guarantee the performance requirements of different QoS level applications and maximizing the resource utilization of each data center. According to the hierarchical management characteristics of multi-datacenter cloud storage, we proposed a quotient space-based hierarchical load balancing scheduling algorithm. The algorithm can schedule the storage load from coarse to fine granularity, with faster convergence time and avoid falling into local optimal values easily by using traditional algorithms. The simulation results showed that the algorithm can improve resource utilization and overall system throughput of cloud storage, guarantee the read and write performance requirements of high QoS level applications.
(3) Proposed an object tiering storage system model for application layer QoS guarantee and proposed a pricing based automated cloud storage tiering algorithm (PBACST). In order to protect the performance requirements of high QoS level applications in cloud storage, we build an application layer QoS guarantee oriented object tiering storage system model. The optimization objective of model is maximizing the utilization of cloud storage high speed cache resource pool under the constraints of different applications' QoS requirements, storage capacity and throughput. Based on the characteristics of multi-datacenter cloud storage, we proposed a pricing based automated tiering scheduling algorithm. The algorithm can make decision distributively, the components make the object tiering scheduling cooperatively. The simulation results showed that the algorithm can improve the utilization of high speed cache resource pool and guarantee the read and write performance requirements of high QoS level applications.
(4) Proposed a QoS guaranteed cloud storage multi-datacenter task scheduling model and proposed a dynamic bandwidth allocation based real-time task scheduling algorithm (DBABRTSA). We analyzed the congestion problem when different QoS level application competing with limited bandwidth resources, proposed a QoS guaranteed multi-datacenter task scheduling model. The optimization objective of model is guaranteeing the performence requirements of high QoS level real-time application and improving the overall thoughtput of cloud storage. According to the hierarchical management characteristics of multi-datacenter cloud storage, we proposed a dynamic bandwidth allocation based real-time task scheduling algorithm. The algorithm can dynamically allocate task bandwidth according to application priorities. The simulation results showed that the algorithm can guarantee the read and write performance requirements of high QoS level applications and the proportional usage fairness of other QoS level applications, meanwhile improve the overall system thoughtput.
(5) Proposed a QoS guaranteed inter-datacenter network traffic scheduling model and proposed a bi-level multi-swarm PSO based network traffic scheduling algorithm (BLMSPSOSA). We analyzed the uneven utilization problem of network link bandwidth in multi-datacenter cloud storage, proposed a QoS guaranteed inter-datacenter network traffic scheduling model. The optimization objective of model is guaranteeing the transmission performance of different QoS level data and maximizing the resource utilization of inter-datacenter network. According to the hierarchical management characteristics of multi-datacenter cloud storage, we proposed a bi-level multi-swarm PSO based network traffic scheduling algorithm. The algorithm has faster convergence time and can avoid falling into local optimal values easily by using traditional algorithms. The simulation results showed that the algorithm can improve the resource utilization of inter-datacenter network and guarantee the transmission requirements of high QoS level data.
In conclusion, by optimizing the resource scheduling of different sub-services, we achieved the multi-datacenter QoS guarantee and effectively improved the overall resource utilization and throughput of cloud storage.
引文
[1]Hengxi Z, Chunlin L, Zhengjun S, et al. Resource pool-oriented resource management for cloud computing[C].2011 International Conference on Business, Economics, and Financial Sciences, Management, BEFM 2011, December 30,2011-December 31,2011. Jeju Island, Korea, Republic of: Springer Verlag,2012:829-832.
[2]Khazaei H, Miic J, Miic V B, et al. Analysis of a pool management scheme for cloud computing centers[J]. IEEE Transactions on Parallel and Distributed Systems,2013,24(5):849-861.
[3]Amanatullah Y, Lim C, Ipung H P, et al. Toward cloud computing reference architecture:Cloud service management perspective[C].2013 International Conference on ICT for Smart Society 2013: "Think Ecosystem Act Convergence", ICISS 2013, June 13,2013-June 14,2013. Jakarta, Indonesia: IEEE Computer Society,2013:34-37.
[4]Divya K, Jeyalatha S. Key technologies in cloud computing[C].2012 International Conference on Cloud Computing Technologies, Applications and Management, ICCCTAM 2012, December 8, 2012-December 10,2012. Dubai, United arab emirates:IEEE Computer Society,2012:196-199.
[5]Sun H, Tu Q W, Wang X W, et al. The pricing and charging of cloud computing SaaS[C].2013 International Forum on Materials Science and Industrial Technology, IFMSIT 2013, August 30,2013-September 1,2013. Qingdao, China:Trans Tech Publications Ltd,2013:703-707.
[6]Sakr S. Cloud-hosted databases:technologies, challenges and opportunities[J]. Cluster Computing,2013,:1-16.
[7]Sugiki A, Kato K. Elements and composition of software-defined data centers[C]. Posters and Demo Track, Middleware 2012, December 3,2012-December 3,2012. Montreal, QC, Canada: Association for Computing Machinery,2012:Professional; IFIP; USENIX.
[8]Zhao Y, Ou K, Zeng W, et al. Research on cloud storage architecture and key technologies[C]. 2nd International Conference on Interaction Sciences:Information Technology, Culture and Human, ICIS 2009, November 24,2009-November 26,2009. Seoul, Korea, Republic of:Association for Computing Machinery,2009:1044-1048.
[9]Abadi D J. Data Management in the Cloud:Limitations and Opportunities[J]. IEEE Data Eng. Bull.,2009,32(1):3-12.
[10]Wu J, Ping L, Ge X, et al. Cloud Storage as the Infrastructure of Cloud Computing[C].2010: 380-383.
[11]Patterson S, Elmore A J, Nawab F, et al. Serializability, not serial:concurrency control and availability in multi-datacenter datastores[J]. Proc. VLDB Endow.,2012,5(11):1459-1470.
[12]Lin C, Wang Y, Ren F. Research on QoS in next generation network[J]. Jisuanji Xuebao/Chinese Journal of Computers,2008,31(9):1525-1535.
[13]Fujikawa K, Ohta M. Integration and simplification of QoS guarantee and multicasting for a New-Generation Network[C].9th International Conference on Optical Internet, COIN 2010, July 11, 2010-July 14,2010. Jeju, Korea, Republic of:IEEE Computer Society,2010.
[14]Wu J C, Brandt S A. Providing quality of service support in object-based file system[C].24th IEEE Conference on Mass Storage Systems and Technologies, MSST 2007, September 24,2007-September 27,2007. San Diego, CA, United states:Inst. of Elec. and Elec. Eng. Computer Society, 2007:157-168.
[15]Wang J, Varmany P, Xie C. Avoiding performance fluctuation in cloud storage[C].17th International Conference on High Performance Computing, HiPC 2010, December 19,2010 December 22,2010. Goa, India:IEEE Computer Society,2010.
[16]Mesnier M, Chen F, Luo T, et al. Differentiated storage services[C].23rd ACM Symposium on Operating Systems Principles, SOSP 2011, October 23,2011-October 26,2011. Cascais, Portugal: Association for Computing Machinery,2011:57-70.
[17]Ju J, Wu J, Fu J, et al. A survey on cloud storage[J]. Journal of Computers,2011,6(8): 1764-1771.
[18]Sota R. Storage QoS provisioning for execution programming of data-intensive applications[C]. Biological Knowledge Discovery and Data Mining. Nieuwe Hemweg 6B, Amsterdam,1013 BG, Netherlands:IOS Press,2012:69-80.
[19]Fu H. A Multi-Objective Optimization Algorithm for the QoS of Cloud Storage[J]. Fuzzy Systems, Knowledge Discovery and Natural Computation Symposium.2013,:490-498.
[20]Zhang Y. Ren S Q, Chen S B, et al. DifferCloudStor:Differentiated quality of service for cloud storage[J]. IEEE Transactions on Magnetics,2013,49(6):2451-2458.
[21]Ryu M, Ramachandran U. FlashStream:A multi-tiered storage architecture for adaptive HTTP streaming[C].21st ACM International Conference on Multimedia. MM 2013, October 21.2013-October 25,2013. Barcelona, Spain:Association for Computing Machinery,2013:313-322.
[22]Hernandez-Ramirez E M, Sosa-Sosa V J, Lopez-Arevalo I. A Comparison of Redundancy Techniques for Private and Hybrid Cloud Storage[J]. Journal of Applied Research and Technology, 2012,10:893-901.
[23]Wang Y, Sun W, Zhou S, et al. Key technologies of distributed storage for cloud computing[J]. Ruan Jian Xue Bao/Journal of Software,2012,23(4):962-986.
[24]Lim H C, Babu S, Chase J S. Automated control for elastic storage[C].7th IEEE/ACM International Conference on Autonomic Computing and Communications, ICAC-2010 and Co-located Workshops, June 7,2010-June 11,2010. Washington, DC, United states:Association for Computing Machinery,2010:1-10.
[25]Vo H T, Chen C, Ooi B C. Towards elastic transactional cloud storage with range query support[J]. Proceedings of the VLDB Endowment,2010,3(1):506-517.
