面向绿色虚拟数据中心资源管理的若干关键技术研究
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摘要
随着Internet的繁荣以及云计算技术的发展，数据中心正变得空前重要。但是当前数据中心动辄装备十万、百万规模的服务器以及相应的其它IT基础设施，使资源管理变得非常困难。此外，海量的基础设施时刻消耗巨大的电能，这不仅增加了运营商的运营成本，而且导致大量的二氧化碳排放。数据中心资源虚拟化、资源管理动态化、弹性化、自动化、节能化成为业界一致认可的发展趋势。面向节能的自适应资源管理是当前研究绿色虚拟数据中心的热点之一。论文相关工作以此为依据，首先分析和总结了当前数据中心中基于资源管理的主要节能技术，然后围绕虚拟数据中心资源管理中的若干关键问题进行了深入研究。论文的主要工作包括：
1.提出了一种基于管理员资源选择偏好的虚拟数据中心资源规划方法。首先基于粒度粗糙理论，提出了支持偏好语义表述的偏好粒子模型以及纵向聚合运算操作符和横向融合运算操作符，建立了一种基于粗糙粒度表示的用户偏好模型，解决了当前用户偏好模型不支持语义表述以及不能处理不确定性偏好的问题。其次，将该模型运用于描述数据中心资源规划过程中的管理员的资源选择偏好。同时，基于不同应用的负载峰值会出现在一天中的不同时段的原理，提出了基于应用负载分时分析的建模方法，在保证不违背服务等级协议（Service LevelAgreement, SLA）的前提下能够使资源利用率进一步提升。考虑了虚拟机（VirtualMachine, VM）之间的联系性和互斥性，VM与服务器之间的兼容性，提出了5项资源规划原则。将资源规划问题看作是有约束的多维装箱问题，提出了基于分组遗传算法（Grouping GeneticAlgorithm，GGA）的智能优化算法搜索全局最优解。最后通过仿真实验对模型进行了验证。
2.针对当前虚拟数据中心中面向节能的动态资源优化方法仅考虑资源利用率最大化，而忽略了VM位置变化（包括VM启动、迁移和回收）带来的系统开销问题，提出一种能同时减少系统能耗以及VM位置变化的动态资源优化新方法。该方法采用基于周期性控制回路的资源管理框架，将动态优化分配问题表述为一个双目标组合优化问题，并提出了一种基于网络流理论和迭代优化的近似优化算法NFT-DRP(Network Flow Theory based Dynamical Resource Provision)。实验结果表明，新方法在降低系统能耗方面与现有方法相比略有提升，而在减少VM位置变化方面则有较大改进。
3.针对当前虚拟数据中心面向节能的VM动态整合研究仅考虑服务器能耗，而忽略了网络设备能耗的现状，提出了一种同时降低服务器和网络设备能耗的新方法。该方法通过感知数据中心的网络拓扑结构，采用一种混合粒子群优化算法HPSO-NA(Hybrid Particle Swarm Optimization-Network Aware)来计算最优的VM整合方案，使运行的服务器和网络设备最少以实现最小化能耗。实验结果表明该方法能进一步降低系统整体能耗。
4.针对当前主要的热点检测模型均只能基于传统资源（CPU，内存，网络带宽）进行决策的现状，提出了一种新的基于多条件决策的虚拟数据中心热点检测模型。该模型通过将不同量纲的系统特征值纳入一个统一决策模型，实现对系统中的服务器和VM进行评分。采用了阈值法，通过设置热点阈值(Hotspot DetectingThreshold，HDT)对系统热点进行检测，当服务器评分超过HDT时，从该服务器上选择VM进行迁移以消除热点；当VM的评分超过HDT时，则首先判断是否可以通过本地资源再分配消除热点，否则对该VM进行迁移。实验结果证明了该模型的有效性。
5.针对当前CMOS多核嵌入式处理器片上仅提供全局动态电压缩放（Dynamic Voltage Scaling，DVS）支持以及亚纳米时代后CMOS处理器泄漏功耗不可忽视的现状，提出了一种新的多核嵌入式环境中的硬实时任务感功调度算法GRR&CS。算法通过基于贪心法的静态任务划分，基于全局资源回收利用和任务迁移的动态负载均衡，以及动态核缩放三个步骤实现整体能耗的降低，并同时保证实时任务的可调度性约束。实验表明，提出的算法相比较现有算法多节省14.8%-41.2%的能耗。
With the prosperity of Internet and development of cloud computing technology,data center becomes more and more important. Currently data center hosts overhundreds of thousands of servers as well as other IT infrastructure devices which notonly makes managerial work more difficult, but also consumes huge amount of energyat the same time. Furthermore, this not only reduces the profit margin of serviceprovider, but also leads to high carbon emission. A unanimously approved trend is thatdata center resource virtualization and resource management has become dynamic,flexible, automatic and energy efficient. One of the hot issues in this research domain isenergy-efficient adaptive resource management. Based on this proposal, this thesisanalyzes and concludes the state-of-art techniques and research on energy efficientresource management in data center at first, then do depth study of some key issuesaround resource management in virtualized data center. The main contributions in thisthesis include:
1. An administrator’s preference based method for capacity planning in virtualizedenterprise data center is proposed. First, based on granular rough theory, a family ofatomic preference granules supporting semantic alongwith two operators, namedvertical aggregation operator and horizon combination operator are proposed, and anew preference model, which makes use of rough granules to describe user preference,is also built. The new model can address the said issue that existing scheme cannot dealwith using semantic preference representation and uncertain data. Second, thepreference model is applied to describe the administrator’s preference of resourceselection. Meanwhile, due to the peak-valley of the workload of applications is indifferent time, a way to problem modeling is proposed based on workload timesharinganalysis, which can achieve maximal resource utilization on the premise of keeping therequired QoS. The associated services and the mutually exclusive services, thecompatibility between services and servers are taken into account and five principles ofconsolidation are proposed. The establishing model is regarded as a multi-dimension binpacking problem with several constraints and a GGA-based algorithm is proposed to search for the global optimal solution. Finally, the proposed method is proved with thehelp of experiments.
2. Since existing methods of energy efficient dynamic resource optimization indata center just maximize resource utilization without considering the overhead of VM(Virtual Machine) placement change (e.g. VM deployment, start, migration and reclaim),we propose a new dynamical resource optimization method which can minimize theenergy consumption and VM placement change at the same time. The new methodadopts control loop based resource management frame work, and regards dynamicalresource optimization as a dual-objective combinational optimization problem, andmoreover it designs a network-flow-theory and iterative optimization basedapproximate algorithm, called NFT-DRP, to solve it. The experimental results whencompared to existing work show that, the proposed method can slightly decrease theenergy consumption but greatly decrease the number of VM placement change.