[26]Wei Q, Veeravalli B, Gong B, et al. CDRM:A cost-effective dynamic replication management scheme for cloud storage cluster[C]. Proceedings-2010 IEEE International Conference on Cluster Computing, Cluster 2010.3 Park Avenue,17th Floor, New York, NY 10016-5997, United States: Institute of Electrical and Electronics Engineers Inc.,2010:188-196.
[27]Chihoub H, Ibrahim S, Antoniu G, et al. Harmony:Towards automated self-adaptive consistency in cloud storage[C].2012 IEEE International Conference on Cluster Computing, CLUSTER 2012, September 24,2012-September 28,2012. Beijing, China:IEEE Computer Society,2012:293-301.
[28]Sun D, Chang G, Gao S, et al. Modeling a dynamic data replication strategy to increase system availability in cloud computing environments[J]. Journal of Computer Science and Technology,2012, 27(2):256-272.
[29]Abad C L, Lu Y, Campbell R H. DARE:Adaptive data replication for efficient cluster scheduling[C].2011 IEEE International Conference on Cluster Computing, CLUSTER 2011, September 26,2011-September 30,2011. Austin, TX, United states:Institute of Electrical and Electronics Engineers Inc.,2011:159-168.
[30]Janpet J, Wen Y. Reliable and available data replication planning for cloud storage[C].27th IEEE International Conference on Advanced Information Networking and Applications, AINA 2013, March 25,2013-March 28,2013. Barcelona, Spain:Institute of Electrical and Electronics Engineers Inc.,2013:772-779.
[31]Silvestre G, Monnet S, Krishnaswamy R, et al. AREN:A popularity aware replication scheme for cloud storage[C].18th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2012, December 17,2012-December 19,2012. Singapore, Singapore:IEEE Computer Society,2012:189-196.
[32]Das S, Nishimura S, Agrawal D, et al. Albatross:Lightweight elasticity in shared storage databases for the cloud using live data migration[J]. Proceedings of the VLDB Endowment,2011,4(8): 494-505.
[33]Du H, Li Z. Data rearrange based on mining block access sequence in cloud storage[C].2011 International Conference on Computer Science and Network Technology, ICCSNT 2011, December 24,2011-December 26,2011. Harbin, China:IEEE Computer Society,2011:2507-2511.
[34]Lu Y, Zhang J, Wu S, et al. A hybrid dynamic load balancing approach for cloud storage[C]. 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, August 23,2012-August 25,2012. Xi'an, China:IEEE Computer Society,2012:1332-1335.
[35]Papaioannou T G, Bonvin N, Aberer K. Scalia:An adaptive scheme for efficient multi-cloud storage[C].201224th International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012, November 10,2012-November 16,2012. Salt Lake City, UT, United states:IEEE Computer Society,2012:Association for Computing Machinery (ACM); IEEE Computer Society.
[36]Chen L, Triantafillou P, Suel T, et al. Providing Scalable Database Services on the Cloud:6488 ed. Springer Berlin Heidelberg,2010:6488,1-19.
[37]Lu L, Hildebrand D, Tewari R. Zone-based data striping for cloud storage[J]. IBM Journal of Research and Development,2011,55(16).
[38]Cranor C, Polte M, Gibson G. PLFS/HDFS:HPC Applications on Cloud Storage[J]. High Performance Computing, Networking, Storage and Analysis,2012,:1410.
[39]Mat Deris M, Abawajy J H, Mamat A. An efficient replicated data access approach for large-scale distributed systems[J]. Future Generation Computer Systems,2008,24(1):1-9.
[40]Cheng K, Wang H, Wen C, et al. Dynamic file replica location and selection strategy in Data Grids[C].2008 the 1st IEEE International Conference on Ubi-Media Computing and Workshops, U-Media2008, July 31,2008-August 1,2008. Lanzhou University, China:Inst. of Elec. and Elec. Eng. Computer Society,2008:484-489.
[41]Al-Mistarihi H H E. Yong C H. On fairness, optimizing replica selection in data grids[J]. IEEE Transactions on Parallel and Distributed Systems,2009,20(8):1102-1111.
[42]Qu M, Wu X, Liao M, et al. A novel resource selection model for data grid based on QoS[C]. 2011 International Conference on Computer-Aided Material and Engineering, ICCME 2011, March 9, 2011-March 11,2011. Hangzhou, China:Trans Tech Publications,2011:203-209.
[43]Tang B, Zhang L. Optimal replica selection algorithm in data grid[C].20112nd International Conference on Theoretical and Mathematical Foundations of Computer Science, ICTMF 2011, May 5, 2011-May 6,2011. Singapore, Singapore:Springer Verlag,2011:297-304.
[44]Rahman R M, Barker K, Alhajj R. A predictive technique for replica selection in grid environment[C].7th IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2007, May 14,2007-May 17,2007. Rio de Janeiro, Brazil:Inst. of Elec. and Elec. Eng. Computer Society, 2007:163-170.
[45]Naseera S, Madhu Murthy K V. Performance evaluation of predictive replica selection using neural network approaches[C].2009 International Conference on Intelligent Agent and Multi-Agent Systems, IAMA 2009, July 22,2009-July 24,2009. Chennai, India:IEEE Computer Society,2009.
[46]Rahman R M, Alhajj R, Barker K. Replica selection strategies in data grid[J]. Journal of Parallel and Distributed Computing,2008,68(12):1561-1574.
[47]Almuttairi R M, Wankar R, Negi A, et al. Replica selection in data grids using preconditioning of decision attributes by K-means clustering (K-RSDG)[C].2nd Vaagdevi International Conference on Information Technology for Real World Problems, VCON 2010, December 9,2010-December 11, 2010. Warangal, India:IEEE Computer Society,2010:18-23.
[48]Mao Y, Ling J. Research on load balance strategy based on grey prediction theory in Cloud Storage[C].20122nd International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2012, September 26,2012-September 28,2012. Shenyang, Liaoning, China:Atlantis Press,2012:199-203.
[49]Wang C, Xue G, Li H, et al. Research on Datacopy Selection Strategy in Cloud Storage[J]. Third International Conference on Computer Engineering and Technology,2011,:349-354.
[50]Li J. A replica selection approach based on prediction in data grid[C].3rd International Conference on Semantics, Knowledge, and Grid, SKG 2007, October 29,2007-October 31,2007. Xi'an, China:Inst. of Elec. and Elec. Eng. Computer Society,2007:274-277.
[51]Wu C, Wu K, Chen M, et al. Dynamic replica selection services based on state evaluation strategy[C].4th ChinaGrid Annual Conference, ChinaGrid 2009, August 21,2009-August 22,2009. Yantai, China:IEEE Computer Society,2009:116-119.
[52]Mendez Munoz V, Amoros Vicente G, Garcia Carballeira F, et al. Emergent algorithms for replica location and selection in data grid[C]. P.O. Box 211, Amsterdam,1000 AE, Netherlands: Elsevier,2010:934-946.
[53]Xiong R, Luo J, Song A, et al. QoS preference-aware replica selection strategy using mapreduce-based PGA in Data Grids[C].40th International Conference on Parallel Processing, ICPP 2011, September 13,2011-September 16,2011. Taipei City, Taiwan:Institute of Electrical and Electronics Engineers Inc.,2011:394-403.
[54]Zhou L, Wang Y, Zhang J, et al. Optimize block-level cloud storage system with load-balance strategy[C].2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012, May 21,2012-May 25,2012. Shanghai, China:IEEE Computer Society,2012: 2162-2167.
[55]Wan J, Zhang J, Zhou L, et al. ORTHRUS:A lightweighted block-level cloud storage system[J]. Cluster Computing,2013,16(4):625-638.
[56]Harnik D, Pinkas B, Shulman-Peleg A. Side channels in cloud services:Deduplication in cloud storage[J]. IEEE Security and Privacy,2010,8(6):40-47.
[57]Wu T Y, Pan J S, Lin C F. Improving Accessing Efficiency of Cloud Storage Using De-Duplication and Feedback Schemes[J]. Systems Journal, IEEE,2014,8(1):208-218.
[58]Wu T, Lee W, Lin C F. Cloud storage performance enhancement by real-time feedback control and de-duplication[C].11th Annual Wireless Telecommunications Symposium, WTS 2012, April 18, 2012-April 20,2012. London, United kingdom:IEEE Computer Society,2012.
[59]Pamies-Juarez L, Garcia-Lopez P, Sanchez-Artigas M, et al. Towards the design of optimal data redundancy schemes for heterogeneous cloud storage infrastructures[J]. Computer Networks,2011. 55(5):1100-1113.
[60]Li Y, Guo L, Guo Y. An Efficient and Performance-Aware Big Data Storage System:Cloud Computing and Services Science. Ivanov I, Sinderen M, Leymann F, et al. Springer International Publishing,2013:367,102-116.
[61]Zhang L, Zhu L G, Zeng S F. Tiered adaptive large-scale storage system with high performance[C].2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT2013, August 17,2013-August 18,2013. Zhengzhou, Henan, China: Trans Tech Publications Ltd,2013:2371-2374.
[62]Youn C. A case study for the application of storage tiering based on ILM through data value analysis[C]. International Conference on IT Convergence and Security, ICITCS 2012, December 5, 2012-December 7,2012. Pyeong Chang, Korea, Republic of:Springer Verlag,2013:1037-1053.
[63]Spillner J, Muller J, Schill A. Creating optimal cloud storage systems[J].2012.
[64]Li Y, Guo L, Guo Y. CACSS:Towards a generic cloud storage service[C].2nd International Conference on Cloud Computing and Services Science, CLOSER 2012, April 18,2012-April 21, 2012. Porto, Portugal:Curran Associates Inc.,2012:27-36.
[65]Abu-Libdeh H, Princehouse L, Weatherspoon H. RACS:A case for cloud storage diversity[C]. 1st ACM Symposium on Cloud Computing, SoCC'10, June 6,2010-June 11,2010. Indianapolis, IN, United states:Association for Computing Machinery,2010:229-239.
[66]Cao L, Huang L, Lei K, et al. Hybrid caching for cloud storage to support traditional application[C].1st IEEE Asia Pacific Cloud Computing Congress, APCloudCC 2012, November 14, 2012-November 17,2012. Shenzhen, China:IEEE Computer Society,2012:11-15.
[67]Hu H, di Zhang, Zhou Y. A Design of Cloud Storage Gateway Based on Data Dispersal:2013: 30,605-608.
[68]Dejiao N, Tao C, Yongzhao Z, et al. Metadata caching subsystem for cloud storage[C].2nd International Conference on Green Power, Materials and Manufacturing Technology and Applications, GPMMTA 2012, July 17,2012-July 19,2012. Kunming, China:Trans Tech Publications,2012: 584-590.
[69]Tanimura Y, Yanagita S. Extension of S3 REST API for Providing QoS Support in Cloud Storage[C].11th USENIX Conference on File and Storage Technologies.2013.
[70]Blair A, Parr G, Morrow P, et al. Realtime utilisation of cloud storage[C].20121st IEEE International Conference on Cloud Networking, CLOUDNET 2012, November 28,2012-November 30,2012. Paris, France:IEEE Computer Society,2012:188-190.
[71]Greenberg A, Hamilton J, Maltz D A, et al. The cost of a cloud:research problems in data center networks[J]. SIGCOMM Comput. Commun. Rev.,2008,39(1):68-73.
[72]Jain S, Kumar A, Mandal S, et al. B4:experience with a globally-deployed software defined wan[J]. SIGCOMM Comput. Commun. Rev.,2013,43(4):3-14.
[73]Grossman R L, Gu Y, Sabala M, et al. Compute and storage clouds using wide area high performance networks[J]. Future Generation Computer Systems,2009,25(2):179-183.
[74]Gu Y, Grossman R L. Sector:A high performance wide area community data storage and sharing system[J]. Future Generation Computer Systems,2010,26(5):720-728.
[75]Wang D. An efficient cloud storage model for heterogeneous cloud infrastructures[C].2011 International Conference on Power Electronics and Engineering Application, PEEA 2011, December 24,2011-December 25,2011. Shenzhen, China:Elsevier Ltd,2011:510-515.
[76]Sakr S, Liu A, Batista D M, et al. A Survey of Large Scale Data Management Approaches in Cloud Environments[J]. Communications Surveys Tutorials, IEEE,2011,13(3):311-336.
[77]Ruiz-Alvarez A, Humphrey M. An automated approach to cloud storage service selection[C]. 2nd International Workshop on Scientific Cloud Computing, ScienceCloud'l 1, Co-located with 20th International ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2011, June 8,2011-June 8,2011. San Jose, CA, United states:Association for Computing Machinery, 2011:39-48.
[78]Palankar M, Lamnitchi A, Ripeanu M, et al. Amazon S3 for science grids:A viable solution?[C]. 2008 International Workshop on Data-aware Distributed Computing 2008, DADC'08, June 24,2008-June 24,2008. Boston, MA, United states:Association for Computing Machinery,2008:55-64.
[79]Dean J. Evolution and future directions of large-scale storage and computation systems at Google[C]. Proceedings of the 1st ACM symposium on Cloud computing. Indianapolis, Indiana, USA: ACM,2010:1.
[80]Calder B, Wang J, Ogus A, et al. Windows azure storage:A highly available cloud storage service with strong consistency[C].23rd ACM Symposium on Operating Systems Principles. SOSP 2011, October 23,2011-October 26,2011. Cascais, Portugal:Association for Computing Machinery, 2011:143-157.
[81]Huo Y, Wang H, Hu L, et al. A cloud storage architecture model for data-intensive applications[C].2011 International Conference on Computer and Management, CAMAN 2011, May 19,2011-May 21,2011. Wuhan, China:IEEE Computer Society.2011:IEEE Wuhan Section; Hunan University; Wuhan University; Engineering Information Institute; Chongqing VIP Information Co., Ltd.
[82]Gu G, Li Q. Wen X, et al. An overview of newly open-source cloud storage platforms[C].2012 IEEE International Conference on Granular Computing, GrC 2012, August 11,2012-August 13,2012. HangZhou, China:IEEE Computer Society,2012:142-147.
[83]Soares T S, Dantas M A R, De Macedo D D J, et al. A data management in a private cloud storage environment utilizing high performance distributed file systems[C].2013 IEEE 22nd International Workshop on Enabling Technologies:Infrastructure for Collaborative Enterprises, WETICE 2013, June 17,2013-June 20,2013. Hammamet, Tunisia:IEEE Computer Society,2013: 158-163.
[84]Ghemawat S, Gobioff H, Leung S. The google file system[C]. SOSP'03:Proceedings of the 19th ACM Symposium on Operating Systems Principles, October 19,2003-October 22,2003. Lake George, NY, United states:Association for Computing Machinery,2003:29-43.
[85]Ford D, Labelle F C C O, Popovici F I, et al. Availability in Globally Distributed Storage Systems[C]. OSDI'10. Berkeley, CA, USA:USENIX Association,2010:1-7.
[86]Jones T, Koniges A, Yates R K. Performance of the IBM general parallel file system[J]. Proceedings of the International Parallel Processing Symposium, IPPS,2000,:673-681.
[87]Zhao T, March V, Dong S, et al. Evaluation of a performance model of lustre file system[C].5th Annual ChinaGrid Conference, ChinaGrid 2010, July 16,2010-July 18,2010. Guangzhou, Guangdong, China:IEEE Computer Society,2010:191-196.
[88]Weil S A, Brandt S A, Miller E L, et al. Ceph:a scalable, high-performance distributed file system[C]. Proceedings of the 7th symposium on Operating systems design and implementation. Seattle, Washington:USENIX Association,2006:307-320.
[89]Shvachko K, Kuang H, Radia S, et al. The Hadoop distributed file system[C].2010 IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST2010, May 6,2010-May 7,2010. Lake Tahoe, NV, United states:IEEE Computer Society,2010:IEEE.
[90]Dean J, Ghemawat S. MapReduce:Simplified data processing on large clusters[J]. Communications of the ACM,2008,51(1):107-113.
[91]Chang F, Dean J, Ghemawat S, et al. Bigtable:A distributed storage system for structured data[J]. ACM Transactions on Computer Systems,2008,26(2).
[92]Azzedin F. Towards a scalable HDFS architecture[C].2013 International Conference on Collaboration Technologies and Systems, CTS 2013, May 20,2013-May 24,2013. San Diego, CA, United states:IEEE Computer Society,2013:155-161.
[93]Kala Karun A, Chitharanjan K. A review on hadoop-HDFS infrastructure extensions[C].2013 IEEE Conference on Information and Communication Technologies, ICT 2013, April 11,2013-April 12,2013. Thuckalay, Tamil Nadu, India:IEEE Computer Society,2013:132-137.
[94]Dong B, Zheng Q, Tian F, et al. An optimized approach for storing and accessing small files on cloud storage[J]. Journal of Network and Computer Applications,2012,35(6):1847-1862.
[95]Xiao L, Wu C, Wei Q. QoS optimization in object storage system[C]. Photonics and Optoelectronics Meetings, POEM 2009-Optical Storage and New Storage Technologies, August 8, 2009-August 10,2009. Wuhan, China:SPIE,2009:Chinese Optical Society; Huazhong University of Science and Technology; China Hubei Provincial Science Technology Department; Adm. Comm. Wuhan East Lake High-Tech Dev. Zone.
[96]Brim M J, Dillow D A, Oral S, et al. Asynchronous object storage with QoS for scientific and commercial big data[C].8th Parallel Data Storage Workshop, PDSW 2013-Held in Conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013, November 17,2013-November 22,2013. Denver, CO, United states:Association for Computing Machinery,2013:7-13.