3. Since current researches only consider part of energy consumed by servers, anovel method is proposed to reduce energy consumption from both servers and networkdevices. The proposal achieves minimum energy consumption by minimizing the activeservers and network devices through analyzing the net topology. Moreover, the workformally models the problem and designs a hybrid particle swarm algorithm calledHPSO-NA to implement virtual machine consolidation. The experimental results showthat the proposed method can effectively reduce the energy consumption.
4. Since current hotspot detecting algorithms work based on traditional resource(i.e. CPU, memory, bandwidth, etc.), a new multi-criteria decision making based modelis proposed to detect hotspot and make decisions of VM scheduling in virtualized datacenter. By incorporating system eigenvalue with different dimensions into the unifieddecision model, it can score the servers and VM in system. Moreover, a HDT ispredefined and applied to detect both server hotspot and VM hotspot. If the server’sscore is higher than HDT, we select suitable VM and shift them to remove hotspot.When VM’s score is higher than HDT, the VM’s resource is reallocated to removehotspot if it is possible; else the VM would be migrated to other server. Experimentalresults prove the efficiency of the model.
5. Since that CMOS multi-core embedded processor only provides global DVS andits leakage power is negligible, this work proposes a new power-aware scheduling algorithm for hard real-time tasks in multi-core embedded environment, calledGRR&CS. The power saving is achieved by three steps including greedy-based statictasks partition, and global resource reclamation based dynamic load balance anddynamic core scaling. The algorithm also keeps the schedulability of tasks. Experimentsshow that the proposed algorithm saves14.8%-41.2%energy more than other existingworks.
1.提出了一种基于管理员资源选择偏好的虚拟数据中心资源规划方法。首先基于粒度粗糙理论，提出了支持偏好语义表述的偏好粒子模型以及纵向聚合运算操作符和横向融合运算操作符，建立了一种基于粗糙粒度表示的用户偏好模型，解决了当前用户偏好模型不支持语义表述以及不能处理不确定性偏好的问题。其次，将该模型运用于描述数据中心资源规划过程中的管理员的资源选择偏好。同时，基于不同应用的负载峰值会出现在一天中的不同时段的原理，提出了基于应用负载分时分析的建模方法，在保证不违背服务等级协议（Service LevelAgreement, SLA）的前提下能够使资源利用率进一步提升。考虑了虚拟机（VirtualMachine, VM）之间的联系性和互斥性，VM与服务器之间的兼容性，提出了5项资源规划原则。将资源规划问题看作是有约束的多维装箱问题，提出了基于分组遗传算法（Grouping GeneticAlgorithm，GGA）的智能优化算法搜索全局最优解。最后通过仿真实验对模型进行了验证。
2.针对当前虚拟数据中心中面向节能的动态资源优化方法仅考虑资源利用率最大化，而忽略了VM位置变化（包括VM启动、迁移和回收）带来的系统开销问题，提出一种能同时减少系统能耗以及VM位置变化的动态资源优化新方法。该方法采用基于周期性控制回路的资源管理框架，将动态优化分配问题表述为一个双目标组合优化问题，并提出了一种基于网络流理论和迭代优化的近似优化算法NFT-DRP(Network Flow Theory based Dynamical Resource Provision)。实验结果表明，新方法在降低系统能耗方面与现有方法相比略有提升，而在减少VM位置变化方面则有较大改进。
3.针对当前虚拟数据中心面向节能的VM动态整合研究仅考虑服务器能耗，而忽略了网络设备能耗的现状，提出了一种同时降低服务器和网络设备能耗的新方法。该方法通过感知数据中心的网络拓扑结构，采用一种混合粒子群优化算法HPSO-NA(Hybrid Particle Swarm Optimization-Network Aware)来计算最优的VM整合方案，使运行的服务器和网络设备最少以实现最小化能耗。实验结果表明该方法能进一步降低系统整体能耗。
4.针对当前主要的热点检测模型均只能基于传统资源（CPU，内存，网络带宽）进行决策的现状，提出了一种新的基于多条件决策的虚拟数据中心热点检测模型。该模型通过将不同量纲的系统特征值纳入一个统一决策模型，实现对系统中的服务器和VM进行评分。采用了阈值法，通过设置热点阈值(Hotspot DetectingThreshold，HDT)对系统热点进行检测，当服务器评分超过HDT时，从该服务器上选择VM进行迁移以消除热点；当VM的评分超过HDT时，则首先判断是否可以通过本地资源再分配消除热点，否则对该VM进行迁移。实验结果证明了该模型的有效性。
5.针对当前CMOS多核嵌入式处理器片上仅提供全局动态电压缩放（Dynamic Voltage Scaling，DVS）支持以及亚纳米时代后CMOS处理器泄漏功耗不可忽视的现状，提出了一种新的多核嵌入式环境中的硬实时任务感功调度算法GRR&CS。算法通过基于贪心法的静态任务划分，基于全局资源回收利用和任务迁移的动态负载均衡，以及动态核缩放三个步骤实现整体能耗的降低，并同时保证实时任务的可调度性约束。实验表明，提出的算法相比较现有算法多节省14.8%-41.2%的能耗。
With the prosperity of Internet and development of cloud computing technology,data center becomes more and more important. Currently data center hosts overhundreds of thousands of servers as well as other IT infrastructure devices which notonly makes managerial work more difficult, but also consumes huge amount of energyat the same time. Furthermore, this not only reduces the profit margin of serviceprovider, but also leads to high carbon emission. A unanimously approved trend is thatdata center resource virtualization and resource management has become dynamic,flexible, automatic and energy efficient. One of the hot issues in this research domain isenergy-efficient adaptive resource management. Based on this proposal, this thesisanalyzes and concludes the state-of-art techniques and research on energy efficientresource management in data center at first, then do depth study of some key issuesaround resource management in virtualized data center. The main contributions in thisthesis include:
1. An administrator’s preference based method for capacity planning in virtualizedenterprise data center is proposed. First, based on granular rough theory, a family ofatomic preference granules supporting semantic alongwith two operators, namedvertical aggregation operator and horizon combination operator are proposed, and anew preference model, which makes use of rough granules to describe user preference,is also built. The new model can address the said issue that existing scheme cannot dealwith using semantic preference representation and uncertain data. Second, thepreference model is applied to describe the administrator’s preference of resourceselection. Meanwhile, due to the peak-valley of the workload of applications is indifferent time, a way to problem modeling is proposed based on workload timesharinganalysis, which can achieve maximal resource utilization on the premise of keeping therequired QoS. The associated services and the mutually exclusive services, thecompatibility between services and servers are taken into account and five principles ofconsolidation are proposed. The establishing model is regarded as a multi-dimension binpacking problem with several constraints and a GGA-based algorithm is proposed to search for the global optimal solution. Finally, the proposed method is proved with thehelp of experiments.