[97]Gupta P, Pegah M. A new thought paradigm:Delivering cost effective and ubiquitously accessible storage with enterprise backup system via a multi-tiered storage framework[C]. SIGUCCS'07:2007 Fall Conference on User Services-Inspiring Magical Outcomes, October 7,2007-October 10,2007. United states:Association for Computing Machinery,2007:146-152.
[98]Greenberg A, Hamilton J R, Jain N, et al. VL2:A scalable and flexible data center network[J]. Communications of the ACM,2011.54(3):95-104.
[99]Seddiki M S, Frikha M. A non-cooperative game theory model for bandwidth allocation in network virtualization[C].201215th International Telecommunications Network Strategy and Planning Symposium, NETWORKS 2012, October 15,2012-October 18,2012. Rome, Italy:IEEE Computer Society,2012.
[100]Casanova H, Legrand A, Quinson M. SimGrid:A generic framework for large-scale distributed experiments[C]. UKSim 10th International Conference on Computer Modelling and Simulation, EUROSIM/UKSim2008, April 1,2008-April 3.2008. Cambridge. United kingdom:Inst. of Elec. and Elec. Eng. Computer Society,2008:126-131.
[101]Quinson M. SimGrid:A generic framework for large-scale distributed experiments[C]. IEEE P2P'09-9th International Conference on Peer-to-Peer Computing, September 9,2009-September 11, 2009. Seattle, WA, United states:IEEE Computer Society,2009:95-96.
[102]Casanova H. Simgrid:A toolkit for the simulation of application scheduling[C].1st IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2001, May 15,2001-May 18, 2001. Brisbane, QLD, Australia:IEEE Computer Society,2001:430-437.
[103]Legrand A, Marchal L, Casanova H. Scheduling distributed applications:The SimGrid simulation framework[C].3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2003, May 12,2003-May 15,2003. Tokyo, Japan:IEEE Computer Society,2003: 138-145.
[104]Bell W H, Cameron D G, Capozza L, et al. Simulation of dynamic grid replication strategies in OptorSim[C].3rd International Workshop on Grid Computing, GRID 2002, November 18,2002-November 18,2002. Baltimore, MD, United states:Springer Verlag,2002:46-57.
[105]Calheiros R N, Ranjan R, Beloglazov A, et al. CloudSim:A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software-Practice and Experience,2011,41(1):23-50.
[106]Long S, Zhao Y. A toolkit for modeling and simulating cloud data storage:an extension to CloudSim[J].2012 International Conference on Control Engineering and Communication Technology, 2012,:597-600.
[107]Baumgart I, Heep B, Krause S. OverSim:A flexible overlay network simulation framework[C]. 2007 IEEE Global Internet Symposium, GI, May 11,2007-May 11,2007. Anchorage, AK, United states:Inst. of Elec. and Elec. Eng. Computer Society,2007:79-84.
[108]Kazmi I, Bukhari S F Y. PeerSim:An efficient scalable testbed for heterogeneous cluster-based P2P network protocols[C].2011 UKSim 13th International Conference on Modelling and Simulation, UKSim 2011, March 30,2011-April 1,2011. Cambridge, United kingdom:IEEE Computer Society, 2011:420-425.
[109]Imbert M, Caron E. Dynamic network forecasting using simgrid simulations[C].2012 IEEE International Conference on Cluster Computing, CLUSTER 2012, September 24,2012-September 28, 2012. Beijing, China:IEEE Computer Society,2012:542-545.
[110]Liu S, Huang X, Fu H, et al. Understanding data characteristics and access patterns in a cloud storage system[C].13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, May 13,2013-May 16,2013. Delft, Netherlands:IEEE Computer Society,2013: 327-334.
[111]Thangaraj R, Pant M, Abraham A, et al. Modified Particle Swarm Optimization with time varying velocity vector[J]. International Journal of Innovative Computing, Information and Control, 2012,8(1A):201-218.
[112]Liu H, Abraham A, Snael V. Convergence analysis of swarm algorithm[C].2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009, December 9,2009 December 11,2009. Coimbatore, India:IEEE Computer Society,2009:1714-1719.
[113]Chakraborty P, Das S, Roy G G, et al. On convergence of the multi-objective particle swarm optimizers[J]. Information Sciences,2011,181(8):1411-1425.
[114]Wang X, Ding S. An overview of quotient space theory[C].2011 International Conference on Sport Material, Modelling and Simulation, ICSMMS 2011, January 27,2011-January 28,2011. Shenzhen, China:Trans Tech Publications,2011:326-331.
[115]Zhao L, Zhang L. Advances in the quotient space theory and its applications[C].701 E. Chocolate Ave., Suite 200, Hershey, PA 17033-1240, United States:IGI Publishing,2009:39-50.
[116]Gao S, Cao C. Convergence analysis of particle swarm optimization algorithm[J]. Advances in Information Sciences and Service Sciences,2012,4(14):25-32.
[117]Mungal A G. Multi-core computing and the tiered storage model[C].35th International Conference on Computer Measurement Group, December 6,2009-December 11,2009. Dallas, TX, United states:Computer Measurement Group Inc,2009.
[118]Zeng L, Feng D, Wang F, et al. Object replication and migration policy based on OSS[C]. International Conference on Machine Learning and Cybernetics, ICMLC 2005. August 18,2005 August 21,2005. Guangzhou, China:Institute of Electrical and Electronics Engineers Computer Society,2005:45-49.
[119]Shan Y, Yao N, Yu J. A hierarchical cache scheme for object storage system[C].2009 International Conference on New Trends in Information and Service Science, NISS 2009, June 30, 2009-July 2,2009. Beijing, China:IEEE Computer Society,2009:20-22.
[120]Luo Y, Deng W. An adaptive data placement scheme for scaleable object storage system[C].8th International Symposium on Optical Storage and 2008 International Workshop on Information Data Storage, November 24,2008-November 27,2008. Wuhan, China:SPIE,2009:Shanghai Institute of Optics and Fine Mechanics, CAS.
[121]Huang P, Peng H, Lin P, et al. Macroeconomics based Grid resource allocation[J]. Future Generation Computer Systems,2008,24(7):694-700.
[122]Lerner V. Macroeconomic analysis of the information dynamic model[J]. Cybernetics and Systems,1993,24(6):591-633.
[123]Kapetanios G, Pagan A, Scott A. Making a match:Combining theory and evidence in policy-oriented macroeconomic modeling[J]. Journal of Econometrics,2007,136(2):565-594.
[124]Wang F, Krunz M, Cui S. Price-based spectrum management in cognitive radio networks[J]. IEEE Journal on Selected Topics in Signal Processing,2008,2(1):74-87.
[125]Wang H, Tian Z. Intelligent price-based congestion control for communication networks[C]. 2010 IEEE 18th International Workshop on Quality of Service, IWQoS 2010, June 16,2010-June 18, 2010. Beijing, China:Institute of Electrical and Electronics Engineers Inc.,2010.
[126]Yuan P, Liang Y, Bi G. Price-based distributed resource allocation for femtocell networks[C]. 2012 IEEE International Conference on Communication Systems, ICCS 2012, November 21,2012-November 23,2012. Singapore, Singapore:IEEE Computer Society,2012:418-422.
[127]Yamanaka N, Sato Y, Sato K. Performance limitation of the leaky bucket algorithm for ATM networks[J]. IEEE Transactions on Communications,1995,43(8):2298-2300.
[128]Solomon B, Ionescu D, Gadea C, et al. Leaky bucket model for autonomic control of distributed, collaborative systems[C].8th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2013, May 23,2013-May 25,2013. Timisoara, Romania:IEEE Computer Society,2013:483-488.
[129]Khan A J, Sahoo A, Manjunath D. Implementation of WFQ in a distributed open software router[C].36th Annual IEEE Conference on Local Computer Networks, LCN 2011, October 4,2011-October 7,2011. Bonn, Germany:IEEE Computer Society,2011:519-527.
[130]Taniguchi S, Kawate R, Sato K, et al. Performance evaluation of the simplified WFQ to multiplex a huge number of queues[C].2012 IEEE International Workshop Technical Committee on Communications Quality and Reliability, CQR 2012, May 15,2012-May 17,2012. San Diego, CA, United states:IEEE Computer Society,2012.
[131]Kim H, Hou J C. Enabling network calculus-based simulation for TCP congestion control[J]. Computer Networks,2009,53(1):1-24.
[132]Angrishi K, Killat U. An approach using node operating point for performance analysis with network calculus[C].2011 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2011, June 27,2011-June 30,2011. The Hague, Netherlands: IEEE Computer Society,2011:30-37.
[133]Li Q, Chen Z, Zhang L, et al. Deterministic upper bounds on QoS performance about wireless ad hoc network based on network calculus[J]. Tongxin Xuebao/Journal on Communications,2008,29(9): 32-39.
[134]Zhang L, Liu J, Yang K. Quality of Service Modelling of Virtualized Wireless Networks:A Network Calculus Approach[J].2014.
[135]Lv B, Wang Z, Huang T, et al. A hierarchical virtual resource management architecture for network virtualization[C].20106th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2010, September 23,2010-September 25,2010. Chengdu, China:IEEE Computer Society,2010:IEEE Antennas and Propagation Society; IEEE Communications Society; Southwest Jiaotong University; University of Electronic Science and Technology of China; Wuhan University.
[136]Kang M, Kang E, Hwang D, et al. Formal modeling and verification of SDN-OpenFlow[C]. IEEE 6th International Conference on Software Testing, Verification and Validation, ICST 2013, May 18,2013-May 20,2013. Luxembourg, Luxembourg:IEEE Computer Society,2013:481-482.
[137]Shin M, Nam K, Kim H. Software-defined networking (SDN):A reference architecture and open APIs[C].2012 International Conference on 1CT Convergence:"Global Open Innovation Summit for Smart ICT Convergence", ICTC 2012, October 15,2012-October 17,2012. Jeju Island, Korea, Republic of:IEEE Computer Society,2012:360-361.
[138]Azodolmolky S, Wieder P, Yahyapour R. SDN-based cloud computing networking[C].2013 15th International Conference on Transparent Optical Networks,ICTON 2013, June 23,2013-June 27,2013. Cartagena, Spain:IEEE Computer Society,2013.
[139]Sharma P, Banerjee S, Tandel S, et al. Enhancing network management frameworks with SDN-like control[C].2013IFIP/IEEE International Symposium on Integrated Network Management, IM 2013, May 27,2013-May 31,2013. Ghent. Belgium:IEEE Computer Society,2013:688-691.
[140]Bueno I, Aznar J I, Escalona E, et al. An OpenNaaS based SDN framework for dynamic QoS control[C].2013 Workshop on Software Defined Networks for Future Networks and Services, SDN4FNS 2013, November 11,2013-November 13,2013. Trento, Italy:IEEE Computer Society, 2013.
[2]Khazaei H, Miic J, Miic V B, et al. Analysis of a pool management scheme for cloud computing centers[J]. IEEE Transactions on Parallel and Distributed Systems,2013,24(5):849-861.
[3]Amanatullah Y, Lim C, Ipung H P, et al. Toward cloud computing reference architecture:Cloud service management perspective[C].2013 International Conference on ICT for Smart Society 2013: "Think Ecosystem Act Convergence", ICISS 2013, June 13,2013-June 14,2013. Jakarta, Indonesia: IEEE Computer Society,2013:34-37.
[4]Divya K, Jeyalatha S. Key technologies in cloud computing[C].2012 International Conference on Cloud Computing Technologies, Applications and Management, ICCCTAM 2012, December 8, 2012-December 10,2012. Dubai, United arab emirates:IEEE Computer Society,2012:196-199.
[5]Sun H, Tu Q W, Wang X W, et al. The pricing and charging of cloud computing SaaS[C].2013 International Forum on Materials Science and Industrial Technology, IFMSIT 2013, August 30,2013-September 1,2013. Qingdao, China:Trans Tech Publications Ltd,2013:703-707.
[6]Sakr S. Cloud-hosted databases:technologies, challenges and opportunities[J]. Cluster Computing,2013,:1-16.
[7]Sugiki A, Kato K. Elements and composition of software-defined data centers[C]. Posters and Demo Track, Middleware 2012, December 3,2012-December 3,2012. Montreal, QC, Canada: Association for Computing Machinery,2012:Professional; IFIP; USENIX.
[8]Zhao Y, Ou K, Zeng W, et al. Research on cloud storage architecture and key technologies[C]. 2nd International Conference on Interaction Sciences:Information Technology, Culture and Human, ICIS 2009, November 24,2009-November 26,2009. Seoul, Korea, Republic of:Association for Computing Machinery,2009:1044-1048.
[9]Abadi D J. Data Management in the Cloud:Limitations and Opportunities[J]. IEEE Data Eng. Bull.,2009,32(1):3-12.
[10]Wu J, Ping L, Ge X, et al. Cloud Storage as the Infrastructure of Cloud Computing[C].2010: 380-383.
[11]Patterson S, Elmore A J, Nawab F, et al. Serializability, not serial:concurrency control and availability in multi-datacenter datastores[J]. Proc. VLDB Endow.,2012,5(11):1459-1470.
[12]Lin C, Wang Y, Ren F. Research on QoS in next generation network[J]. Jisuanji Xuebao/Chinese Journal of Computers,2008,31(9):1525-1535.
[13]Fujikawa K, Ohta M. Integration and simplification of QoS guarantee and multicasting for a New-Generation Network[C].9th International Conference on Optical Internet, COIN 2010, July 11, 2010-July 14,2010. Jeju, Korea, Republic of:IEEE Computer Society,2010.
[14]Wu J C, Brandt S A. Providing quality of service support in object-based file system[C].24th IEEE Conference on Mass Storage Systems and Technologies, MSST 2007, September 24,2007-September 27,2007. San Diego, CA, United states:Inst. of Elec. and Elec. Eng. Computer Society, 2007:157-168.
[15]Wang J, Varmany P, Xie C. Avoiding performance fluctuation in cloud storage[C].17th International Conference on High Performance Computing, HiPC 2010, December 19,2010 December 22,2010. Goa, India:IEEE Computer Society,2010.
[16]Mesnier M, Chen F, Luo T, et al. Differentiated storage services[C].23rd ACM Symposium on Operating Systems Principles, SOSP 2011, October 23,2011-October 26,2011. Cascais, Portugal: Association for Computing Machinery,2011:57-70.
[17]Ju J, Wu J, Fu J, et al. A survey on cloud storage[J]. Journal of Computers,2011,6(8): 1764-1771.
[18]Sota R. Storage QoS provisioning for execution programming of data-intensive applications[C]. Biological Knowledge Discovery and Data Mining. Nieuwe Hemweg 6B, Amsterdam,1013 BG, Netherlands:IOS Press,2012:69-80.
[19]Fu H. A Multi-Objective Optimization Algorithm for the QoS of Cloud Storage[J]. Fuzzy Systems, Knowledge Discovery and Natural Computation Symposium.2013,:490-498.
[20]Zhang Y. Ren S Q, Chen S B, et al. DifferCloudStor:Differentiated quality of service for cloud storage[J]. IEEE Transactions on Magnetics,2013,49(6):2451-2458.
[21]Ryu M, Ramachandran U. FlashStream:A multi-tiered storage architecture for adaptive HTTP streaming[C].21st ACM International Conference on Multimedia. MM 2013, October 21.2013-October 25,2013. Barcelona, Spain:Association for Computing Machinery,2013:313-322.
[22]Hernandez-Ramirez E M, Sosa-Sosa V J, Lopez-Arevalo I. A Comparison of Redundancy Techniques for Private and Hybrid Cloud Storage[J]. Journal of Applied Research and Technology, 2012,10:893-901.
[23]Wang Y, Sun W, Zhou S, et al. Key technologies of distributed storage for cloud computing[J]. Ruan Jian Xue Bao/Journal of Software,2012,23(4):962-986.
[24]Lim H C, Babu S, Chase J S. Automated control for elastic storage[C].7th IEEE/ACM International Conference on Autonomic Computing and Communications, ICAC-2010 and Co-located Workshops, June 7,2010-June 11,2010. Washington, DC, United states:Association for Computing Machinery,2010:1-10.
[25]Vo H T, Chen C, Ooi B C. Towards elastic transactional cloud storage with range query support[J]. Proceedings of the VLDB Endowment,2010,3(1):506-517.
[26]Wei Q, Veeravalli B, Gong B, et al. CDRM:A cost-effective dynamic replication management scheme for cloud storage cluster[C]. Proceedings-2010 IEEE International Conference on Cluster Computing, Cluster 2010.3 Park Avenue,17th Floor, New York, NY 10016-5997, United States: Institute of Electrical and Electronics Engineers Inc.,2010:188-196.
[27]Chihoub H, Ibrahim S, Antoniu G, et al. Harmony:Towards automated self-adaptive consistency in cloud storage[C].2012 IEEE International Conference on Cluster Computing, CLUSTER 2012, September 24,2012-September 28,2012. Beijing, China:IEEE Computer Society,2012:293-301.
[28]Sun D, Chang G, Gao S, et al. Modeling a dynamic data replication strategy to increase system availability in cloud computing environments[J]. Journal of Computer Science and Technology,2012, 27(2):256-272.
[29]Abad C L, Lu Y, Campbell R H. DARE:Adaptive data replication for efficient cluster scheduling[C].2011 IEEE International Conference on Cluster Computing, CLUSTER 2011, September 26,2011-September 30,2011. Austin, TX, United states:Institute of Electrical and Electronics Engineers Inc.,2011:159-168.
[30]Janpet J, Wen Y. Reliable and available data replication planning for cloud storage[C].27th IEEE International Conference on Advanced Information Networking and Applications, AINA 2013, March 25,2013-March 28,2013. Barcelona, Spain:Institute of Electrical and Electronics Engineers Inc.,2013:772-779.
[31]Silvestre G, Monnet S, Krishnaswamy R, et al. AREN:A popularity aware replication scheme for cloud storage[C].18th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2012, December 17,2012-December 19,2012. Singapore, Singapore:IEEE Computer Society,2012:189-196.