2. Since existing methods of energy efficient dynamic resource optimization indata center just maximize resource utilization without considering the overhead of VM(Virtual Machine) placement change (e.g. VM deployment, start, migration and reclaim),we propose a new dynamical resource optimization method which can minimize theenergy consumption and VM placement change at the same time. The new methodadopts control loop based resource management frame work, and regards dynamicalresource optimization as a dual-objective combinational optimization problem, andmoreover it designs a network-flow-theory and iterative optimization basedapproximate algorithm, called NFT-DRP, to solve it. The experimental results whencompared to existing work show that, the proposed method can slightly decrease theenergy consumption but greatly decrease the number of VM placement change.
3. Since current researches only consider part of energy consumed by servers, anovel method is proposed to reduce energy consumption from both servers and networkdevices. The proposal achieves minimum energy consumption by minimizing the activeservers and network devices through analyzing the net topology. Moreover, the workformally models the problem and designs a hybrid particle swarm algorithm calledHPSO-NA to implement virtual machine consolidation. The experimental results showthat the proposed method can effectively reduce the energy consumption.
4. Since current hotspot detecting algorithms work based on traditional resource(i.e. CPU, memory, bandwidth, etc.), a new multi-criteria decision making based modelis proposed to detect hotspot and make decisions of VM scheduling in virtualized datacenter. By incorporating system eigenvalue with different dimensions into the unifieddecision model, it can score the servers and VM in system. Moreover, a HDT ispredefined and applied to detect both server hotspot and VM hotspot. If the server’sscore is higher than HDT, we select suitable VM and shift them to remove hotspot.When VM’s score is higher than HDT, the VM’s resource is reallocated to removehotspot if it is possible; else the VM would be migrated to other server. Experimentalresults prove the efficiency of the model.
5. Since that CMOS multi-core embedded processor only provides global DVS andits leakage power is negligible, this work proposes a new power-aware scheduling algorithm for hard real-time tasks in multi-core embedded environment, calledGRR&CS. The power saving is achieved by three steps including greedy-based statictasks partition, and global resource reclamation based dynamic load balance anddynamic core scaling. The algorithm also keeps the schedulability of tasks. Experimentsshow that the proposed algorithm saves14.8%-41.2%energy more than other existingworks.
引文
[1] Uptime Institute.数据中心分级标准. http://uptimeinstitute.com/TierCertification/
[2] G. Schulz (著).绿色虚拟数据中心[M].韩毅刚,李亚娜,王欢(译).北京：人民邮电出版社,2010,50-88.
[3] Wikipedia.虚拟化的定义. http://en.wikipedia.org/wiki/Virtualization
[4]王庆波,金涬,何乐等著.虚拟化与云计算[M].北京：电子工业出版社,2009.
[5] P. Barham, B. Dragovic, K. Fraser, et al. Xen and the art of virtualization[C]. Proceedings ofthe19th ACM Symposium on Operating Systems Principles (SOSP’03),2003,164-177.
[6] R. Hiremane. Intel Virtualization Technology for Directed I/O (Intel VT-d)[J]. Intel Magazine,2007,4(10):179-192.
[7] VMWare.AMD64Virtualization Codenamed “Pacifica” Technology. VMWare,2005.
[8]刘鹏.云计算(第二版)[M].北京：电子工业出版社.2011,1-15.
[9] P. Mell, T. Grance. The NIST definition of cloud computing[R]. USA: NIST,2011.
[10] M. Armbrust, A. Fox, R. Griffith, et al. Above the Clouds: A Berkeley View of CloudComputing[J]. Communications of the ACM,2010,53(4):50-58.
[11] Gartner. Meeting the DC Power and Cooling Challenges.2008.
[12] EPA. Report to congress on server and data center energy efficiency[R]. U.S. EnvironmentalProtection Agency,2007.
[13] TGG. The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE.2007.
[14] J. Sweeney, J. Bradfield. Reducing data centers power and energy consumption: savingmoney and go green.2008.
[15] W. Tschudi, T. Xu, D. Sartor, et al. Energy efficient data center[R]. USA：Berkeley,2004.
[16] J. Hamilton. Cooperative Expendable Micro-Slice Servers (CEMS): Low Cost, Low PowerServers for Internet-Scale Services[C]. Proceedings of the4thBiennial Conference onInnovative Data Systems Research (CIDR’09), Asilomar,2009,1-8.
[17] S.-M. Kang, Y. Leblebici. CMOS Digital Integrated Circuits-Analysis and Design[M].北京：清华大学出版社,2002.
[18] L. Benini, A. Bogliolo, G. D. Micheli. A Survey of Design Techniques for System-LevelDynamic Power Management[J]. IEEE Trans. on VLSI,2000,8(3):299-316.
[19] Intel. Wireless Intel SpeedStep Power Manager: optimizing power consumption for the IntelPXA27x processor family.2004.
[20] Tom Farre, Bulkeley. Energy-Efficient Servers:ABright Spot in the Cloud.AMD,2009.
[21] Intel White Paper. Why the Intel Xeon Processor5500Series is the Ideal Foundation forCloud Computing.2009.
[22] Hewlett-Packard, Intel, Microsoft, et al. Advanced configuration and power interfacespecification[S].2011.
[23] F. Yao, A. Demers, S. Shenker. A scheduling model for reduced CPU energy[C]. Proceedingsof the36thAnnual Symposium on Foundations of Computer Science (FOCS’95),1995:374-382.
[24] P. Padmanabhan, G. S. Kang. Real-time dynamic voltage scaling for low-power embeddedoperating systems[C]. Proceedings of the18thACM Symposium on Operating SystemsPrinciples (SOSP’03),2001,89-102.
[25] S. Martin, K. Flautne, T. Mudge, et al. Combined dynamic voltage scaling and adaptive bodybiasing for lower power microprocessors under dynamic workloads[C]. Proceedings ofInternational Conference on Computer-Aided Design (ICCAD’02),2002,721–725.
[26] R. Jejurikar, C. Pereira, R. K. Gupta. Leakage aware dynamic voltage scaling for real-timeembedded systems[C]. Proceedings of the41stDesign Automation Conference (DAC’04),2004,275-280.
[27] L. Yan, J. Luo, N. K. Jha. Joint dynamic voltage scaling and adaptive body biasing forheterogeneous distributed real-time embedded systems[J]. IEEE Trans. on CAD of IntegratedCircuits and Systems,2005,24(7):1030-1041.
[28] H. Aydin, Y. Qi. Energy-Aware Partitioning for Multiprocessor Real-Time Systems[C].Proceedings of the17thInternational Parallel and Distributed Processing Symposium(IPDPS’03),2003,113b.