[32]Das S, Nishimura S, Agrawal D, et al. Albatross:Lightweight elasticity in shared storage databases for the cloud using live data migration[J]. Proceedings of the VLDB Endowment,2011,4(8): 494-505.
[33]Du H, Li Z. Data rearrange based on mining block access sequence in cloud storage[C].2011 International Conference on Computer Science and Network Technology, ICCSNT 2011, December 24,2011-December 26,2011. Harbin, China:IEEE Computer Society,2011:2507-2511.
[34]Lu Y, Zhang J, Wu S, et al. A hybrid dynamic load balancing approach for cloud storage[C]. 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, August 23,2012-August 25,2012. Xi'an, China:IEEE Computer Society,2012:1332-1335.
[35]Papaioannou T G, Bonvin N, Aberer K. Scalia:An adaptive scheme for efficient multi-cloud storage[C].201224th International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012, November 10,2012-November 16,2012. Salt Lake City, UT, United states:IEEE Computer Society,2012:Association for Computing Machinery (ACM); IEEE Computer Society.
[36]Chen L, Triantafillou P, Suel T, et al. Providing Scalable Database Services on the Cloud:6488 ed. Springer Berlin Heidelberg,2010:6488,1-19.
[37]Lu L, Hildebrand D, Tewari R. Zone-based data striping for cloud storage[J]. IBM Journal of Research and Development,2011,55(16).
[38]Cranor C, Polte M, Gibson G. PLFS/HDFS:HPC Applications on Cloud Storage[J]. High Performance Computing, Networking, Storage and Analysis,2012,:1410.
[39]Mat Deris M, Abawajy J H, Mamat A. An efficient replicated data access approach for large-scale distributed systems[J]. Future Generation Computer Systems,2008,24(1):1-9.
[40]Cheng K, Wang H, Wen C, et al. Dynamic file replica location and selection strategy in Data Grids[C].2008 the 1st IEEE International Conference on Ubi-Media Computing and Workshops, U-Media2008, July 31,2008-August 1,2008. Lanzhou University, China:Inst. of Elec. and Elec. Eng. Computer Society,2008:484-489.
[41]Al-Mistarihi H H E. Yong C H. On fairness, optimizing replica selection in data grids[J]. IEEE Transactions on Parallel and Distributed Systems,2009,20(8):1102-1111.
[42]Qu M, Wu X, Liao M, et al. A novel resource selection model for data grid based on QoS[C]. 2011 International Conference on Computer-Aided Material and Engineering, ICCME 2011, March 9, 2011-March 11,2011. Hangzhou, China:Trans Tech Publications,2011:203-209.
[43]Tang B, Zhang L. Optimal replica selection algorithm in data grid[C].20112nd International Conference on Theoretical and Mathematical Foundations of Computer Science, ICTMF 2011, May 5, 2011-May 6,2011. Singapore, Singapore:Springer Verlag,2011:297-304.
[44]Rahman R M, Barker K, Alhajj R. A predictive technique for replica selection in grid environment[C].7th IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2007, May 14,2007-May 17,2007. Rio de Janeiro, Brazil:Inst. of Elec. and Elec. Eng. Computer Society, 2007:163-170.
[45]Naseera S, Madhu Murthy K V. Performance evaluation of predictive replica selection using neural network approaches[C].2009 International Conference on Intelligent Agent and Multi-Agent Systems, IAMA 2009, July 22,2009-July 24,2009. Chennai, India:IEEE Computer Society,2009.
[46]Rahman R M, Alhajj R, Barker K. Replica selection strategies in data grid[J]. Journal of Parallel and Distributed Computing,2008,68(12):1561-1574.
[47]Almuttairi R M, Wankar R, Negi A, et al. Replica selection in data grids using preconditioning of decision attributes by K-means clustering (K-RSDG)[C].2nd Vaagdevi International Conference on Information Technology for Real World Problems, VCON 2010, December 9,2010-December 11, 2010. Warangal, India:IEEE Computer Society,2010:18-23.
[48]Mao Y, Ling J. Research on load balance strategy based on grey prediction theory in Cloud Storage[C].20122nd International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2012, September 26,2012-September 28,2012. Shenyang, Liaoning, China:Atlantis Press,2012:199-203.
[49]Wang C, Xue G, Li H, et al. Research on Datacopy Selection Strategy in Cloud Storage[J]. Third International Conference on Computer Engineering and Technology,2011,:349-354.
[50]Li J. A replica selection approach based on prediction in data grid[C].3rd International Conference on Semantics, Knowledge, and Grid, SKG 2007, October 29,2007-October 31,2007. Xi'an, China:Inst. of Elec. and Elec. Eng. Computer Society,2007:274-277.
[51]Wu C, Wu K, Chen M, et al. Dynamic replica selection services based on state evaluation strategy[C].4th ChinaGrid Annual Conference, ChinaGrid 2009, August 21,2009-August 22,2009. Yantai, China:IEEE Computer Society,2009:116-119.
[52]Mendez Munoz V, Amoros Vicente G, Garcia Carballeira F, et al. Emergent algorithms for replica location and selection in data grid[C]. P.O. Box 211, Amsterdam,1000 AE, Netherlands: Elsevier,2010:934-946.
[53]Xiong R, Luo J, Song A, et al. QoS preference-aware replica selection strategy using mapreduce-based PGA in Data Grids[C].40th International Conference on Parallel Processing, ICPP 2011, September 13,2011-September 16,2011. Taipei City, Taiwan:Institute of Electrical and Electronics Engineers Inc.,2011:394-403.
[54]Zhou L, Wang Y, Zhang J, et al. Optimize block-level cloud storage system with load-balance strategy[C].2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012, May 21,2012-May 25,2012. Shanghai, China:IEEE Computer Society,2012: 2162-2167.
[55]Wan J, Zhang J, Zhou L, et al. ORTHRUS:A lightweighted block-level cloud storage system[J]. Cluster Computing,2013,16(4):625-638.
[56]Harnik D, Pinkas B, Shulman-Peleg A. Side channels in cloud services:Deduplication in cloud storage[J]. IEEE Security and Privacy,2010,8(6):40-47.
[57]Wu T Y, Pan J S, Lin C F. Improving Accessing Efficiency of Cloud Storage Using De-Duplication and Feedback Schemes[J]. Systems Journal, IEEE,2014,8(1):208-218.
[58]Wu T, Lee W, Lin C F. Cloud storage performance enhancement by real-time feedback control and de-duplication[C].11th Annual Wireless Telecommunications Symposium, WTS 2012, April 18, 2012-April 20,2012. London, United kingdom:IEEE Computer Society,2012.
[59]Pamies-Juarez L, Garcia-Lopez P, Sanchez-Artigas M, et al. Towards the design of optimal data redundancy schemes for heterogeneous cloud storage infrastructures[J]. Computer Networks,2011. 55(5):1100-1113.
[60]Li Y, Guo L, Guo Y. An Efficient and Performance-Aware Big Data Storage System:Cloud Computing and Services Science. Ivanov I, Sinderen M, Leymann F, et al. Springer International Publishing,2013:367,102-116.
[61]Zhang L, Zhu L G, Zeng S F. Tiered adaptive large-scale storage system with high performance[C].2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT2013, August 17,2013-August 18,2013. Zhengzhou, Henan, China: Trans Tech Publications Ltd,2013:2371-2374.
[62]Youn C. A case study for the application of storage tiering based on ILM through data value analysis[C]. International Conference on IT Convergence and Security, ICITCS 2012, December 5, 2012-December 7,2012. Pyeong Chang, Korea, Republic of:Springer Verlag,2013:1037-1053.
[63]Spillner J, Muller J, Schill A. Creating optimal cloud storage systems[J].2012.
[64]Li Y, Guo L, Guo Y. CACSS:Towards a generic cloud storage service[C].2nd International Conference on Cloud Computing and Services Science, CLOSER 2012, April 18,2012-April 21, 2012. Porto, Portugal:Curran Associates Inc.,2012:27-36.
[65]Abu-Libdeh H, Princehouse L, Weatherspoon H. RACS:A case for cloud storage diversity[C]. 1st ACM Symposium on Cloud Computing, SoCC'10, June 6,2010-June 11,2010. Indianapolis, IN, United states:Association for Computing Machinery,2010:229-239.
[66]Cao L, Huang L, Lei K, et al. Hybrid caching for cloud storage to support traditional application[C].1st IEEE Asia Pacific Cloud Computing Congress, APCloudCC 2012, November 14, 2012-November 17,2012. Shenzhen, China:IEEE Computer Society,2012:11-15.
[67]Hu H, di Zhang, Zhou Y. A Design of Cloud Storage Gateway Based on Data Dispersal:2013: 30,605-608.
[68]Dejiao N, Tao C, Yongzhao Z, et al. Metadata caching subsystem for cloud storage[C].2nd International Conference on Green Power, Materials and Manufacturing Technology and Applications, GPMMTA 2012, July 17,2012-July 19,2012. Kunming, China:Trans Tech Publications,2012: 584-590.