[29] D. Zhu, R. Melhem, B. R. Childers. Scheduling with Dynamic Voltage/Speed AdjustmentUsing Slack Reclamation in Multiprocessor Real Time Systems[J]. IEEE Trans. on Paralleland Distributed Systems,2003,14(7):686-699.
[30]钟虓,齐勇,侯迪,等.基于DVS的多核实时系统节能调度[J].电子学报,2006,34(12A):2481-2484.
[31]敬思远,佘堃,钟毅.用于多核嵌入式环境的硬实时任务感功调度算法[J].计算机应用,2011,31(11):2936-2939.
[32] Accenture. Data Centre Energy Forecast Report[R]. EPA,2008.
[33] E. Pinheiro, R. Bianchini, E. V. Carrera, et al. Load Balancing and Unbalancing for Powerand Performance in Cluster-Based Systems[C]. Proceedings of the International Workshopon Compilers and Operating Systems for Low Power,2001.
[34] J. S. Chase, D. C. Anderson, P. N. Thakar, et al. Managing energy and server resources inhosting centers[C]. Proceedings of the18thACM symposium on Operating systemsprinciples (SOSP’01),2001,103-116.
[35] E. N. Elnozahy, M. Kistler, R. Rajamony. Energy-efficient server clusters[C]. Proceedings ofthe2ndWorkshop on Power-Aware Computing Systems,2002,179-197.
[36] D. Meisner, B. T. Gold, T. F. Wenisch. PowerNap: eliminating server idle power[C].Proceedings of the14thInternational Conference on Architectural Support for ProgrammingLanguages and Operating Systems (ASPLOS’09),2009,205-216.
[37] J. Stoess, C. Lang, F. Bellosa. Energy Management for Hypervisor-based virtual machines[C].Proceedings of the USENIX Annual Technical Conference (ATC’07),2007,1-14.
[38] K. H. Kim, A. Beloglazov, R. Buyya. Power-aware Provisioning of Cloud Resources forReal-time Services[C]. Proceedings of the7thInternational Workshop on Middleware forGrids, Clouds and e-Science (MGC’09),2009,1-6.
[39] A. J. Younge, G. Laszewski, L. Wang, et al. Efficient Resource Management for CloudComputing Environments[C]. International Conference on Green Computing,2010,357-364.
[40] D. Kusic, J. O. Kephart, J. E. Hanson, et al. Power and performance management ofvirtualized computing environments via lookahead control[J]. Cluster Computing,2009,12(1):1-15.
[41] A. Verma, P. Ahuja, A. Neogi. pMapper: power and migration cost aware applicationplacement in virtualized systems[C]. Lecture Notes in Computer Science (Middleware’08),2008,243-264.
[42] S. Srikantaiah, A. Kansal, F. Zhao. Energy aware consolidation for cloud computing[C].Proceedings of the2008conference on power aware computing and systems,2008.
[43] A. Beloglazov, R. Buyya. Energy efficient allocation of virtual machines in cloud datacenters[C]. Proceedings of the2010IEEE/ACM International Conference on Cluster, Cloudand Grid Computing,2010,577-578.
[44] A. Berl, E. Gelenbe, M. Di Girolamo, et al. Energy-Efficient Cloud Computing[J]. TheComputer Journal,2009,53(7):1045-1051.
[45] S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, et al. DRPM: dynamic speed control forpower management in server class disk[C]. Proceedings of the30thannual internationalsymposium on Computer architecture (ISCA’03),2003,169-181.
[46] D. Colarelli, D. Grunwald. Massive arrays of idle disks for storage archives[C]. Proceedingsof the2002ACM/IEEE conference on Supercomputing (SC’02),2002,1-11.
[47] E. Pinheiro, R. Bianchini. Energy conservation techniques for disk array-based servers[C].Proceedings of the18th annual conference on Supercomputing (ICS’04),2004,68-78.
[48] C. Weddle, M. Oldham, J. Qian, et al. PARAID: The gear-shifting power-aware RAID[J].ACM Trans. on Storage,2007,3(13):245-260.
[49] J. Kim, D. Rotem.Using Replication for Energy Conservation in RAID Systems[C].Proceedings of the Parallel and Distributed Processing Techniques and ApplicationsConference (PDPTA’10),2010.
[50] J. Chou, J. Kim, D. Rotem. Energy-Aware Scheduling in Disk Storage Systems[C].Proceedings of the31stInternational Conference on Distributed Computing Systems(ICDCS’11),2011,423-433.
[51] Q. Zhu, Z. Chen, L. Tan, et al. Hibernator: Helping Disk Arrays Sleep through the winter[C].Proceedings of the20thACM symposium on Operating Systems Principles (SOSP’05),2005,177-190.
[52] EMC. EMC Symmetrix3000and5000Enterprise Storage Systems product description guide.http://www.emc.com/products/productpdfs/pdg/symm_3_5_pdg.pdf.
[53] Q. Zhu, A. Shankar, Y. Zhou. PB-LRU: A SelfTuning Power Aware Storage CacheReplacement Algorithm for Conserving Disk Energy[C]. Proceedings of the18th annualinternational conference on Supercomputing (ICS’04),2004,79-88.
[54] Q. Zhu, Y. Zhou. Power-aware storage cache management[J]. IEEE Trans. on Computers,2005,54(5):587–602.
[55] D. Narayanan, A. Donnelly, A. Rowstron. Write off-loading: Practical power management forenterprise storage[J]. ACM Trans. on Storage,2008,4(3):1-23.
[56] A. Greenberg, J. Hamilton, D. A. Maltz, et al. The cost of a cloud: Research problems in datacenter networks[J]. ACM SIGCOMM Computer Communication Review,2009,68-73.
[57] A. Greenberg, J. R. Hamilton, N. Jain, et al. VL2: a scalable and flexible data centernetwork[C]. Proceedings of the ACM SIGCOMM2009conference on Data communication(SIGCOMM’09),2009,51-62.
[58] C. Guo, G. Lu, D. Li, et al. BCube: a high performance, server-centric network architecturefor modular data centers[C]. Proceedings of the ACM SIGCOMM2009conference on Datacommunication (SIGCOMM’09),2009,63-74.
[59] C. Guo, H. Wu, K. Tan, et al. Dcell: a scalable and fault-tolerant network structure for datacenters[C]. Proceedings of the ACM SIGCOMM2008conference on Data communication(SIGCOMM’08),2008,75-86.
[60]刘晓茜.云计算数据中心结构及其调度机制研究[D].合肥：中国科学技术大学,2011.
[61] M. Gupta, S. Singh. Greening of the Internet[C]. Proceedings of the ACM SIGCOMM2003conference on Data communication (SIGCOMM’03),2003,19-26.
[62] M. Gupta, S. Singh. Using low-power modes for energy conservation in Ethernet LANs[C].Proceedings of IEEE INFOCOM’07,2007,2451-2455.
[63] M. Gupta, S. Singh. Dynamic Ethernet link shutdown for energy conservation on Ethernetlinks[C]. Proceedings of the IEEE International Conference on Communications (ICC’07),2007,6156-6161.
[64] D. Kliazovich, P. Bouvry, S. U. Khan. DENS: Data Center Energy-Efficient Network-AwareScheduling[C]. Proceedings of the ACM/IEEE International Conference on GreenComputing and Communications,2010,69-75.
[65]敬思远,佘堃.基于混合粒子群算法的虚拟数据中心能耗优化[J].计算机工程,2012,38(15):276-278.
[66] C. Gunaratne, K. Christensen, B. Nordman, et al. Reducing the energy consumption ofEthernet with adaptive link rate (ALR)[J]. IEEE Trans. on Computers,2008,57(4):448-461.
[67] IEEE802.3Standard[S]. http://standards.ieee.org/getieee802/802.3.html,2006.
[68] P. Mahadevan, P. Sharma, S. Banerjee, et al. Energy aware network operations[C].Proceedings of IEEE INFOCOM’09, Rio de Janeiro,2009,1-6.
[69] T. Brunschwiler, B. Smith, E. Ruetsche, et al. Toward zero-emission data centers throughdirect reuse of thermal energy[J]. IBM Journal of Research and Development,2009,53(3):11.1-11.12.
[70] H. F. Hamann, T. G. Kessel, M. Iyengar, et al. Uncovering energy efficiency opportunities indata centers[J]. IBM Journal of Research and Development,2009,53(3):10.1-10.12.
[71] J. Moore, J. Chase, P. Ranganathan. Weatherman: Automated, online, and predictive thermalmapping and management for data center[C]. Proceedings of the International Conference onAutonomic Computing (ICAC’06),2006,155-164.
[72] J. Moore, J. Chase, P. Ranganathan. Making scheduling cool: Temperature-aware workloadplacement in data centers[C]. Proceedings of the annual conference on USENIX AnnualTechnical Conference (ATC’05),2005.
[73] Q. Tang, S. Kumar, S. Gupta. Energy-Efficient Thermal-Aware Task Scheduling forHomogeneous High-Performance Computing Data Center: A Cyber-Physical Approach[J].IEEE Trans. on Parallel and Distribution Systems,2008,19(11):1458-1472.
[74] E. Pakbaznia, M. Ghasemazar, M. Pedram. Temperature-aware dynamic resourceprovisioning in a power-optimized datacenter[C]. Proceedings of the Conference on Design,Automation and Test in Europe (DATE’10), Germany,2010,124-129.
[75] F. Ahmad, T. N. Vijaykumar. Joint optimization of idle and cooling power in data centerswhile maintaining response time[C]. Proceedings of the15thinternational conference onArchitectural support for programming languages and operating systems (ASPLOS’10),2010,243-256.
[76] S. Y. Jing, S. Ali, K. She, et al. State-of-the-art research study for green cloud computing[J].Journal of Supercomputing,2011.
[77] Y. Aijiro, A. Tanaka. A Combinational Optimization Algorithm for Server Consolidation [C].Proceedings of the21stAnnual Conference of Japanese Society for Artificial Intelligence,2007.
[78] S. Agrawal, S. K. Bose, S. Sundarrajan. Grouping genetic algorithm for solving the serverconsolidation problem with conflicts [C]. Proceedings of the first ACM/SIGEVO Summit onGenetic and Evolutionary Computation, Shanghai, China,2009,1-8.
[79] S. Y. Jing, K. She. A Genetic Approach to Achieve Optimal Server Consolidation by UsingExisting Servers in Virtualized Data Center[J]. World Academy of Science, Engineering andTechnology,2011,51(1):1177-1183.
[80]张伟,宋莹,阮利,等.面向Internet数据中心的资源管理[J].软件学报,2012,23(2):179-199.
[81] X. Y. Zhu，D.Young，B. J. Watson，et al.1000islands: an integrated approach to resourcemanagement for virtualized data centers[J]. Cluster Computing,2009,12(1):45-57.
[82] VMWare. Resource management with VMware DRS.2006.
[83] D. Nurmi, R. Wolski, C. Grzegorczyk. The Eucalyptus Open-source Cloud-computingSystem[C]. Proceedings of the9th IEEE/ACM International Symposium on ClusterComputing and the Grid(CCGRID '09),2009,124-131.
[84] T. Wood, P. Shenoy, A. Venkataramani. Sandpiper: Black-box and gray-box resourcemanagement for virtual machines[C]. migration. Proceedings of the4thUSENIX symposiumon Networked Systems Design and Implementation(NSDI’07), Cambridge,2007,229-242.
[85] J. Xu, M. Zhao, J. Fortes, et al. On the use offuzzy modeling in virtualized data centermanagement[C]. Proceedings of the4thInternational conference on autonomiccomputing(ICAC’07),2007,25-38.
[86] N. Huber, F. brosig, S. Kounev. Model-based Self-Adaptive Resource Allocation inVirtualized Environments[C]. Proceedings of the6thInternational Symposium on SoftwareEngineering for Adaptive and Self-Managing Systems,2011,90-99.
[87] I. Subramanian, C. McCarthy, M. Murphy. Meeting Performance Goals with the HP-UXWorkload Manager[C]. Proceedings of the1stConference on Industrial Experiences withSystems Software,2000,10-19.
[88] P. Padala, K. G. Shin, X. Zhu, et al. Adaptive control of virtualized resources in utilitycomputing environments[C]. Proceedings of the2ndACM SIGOPS/EuroSys EuropeanConference on Computer Systems,2007,289-302.
[89] P. Padala, K. Y. Hou, K. G. Shin et al. Automated control of multiple virtualized resources[C].Proceedings of the4thACM European conference on Computer systems,2009,13-26.
[90] Z. K. Wang, Y. Chen, D. Gmach, et al. Appraise: Application-Level performancemanagement in virtualized server environments[J]. IEEE Trans. on Networking and ServiceManagement,2009,6(4):240—254.
[91] X. Y. Wang, Z. H. Du, Y. N. Chen et al. Virtualization-Based autonomic resourcemanagement for multi-tier Web applications in shared data center[J]. The Journal of Systemsand Software,2008,8l(9):1591-1608.
[92] A. Beloglazov, J. Abawajy, R. Buyya. Energy-aware resource allocation heuristics forefficient management of data centers for cloud computing[J]. Future Generation ComputerScience,2012,28(5):755-768.
[93] L. Zhang, D. Ardagna. SLA based profit optimization in autonomic computing systems[C].Proceedings of the2ndinternational conference on Service oriented computing,2004,173-182.