[69]Tanimura Y, Yanagita S. Extension of S3 REST API for Providing QoS Support in Cloud Storage[C].11th USENIX Conference on File and Storage Technologies.2013.
[70]Blair A, Parr G, Morrow P, et al. Realtime utilisation of cloud storage[C].20121st IEEE International Conference on Cloud Networking, CLOUDNET 2012, November 28,2012-November 30,2012. Paris, France:IEEE Computer Society,2012:188-190.
[71]Greenberg A, Hamilton J, Maltz D A, et al. The cost of a cloud:research problems in data center networks[J]. SIGCOMM Comput. Commun. Rev.,2008,39(1):68-73.
[72]Jain S, Kumar A, Mandal S, et al. B4:experience with a globally-deployed software defined wan[J]. SIGCOMM Comput. Commun. Rev.,2013,43(4):3-14.
[73]Grossman R L, Gu Y, Sabala M, et al. Compute and storage clouds using wide area high performance networks[J]. Future Generation Computer Systems,2009,25(2):179-183.
[74]Gu Y, Grossman R L. Sector:A high performance wide area community data storage and sharing system[J]. Future Generation Computer Systems,2010,26(5):720-728.
[75]Wang D. An efficient cloud storage model for heterogeneous cloud infrastructures[C].2011 International Conference on Power Electronics and Engineering Application, PEEA 2011, December 24,2011-December 25,2011. Shenzhen, China:Elsevier Ltd,2011:510-515.
[76]Sakr S, Liu A, Batista D M, et al. A Survey of Large Scale Data Management Approaches in Cloud Environments[J]. Communications Surveys Tutorials, IEEE,2011,13(3):311-336.
[77]Ruiz-Alvarez A, Humphrey M. An automated approach to cloud storage service selection[C]. 2nd International Workshop on Scientific Cloud Computing, ScienceCloud'l 1, Co-located with 20th International ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2011, June 8,2011-June 8,2011. San Jose, CA, United states:Association for Computing Machinery, 2011:39-48.
[78]Palankar M, Lamnitchi A, Ripeanu M, et al. Amazon S3 for science grids:A viable solution?[C]. 2008 International Workshop on Data-aware Distributed Computing 2008, DADC'08, June 24,2008-June 24,2008. Boston, MA, United states:Association for Computing Machinery,2008:55-64.
[79]Dean J. Evolution and future directions of large-scale storage and computation systems at Google[C]. Proceedings of the 1st ACM symposium on Cloud computing. Indianapolis, Indiana, USA: ACM,2010:1.
[80]Calder B, Wang J, Ogus A, et al. Windows azure storage:A highly available cloud storage service with strong consistency[C].23rd ACM Symposium on Operating Systems Principles. SOSP 2011, October 23,2011-October 26,2011. Cascais, Portugal:Association for Computing Machinery, 2011:143-157.
[81]Huo Y, Wang H, Hu L, et al. A cloud storage architecture model for data-intensive applications[C].2011 International Conference on Computer and Management, CAMAN 2011, May 19,2011-May 21,2011. Wuhan, China:IEEE Computer Society.2011:IEEE Wuhan Section; Hunan University; Wuhan University; Engineering Information Institute; Chongqing VIP Information Co., Ltd.
[82]Gu G, Li Q. Wen X, et al. An overview of newly open-source cloud storage platforms[C].2012 IEEE International Conference on Granular Computing, GrC 2012, August 11,2012-August 13,2012. HangZhou, China:IEEE Computer Society,2012:142-147.
[83]Soares T S, Dantas M A R, De Macedo D D J, et al. A data management in a private cloud storage environment utilizing high performance distributed file systems[C].2013 IEEE 22nd International Workshop on Enabling Technologies:Infrastructure for Collaborative Enterprises, WETICE 2013, June 17,2013-June 20,2013. Hammamet, Tunisia:IEEE Computer Society,2013: 158-163.
[84]Ghemawat S, Gobioff H, Leung S. The google file system[C]. SOSP'03:Proceedings of the 19th ACM Symposium on Operating Systems Principles, October 19,2003-October 22,2003. Lake George, NY, United states:Association for Computing Machinery,2003:29-43.
[85]Ford D, Labelle F C C O, Popovici F I, et al. Availability in Globally Distributed Storage Systems[C]. OSDI'10. Berkeley, CA, USA:USENIX Association,2010:1-7.
[86]Jones T, Koniges A, Yates R K. Performance of the IBM general parallel file system[J]. Proceedings of the International Parallel Processing Symposium, IPPS,2000,:673-681.
[87]Zhao T, March V, Dong S, et al. Evaluation of a performance model of lustre file system[C].5th Annual ChinaGrid Conference, ChinaGrid 2010, July 16,2010-July 18,2010. Guangzhou, Guangdong, China:IEEE Computer Society,2010:191-196.
[88]Weil S A, Brandt S A, Miller E L, et al. Ceph:a scalable, high-performance distributed file system[C]. Proceedings of the 7th symposium on Operating systems design and implementation. Seattle, Washington:USENIX Association,2006:307-320.
[89]Shvachko K, Kuang H, Radia S, et al. The Hadoop distributed file system[C].2010 IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST2010, May 6,2010-May 7,2010. Lake Tahoe, NV, United states:IEEE Computer Society,2010:IEEE.
[90]Dean J, Ghemawat S. MapReduce:Simplified data processing on large clusters[J]. Communications of the ACM,2008,51(1):107-113.
[91]Chang F, Dean J, Ghemawat S, et al. Bigtable:A distributed storage system for structured data[J]. ACM Transactions on Computer Systems,2008,26(2).
[92]Azzedin F. Towards a scalable HDFS architecture[C].2013 International Conference on Collaboration Technologies and Systems, CTS 2013, May 20,2013-May 24,2013. San Diego, CA, United states:IEEE Computer Society,2013:155-161.
[93]Kala Karun A, Chitharanjan K. A review on hadoop-HDFS infrastructure extensions[C].2013 IEEE Conference on Information and Communication Technologies, ICT 2013, April 11,2013-April 12,2013. Thuckalay, Tamil Nadu, India:IEEE Computer Society,2013:132-137.
[94]Dong B, Zheng Q, Tian F, et al. An optimized approach for storing and accessing small files on cloud storage[J]. Journal of Network and Computer Applications,2012,35(6):1847-1862.
[95]Xiao L, Wu C, Wei Q. QoS optimization in object storage system[C]. Photonics and Optoelectronics Meetings, POEM 2009-Optical Storage and New Storage Technologies, August 8, 2009-August 10,2009. Wuhan, China:SPIE,2009:Chinese Optical Society; Huazhong University of Science and Technology; China Hubei Provincial Science Technology Department; Adm. Comm. Wuhan East Lake High-Tech Dev. Zone.
[96]Brim M J, Dillow D A, Oral S, et al. Asynchronous object storage with QoS for scientific and commercial big data[C].8th Parallel Data Storage Workshop, PDSW 2013-Held in Conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013, November 17,2013-November 22,2013. Denver, CO, United states:Association for Computing Machinery,2013:7-13.
[97]Gupta P, Pegah M. A new thought paradigm:Delivering cost effective and ubiquitously accessible storage with enterprise backup system via a multi-tiered storage framework[C]. SIGUCCS'07:2007 Fall Conference on User Services-Inspiring Magical Outcomes, October 7,2007-October 10,2007. United states:Association for Computing Machinery,2007:146-152.
[98]Greenberg A, Hamilton J R, Jain N, et al. VL2:A scalable and flexible data center network[J]. Communications of the ACM,2011.54(3):95-104.
[99]Seddiki M S, Frikha M. A non-cooperative game theory model for bandwidth allocation in network virtualization[C].201215th International Telecommunications Network Strategy and Planning Symposium, NETWORKS 2012, October 15,2012-October 18,2012. Rome, Italy:IEEE Computer Society,2012.
[100]Casanova H, Legrand A, Quinson M. SimGrid:A generic framework for large-scale distributed experiments[C]. UKSim 10th International Conference on Computer Modelling and Simulation, EUROSIM/UKSim2008, April 1,2008-April 3.2008. Cambridge. United kingdom:Inst. of Elec. and Elec. Eng. Computer Society,2008:126-131.
[101]Quinson M. SimGrid:A generic framework for large-scale distributed experiments[C]. IEEE P2P'09-9th International Conference on Peer-to-Peer Computing, September 9,2009-September 11, 2009. Seattle, WA, United states:IEEE Computer Society,2009:95-96.
[102]Casanova H. Simgrid:A toolkit for the simulation of application scheduling[C].1st IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2001, May 15,2001-May 18, 2001. Brisbane, QLD, Australia:IEEE Computer Society,2001:430-437.
[103]Legrand A, Marchal L, Casanova H. Scheduling distributed applications:The SimGrid simulation framework[C].3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2003, May 12,2003-May 15,2003. Tokyo, Japan:IEEE Computer Society,2003: 138-145.