[94] D. Ardagna, B. Panicucci, M. Trubian, et al. Energy-Aware Autonomic Resource Allocationin Multi-tier Virtualized Environments[J]. IEEE Trans. on Service Computing,2012,5(1):2-19.
[95] M. N. Bennani, D. A. Menasc. Resource allocation for autonomic data centers using analyticperformance models[C]. Proceedings of the2ndInternational Conference on AutonomicComputing (ICAC’05),2005.
[96] G. Tesauro, N. K. Jong, R. Das, et al. A Hybrid Reinforcement Learning Approach toAutonomic Resource Allocation[C]. Proceedings of the IEEE International Conference onAutonomic Computing (ICAC’06),2006,13-16.
[97] DMTF.开放虚拟格式标准[S]. http://www.dmtf.org/standards/ovf,2009.
[98] Z. Pawlak. Rough sets[J]. International Journal of Computer and information Science,1982,11(5):341-356.
[99]刘清,孙辉,王洪发.粒计算研究现状及基于Rough逻辑语义的粒计算研究[J].计算机学报,2008,31(4):543-555.
[100]刘清. Rough集和Rough推理[M].北京：科学出版社,2005.
[101]刘清,刘群.粒及粒计算在逻辑推理中的应用[J].计算机研究与发展,2004,41(4):546-551.
[102]刘清,刘少辉,郑非. Rough逻辑及其在数据挖掘中的应用[J].软件学报,2001,12(3):415-419.
[103]陈波,周明天.粒度粗糙理论研究[J].软件学报,2008,19(3):565583.
[104] A. Spellmann, K. Erickson, J. Reynolds. Server consolidation using performance modeling[J].IT Professional,2003,5(5):31-36.
[105] K. Parent. Server Consolidation Improves IT’s Capacity Utilization. Court Square Data Group,2005.
[106] C. Chekuri, S. Khanna. On Multi-Dimensional Packing Problems[C]. Proceedings of the10thAnnual ACM-SIAM Symposium on Discrete Algorithms,1999,185-194.
[107] M. R. Garey, D. S. Johnson. Computer and Interactability: A Guide to the Theory ofNP-Completeness [M]. W.H. Freeman and Company,1979.
[108]陈宝林.最优化理论与算法[M].北京：清华大学出版社,2005.
[109]汪定伟,王俊伟,王洪峰,等.智能优化方法[M].北京：高等教育出版社,2007.
[110] E. Falkekenauer. Genetic Algorithm and Grouping Problems[M]. John Wiley&Sons Ltd.,1998.
[111] E. Falkenauer. A Hybrid Group Genetic Algorithm for Bin Packing[J]. Journal of Heuristic,2004,2(1):5-30.
[112] S. Y. Jing, S. Ali, K. She. Minimization of VM Placement Change in Energy-Aware ResourceProvisioning for Cloud Data Center.(Accepted by IWMME2012)
[113] Libvirt. http://libvirt.org/,2012.
[114] X. Liu, X. Zhu, S. Singhal, et al. Adaptive entitlement control of resource containers onshared servers[C]. Proceedings of the9th IFIP/IEEE International Symposium on IntegratedNetwork Management,2005,163-175.
[115] C. Isci, G. Contreras, M. Martonosi. Live, runtime phase monitoring and prediction on realsystems with application to dynamic power management[C]. Proceedings of the39thAnnualIEEE/ACM International Symposium on Microarchitecture, Orlando USA,2006.
[116] E. Kalyvianaki, T. Charalambous, S. Hand. Self-Adaptive and Self-Configured CPU ResourceProvisioning for Virtualized Servers Using Kalman Filters[C]. Proceedings of the6thinternational conference on Autonomic computing(ICAC’06), Barcelona Spain,2009.
[117] J. Almeida, V. Almeida, D. Ardagna, et al. Joint admission control and resource allocation invirtualized servers[J]. Journal of Parallel and Distributed Computing,2010,70(4):344-362.
[118] C. P. Sapuntzakis, R. Chandra, B. Pfaff, et al. Optimizing the migration of virtualcomputers[C]. Proceedings of the5thsymposium on Operating systems design andimplementation (OSDI’02),2002,377-390.
[119]孙昱.虚拟机Xen及其实时迁移技术研究[D].上海：上海交通大学,2008.
[120] L. Lefèvre, A. Orgerie. Designing and evaluating an energy efficient Cloud[J]. Journal ofSupercomupting,2010,51(3):352-373.
[121] W. Voorsluys, J. Broberg, S. Venugopal, et al. Cost of virtual machine live migration in cloud:A performance evaluation[C]. Lecture Notes in Computer Science,2009,245-265.
[122] J. L. Hellerstein, Y. Diao, S. Parekh, and D. M. Tilbury. Feedback Control of ComputingSystems[M]. IEEE Press/Wiley Interscience,2004.
[123] X. Fan, W. D. Weber, L. A. Barroso. Power provisioning for a warehouse-sized computer[C].Proceedings of the34thannual international symposium on Computer architecture,2007,13-23,.
[124] M. Yue. A simple proof of the inequality FFD (L)<11/9OPT(L)+1, for all l for the FFDbin-packing algorithm[J]. Acta Mathematicae Applicatae Sinica,1991,7(4):321-331.
[125] C. Tang, M. Steinder, M. Spreitzer, et al. A scalable application placement controller forenterprise data centers. Proceedings of the16thinternational conference on World WideWeb(WWW’07),2007,331-340.
[126] A. Karve, T. Kimbrel, G. Pacici, et al. Dynamic Application Placement for Clustered WebApplications[C]. Proceedings of the15thinternational conference on World WideWeb(WWW’06),2006,595-604.
[127] B. Urgaonkar, A. L. Rosenberg, P. Shenoy, et al. Application Placement on a Cluster ofServers[J]. International Journal of Foundations of Computer Science,2007,18(5):1023-1041.
[128] Y. C. Lee, A. Y. Zomaya. Energy efficient utilization of resources in cloud computingsystems[J]. Journal of Supercomputing,2012,60(2):268-280.
[129]林闯,田源,姚敏.绿色网络和绿色评价：节能机制、模型和评价[J].计算机学报,2011,34(4):593-612.
[130] D. S. Liu, K. C. Tan, S. Y. Huang, et al. On solving multiobjective bin packing problemsusing evolutionary particle swarm optimization[J]. European Journal of Operational Research,2008,190(2):357-382.
[131] R. N. Calheiros, R. Ranjan, A. Beloglazov, et al. CloudSim: a toolkit for modeling andsimulation of cloud computing environments and evaluation of resource provisioningalgorithms[J]. Journal of Software: Practice and Experience,2011,41(1):23-50.
[132] C. L. HWang, K. Yoon. Multiple Attributes Decision Making Methods and Applications[M].Berlin: Springer,1981.
[133] P. Padmanabhan, G. S. Kang, Real-time dynamic voltage scaling for low-power embeddedoperating systems [C]. Proceedings of the18th ACM symposium on operating systemsprinciples(SOSP’01),2001,89-102.
[134]韩建军,刘同涛,李庆华,等.硬实时系统中基于任务同步及节能的动态调度算法[J],通信学报,2009,30(11):15-26.
[135]王颖锋,刘志镜.一种变电压多核处理器上的有效节能方法[J],计算机科学,2010,37(9):294-296.
[136]张冬松,陈芳园,金士尧.多核系统中基于动态电压频率调节的实时节能调度研究[J].计算机工程与科学,2010,32(9):157-164.
[137] R. Davis, A. Burns. A Survey of Hard Real-Time Scheduling for Multiprocessor Systems[J].ACM Computing Surveys,2010,43(4):1-41.
[138] C. M. Krishna, G. S. Kang. Real-time Systems [M]. McGraw-Hill,1997:67-72.
[139] X. Y. Zhu, Z. Wang, S. Singhal. Utility-driven workload management using nested controldesign[C]. Proceedings of the American Control Conference (ACC’06),2006.
[140] S. Y. Jing, K. She. A Novel Model for Load Balancing in Cloud Data Center. Journal ofConvergence Information Technology,2011,6(4):171-179.
[141] S. Y. Jing, K. She. A Rough Sets Approach to User Preference Modeling. Proceedings of5thInternational Conference on Rough Set and Knowledge Technology,2010.
[2] G. Schulz (著).绿色虚拟数据中心[M].韩毅刚,李亚娜,王欢(译).北京：人民邮电出版社,2010,50-88.
[3] Wikipedia.虚拟化的定义. http://en.wikipedia.org/wiki/Virtualization
[4]王庆波,金涬,何乐等著.虚拟化与云计算[M].北京：电子工业出版社,2009.
[5] P. Barham, B. Dragovic, K. Fraser, et al. Xen and the art of virtualization[C]. Proceedings ofthe19th ACM Symposium on Operating Systems Principles (SOSP’03),2003,164-177.
[6] R. Hiremane. Intel Virtualization Technology for Directed I/O (Intel VT-d)[J]. Intel Magazine,2007,4(10):179-192.
[7] VMWare.AMD64Virtualization Codenamed “Pacifica” Technology. VMWare,2005.
[8]刘鹏.云计算(第二版)[M].北京：电子工业出版社.2011,1-15.
[9] P. Mell, T. Grance. The NIST definition of cloud computing[R]. USA: NIST,2011.
[10] M. Armbrust, A. Fox, R. Griffith, et al. Above the Clouds: A Berkeley View of CloudComputing[J]. Communications of the ACM,2010,53(4):50-58.
[11] Gartner. Meeting the DC Power and Cooling Challenges.2008.
[12] EPA. Report to congress on server and data center energy efficiency[R]. U.S. EnvironmentalProtection Agency,2007.
[13] TGG. The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE.2007.
[14] J. Sweeney, J. Bradfield. Reducing data centers power and energy consumption: savingmoney and go green.2008.
[15] W. Tschudi, T. Xu, D. Sartor, et al. Energy efficient data center[R]. USA：Berkeley,2004.
[16] J. Hamilton. Cooperative Expendable Micro-Slice Servers (CEMS): Low Cost, Low PowerServers for Internet-Scale Services[C]. Proceedings of the4thBiennial Conference onInnovative Data Systems Research (CIDR’09), Asilomar,2009,1-8.
[17] S.-M. Kang, Y. Leblebici. CMOS Digital Integrated Circuits-Analysis and Design[M].北京：清华大学出版社,2002.
[18] L. Benini, A. Bogliolo, G. D. Micheli. A Survey of Design Techniques for System-LevelDynamic Power Management[J]. IEEE Trans. on VLSI,2000,8(3):299-316.
[19] Intel. Wireless Intel SpeedStep Power Manager: optimizing power consumption for the IntelPXA27x processor family.2004.
[20] Tom Farre, Bulkeley. Energy-Efficient Servers:ABright Spot in the Cloud.AMD,2009.
[21] Intel White Paper. Why the Intel Xeon Processor5500Series is the Ideal Foundation forCloud Computing.2009.
[22] Hewlett-Packard, Intel, Microsoft, et al. Advanced configuration and power interfacespecification[S].2011.
[23] F. Yao, A. Demers, S. Shenker. A scheduling model for reduced CPU energy[C]. Proceedingsof the36thAnnual Symposium on Foundations of Computer Science (FOCS’95),1995:374-382.
[24] P. Padmanabhan, G. S. Kang. Real-time dynamic voltage scaling for low-power embeddedoperating systems[C]. Proceedings of the18thACM Symposium on Operating SystemsPrinciples (SOSP’03),2001,89-102.
[25] S. Martin, K. Flautne, T. Mudge, et al. Combined dynamic voltage scaling and adaptive bodybiasing for lower power microprocessors under dynamic workloads[C]. Proceedings ofInternational Conference on Computer-Aided Design (ICCAD’02),2002,721–725.
[26] R. Jejurikar, C. Pereira, R. K. Gupta. Leakage aware dynamic voltage scaling for real-timeembedded systems[C]. Proceedings of the41stDesign Automation Conference (DAC’04),2004,275-280.
[27] L. Yan, J. Luo, N. K. Jha. Joint dynamic voltage scaling and adaptive body biasing forheterogeneous distributed real-time embedded systems[J]. IEEE Trans. on CAD of IntegratedCircuits and Systems,2005,24(7):1030-1041.
[28] H. Aydin, Y. Qi. Energy-Aware Partitioning for Multiprocessor Real-Time Systems[C].Proceedings of the17thInternational Parallel and Distributed Processing Symposium(IPDPS’03),2003,113b.
[29] D. Zhu, R. Melhem, B. R. Childers. Scheduling with Dynamic Voltage/Speed AdjustmentUsing Slack Reclamation in Multiprocessor Real Time Systems[J]. IEEE Trans. on Paralleland Distributed Systems,2003,14(7):686-699.
[30]钟虓,齐勇,侯迪,等.基于DVS的多核实时系统节能调度[J].电子学报,2006,34(12A):2481-2484.
[31]敬思远,佘堃,钟毅.用于多核嵌入式环境的硬实时任务感功调度算法[J].计算机应用,2011,31(11):2936-2939.
[32] Accenture. Data Centre Energy Forecast Report[R]. EPA,2008.
[33] E. Pinheiro, R. Bianchini, E. V. Carrera, et al. Load Balancing and Unbalancing for Powerand Performance in Cluster-Based Systems[C]. Proceedings of the International Workshopon Compilers and Operating Systems for Low Power,2001.