[104]Bell W H, Cameron D G, Capozza L, et al. Simulation of dynamic grid replication strategies in OptorSim[C].3rd International Workshop on Grid Computing, GRID 2002, November 18,2002-November 18,2002. Baltimore, MD, United states:Springer Verlag,2002:46-57.
[105]Calheiros R N, Ranjan R, Beloglazov A, et al. CloudSim:A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software-Practice and Experience,2011,41(1):23-50.
[106]Long S, Zhao Y. A toolkit for modeling and simulating cloud data storage:an extension to CloudSim[J].2012 International Conference on Control Engineering and Communication Technology, 2012,:597-600.
[107]Baumgart I, Heep B, Krause S. OverSim:A flexible overlay network simulation framework[C]. 2007 IEEE Global Internet Symposium, GI, May 11,2007-May 11,2007. Anchorage, AK, United states:Inst. of Elec. and Elec. Eng. Computer Society,2007:79-84.
[108]Kazmi I, Bukhari S F Y. PeerSim:An efficient scalable testbed for heterogeneous cluster-based P2P network protocols[C].2011 UKSim 13th International Conference on Modelling and Simulation, UKSim 2011, March 30,2011-April 1,2011. Cambridge, United kingdom:IEEE Computer Society, 2011:420-425.
[109]Imbert M, Caron E. Dynamic network forecasting using simgrid simulations[C].2012 IEEE International Conference on Cluster Computing, CLUSTER 2012, September 24,2012-September 28, 2012. Beijing, China:IEEE Computer Society,2012:542-545.
[110]Liu S, Huang X, Fu H, et al. Understanding data characteristics and access patterns in a cloud storage system[C].13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, May 13,2013-May 16,2013. Delft, Netherlands:IEEE Computer Society,2013: 327-334.
[111]Thangaraj R, Pant M, Abraham A, et al. Modified Particle Swarm Optimization with time varying velocity vector[J]. International Journal of Innovative Computing, Information and Control, 2012,8(1A):201-218.
[112]Liu H, Abraham A, Snael V. Convergence analysis of swarm algorithm[C].2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009, December 9,2009 December 11,2009. Coimbatore, India:IEEE Computer Society,2009:1714-1719.
[113]Chakraborty P, Das S, Roy G G, et al. On convergence of the multi-objective particle swarm optimizers[J]. Information Sciences,2011,181(8):1411-1425.
[114]Wang X, Ding S. An overview of quotient space theory[C].2011 International Conference on Sport Material, Modelling and Simulation, ICSMMS 2011, January 27,2011-January 28,2011. Shenzhen, China:Trans Tech Publications,2011:326-331.
[115]Zhao L, Zhang L. Advances in the quotient space theory and its applications[C].701 E. Chocolate Ave., Suite 200, Hershey, PA 17033-1240, United States:IGI Publishing,2009:39-50.
[116]Gao S, Cao C. Convergence analysis of particle swarm optimization algorithm[J]. Advances in Information Sciences and Service Sciences,2012,4(14):25-32.
[117]Mungal A G. Multi-core computing and the tiered storage model[C].35th International Conference on Computer Measurement Group, December 6,2009-December 11,2009. Dallas, TX, United states:Computer Measurement Group Inc,2009.
[118]Zeng L, Feng D, Wang F, et al. Object replication and migration policy based on OSS[C]. International Conference on Machine Learning and Cybernetics, ICMLC 2005. August 18,2005 August 21,2005. Guangzhou, China:Institute of Electrical and Electronics Engineers Computer Society,2005:45-49.
[119]Shan Y, Yao N, Yu J. A hierarchical cache scheme for object storage system[C].2009 International Conference on New Trends in Information and Service Science, NISS 2009, June 30, 2009-July 2,2009. Beijing, China:IEEE Computer Society,2009:20-22.
[120]Luo Y, Deng W. An adaptive data placement scheme for scaleable object storage system[C].8th International Symposium on Optical Storage and 2008 International Workshop on Information Data Storage, November 24,2008-November 27,2008. Wuhan, China:SPIE,2009:Shanghai Institute of Optics and Fine Mechanics, CAS.
[121]Huang P, Peng H, Lin P, et al. Macroeconomics based Grid resource allocation[J]. Future Generation Computer Systems,2008,24(7):694-700.
[122]Lerner V. Macroeconomic analysis of the information dynamic model[J]. Cybernetics and Systems,1993,24(6):591-633.
[123]Kapetanios G, Pagan A, Scott A. Making a match:Combining theory and evidence in policy-oriented macroeconomic modeling[J]. Journal of Econometrics,2007,136(2):565-594.
[124]Wang F, Krunz M, Cui S. Price-based spectrum management in cognitive radio networks[J]. IEEE Journal on Selected Topics in Signal Processing,2008,2(1):74-87.
[125]Wang H, Tian Z. Intelligent price-based congestion control for communication networks[C]. 2010 IEEE 18th International Workshop on Quality of Service, IWQoS 2010, June 16,2010-June 18, 2010. Beijing, China:Institute of Electrical and Electronics Engineers Inc.,2010.
[126]Yuan P, Liang Y, Bi G. Price-based distributed resource allocation for femtocell networks[C]. 2012 IEEE International Conference on Communication Systems, ICCS 2012, November 21,2012-November 23,2012. Singapore, Singapore:IEEE Computer Society,2012:418-422.
[127]Yamanaka N, Sato Y, Sato K. Performance limitation of the leaky bucket algorithm for ATM networks[J]. IEEE Transactions on Communications,1995,43(8):2298-2300.
[128]Solomon B, Ionescu D, Gadea C, et al. Leaky bucket model for autonomic control of distributed, collaborative systems[C].8th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2013, May 23,2013-May 25,2013. Timisoara, Romania:IEEE Computer Society,2013:483-488.
[129]Khan A J, Sahoo A, Manjunath D. Implementation of WFQ in a distributed open software router[C].36th Annual IEEE Conference on Local Computer Networks, LCN 2011, October 4,2011-October 7,2011. Bonn, Germany:IEEE Computer Society,2011:519-527.
[130]Taniguchi S, Kawate R, Sato K, et al. Performance evaluation of the simplified WFQ to multiplex a huge number of queues[C].2012 IEEE International Workshop Technical Committee on Communications Quality and Reliability, CQR 2012, May 15,2012-May 17,2012. San Diego, CA, United states:IEEE Computer Society,2012.
[131]Kim H, Hou J C. Enabling network calculus-based simulation for TCP congestion control[J]. Computer Networks,2009,53(1):1-24.
[132]Angrishi K, Killat U. An approach using node operating point for performance analysis with network calculus[C].2011 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2011, June 27,2011-June 30,2011. The Hague, Netherlands: IEEE Computer Society,2011:30-37.
[133]Li Q, Chen Z, Zhang L, et al. Deterministic upper bounds on QoS performance about wireless ad hoc network based on network calculus[J]. Tongxin Xuebao/Journal on Communications,2008,29(9): 32-39.
[134]Zhang L, Liu J, Yang K. Quality of Service Modelling of Virtualized Wireless Networks:A Network Calculus Approach[J].2014.
[135]Lv B, Wang Z, Huang T, et al. A hierarchical virtual resource management architecture for network virtualization[C].20106th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2010, September 23,2010-September 25,2010. Chengdu, China:IEEE Computer Society,2010:IEEE Antennas and Propagation Society; IEEE Communications Society; Southwest Jiaotong University; University of Electronic Science and Technology of China; Wuhan University.
[136]Kang M, Kang E, Hwang D, et al. Formal modeling and verification of SDN-OpenFlow[C]. IEEE 6th International Conference on Software Testing, Verification and Validation, ICST 2013, May 18,2013-May 20,2013. Luxembourg, Luxembourg:IEEE Computer Society,2013:481-482.
[137]Shin M, Nam K, Kim H. Software-defined networking (SDN):A reference architecture and open APIs[C].2012 International Conference on 1CT Convergence:"Global Open Innovation Summit for Smart ICT Convergence", ICTC 2012, October 15,2012-October 17,2012. Jeju Island, Korea, Republic of:IEEE Computer Society,2012:360-361.
[138]Azodolmolky S, Wieder P, Yahyapour R. SDN-based cloud computing networking[C].2013 15th International Conference on Transparent Optical Networks,ICTON 2013, June 23,2013-June 27,2013. Cartagena, Spain:IEEE Computer Society,2013.
[139]Sharma P, Banerjee S, Tandel S, et al. Enhancing network management frameworks with SDN-like control[C].2013IFIP/IEEE International Symposium on Integrated Network Management, IM 2013, May 27,2013-May 31,2013. Ghent. Belgium:IEEE Computer Society,2013:688-691.
[140]Bueno I, Aznar J I, Escalona E, et al. An OpenNaaS based SDN framework for dynamic QoS control[C].2013 Workshop on Software Defined Networks for Future Networks and Services, SDN4FNS 2013, November 11,2013-November 13,2013. Trento, Italy:IEEE Computer Society, 2013.
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