[34] J. S. Chase, D. C. Anderson, P. N. Thakar, et al. Managing energy and server resources inhosting centers[C]. Proceedings of the18thACM symposium on Operating systemsprinciples (SOSP’01),2001,103-116.
[35] E. N. Elnozahy, M. Kistler, R. Rajamony. Energy-efficient server clusters[C]. Proceedings ofthe2ndWorkshop on Power-Aware Computing Systems,2002,179-197.
[36] D. Meisner, B. T. Gold, T. F. Wenisch. PowerNap: eliminating server idle power[C].Proceedings of the14thInternational Conference on Architectural Support for ProgrammingLanguages and Operating Systems (ASPLOS’09),2009,205-216.
[37] J. Stoess, C. Lang, F. Bellosa. Energy Management for Hypervisor-based virtual machines[C].Proceedings of the USENIX Annual Technical Conference (ATC’07),2007,1-14.
[38] K. H. Kim, A. Beloglazov, R. Buyya. Power-aware Provisioning of Cloud Resources forReal-time Services[C]. Proceedings of the7thInternational Workshop on Middleware forGrids, Clouds and e-Science (MGC’09),2009,1-6.
[39] A. J. Younge, G. Laszewski, L. Wang, et al. Efficient Resource Management for CloudComputing Environments[C]. International Conference on Green Computing,2010,357-364.
[40] D. Kusic, J. O. Kephart, J. E. Hanson, et al. Power and performance management ofvirtualized computing environments via lookahead control[J]. Cluster Computing,2009,12(1):1-15.
[41] A. Verma, P. Ahuja, A. Neogi. pMapper: power and migration cost aware applicationplacement in virtualized systems[C]. Lecture Notes in Computer Science (Middleware’08),2008,243-264.
[42] S. Srikantaiah, A. Kansal, F. Zhao. Energy aware consolidation for cloud computing[C].Proceedings of the2008conference on power aware computing and systems,2008.
[43] A. Beloglazov, R. Buyya. Energy efficient allocation of virtual machines in cloud datacenters[C]. Proceedings of the2010IEEE/ACM International Conference on Cluster, Cloudand Grid Computing,2010,577-578.
[44] A. Berl, E. Gelenbe, M. Di Girolamo, et al. Energy-Efficient Cloud Computing[J]. TheComputer Journal,2009,53(7):1045-1051.
[45] S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, et al. DRPM: dynamic speed control forpower management in server class disk[C]. Proceedings of the30thannual internationalsymposium on Computer architecture (ISCA’03),2003,169-181.
[46] D. Colarelli, D. Grunwald. Massive arrays of idle disks for storage archives[C]. Proceedingsof the2002ACM/IEEE conference on Supercomputing (SC’02),2002,1-11.
[47] E. Pinheiro, R. Bianchini. Energy conservation techniques for disk array-based servers[C].Proceedings of the18th annual conference on Supercomputing (ICS’04),2004,68-78.
[48] C. Weddle, M. Oldham, J. Qian, et al. PARAID: The gear-shifting power-aware RAID[J].ACM Trans. on Storage,2007,3(13):245-260.
[49] J. Kim, D. Rotem.Using Replication for Energy Conservation in RAID Systems[C].Proceedings of the Parallel and Distributed Processing Techniques and ApplicationsConference (PDPTA’10),2010.
[50] J. Chou, J. Kim, D. Rotem. Energy-Aware Scheduling in Disk Storage Systems[C].Proceedings of the31stInternational Conference on Distributed Computing Systems(ICDCS’11),2011,423-433.
[51] Q. Zhu, Z. Chen, L. Tan, et al. Hibernator: Helping Disk Arrays Sleep through the winter[C].Proceedings of the20thACM symposium on Operating Systems Principles (SOSP’05),2005,177-190.
[52] EMC. EMC Symmetrix3000and5000Enterprise Storage Systems product description guide.http://www.emc.com/products/productpdfs/pdg/symm_3_5_pdg.pdf.
[53] Q. Zhu, A. Shankar, Y. Zhou. PB-LRU: A SelfTuning Power Aware Storage CacheReplacement Algorithm for Conserving Disk Energy[C]. Proceedings of the18th annualinternational conference on Supercomputing (ICS’04),2004,79-88.
[54] Q. Zhu, Y. Zhou. Power-aware storage cache management[J]. IEEE Trans. on Computers,2005,54(5):587–602.
[55] D. Narayanan, A. Donnelly, A. Rowstron. Write off-loading: Practical power management forenterprise storage[J]. ACM Trans. on Storage,2008,4(3):1-23.
[56] A. Greenberg, J. Hamilton, D. A. Maltz, et al. The cost of a cloud: Research problems in datacenter networks[J]. ACM SIGCOMM Computer Communication Review,2009,68-73.
[57] A. Greenberg, J. R. Hamilton, N. Jain, et al. VL2: a scalable and flexible data centernetwork[C]. Proceedings of the ACM SIGCOMM2009conference on Data communication(SIGCOMM’09),2009,51-62.
[58] C. Guo, G. Lu, D. Li, et al. BCube: a high performance, server-centric network architecturefor modular data centers[C]. Proceedings of the ACM SIGCOMM2009conference on Datacommunication (SIGCOMM’09),2009,63-74.
[59] C. Guo, H. Wu, K. Tan, et al. Dcell: a scalable and fault-tolerant network structure for datacenters[C]. Proceedings of the ACM SIGCOMM2008conference on Data communication(SIGCOMM’08),2008,75-86.
[60]刘晓茜.云计算数据中心结构及其调度机制研究[D].合肥：中国科学技术大学,2011.
[61] M. Gupta, S. Singh. Greening of the Internet[C]. Proceedings of the ACM SIGCOMM2003conference on Data communication (SIGCOMM’03),2003,19-26.
[62] M. Gupta, S. Singh. Using low-power modes for energy conservation in Ethernet LANs[C].Proceedings of IEEE INFOCOM’07,2007,2451-2455.
[63] M. Gupta, S. Singh. Dynamic Ethernet link shutdown for energy conservation on Ethernetlinks[C]. Proceedings of the IEEE International Conference on Communications (ICC’07),2007,6156-6161.
[64] D. Kliazovich, P. Bouvry, S. U. Khan. DENS: Data Center Energy-Efficient Network-AwareScheduling[C]. Proceedings of the ACM/IEEE International Conference on GreenComputing and Communications,2010,69-75.
[65]敬思远,佘堃.基于混合粒子群算法的虚拟数据中心能耗优化[J].计算机工程,2012,38(15):276-278.
[66] C. Gunaratne, K. Christensen, B. Nordman, et al. Reducing the energy consumption ofEthernet with adaptive link rate (ALR)[J]. IEEE Trans. on Computers,2008,57(4):448-461.
[67] IEEE802.3Standard[S]. http://standards.ieee.org/getieee802/802.3.html,2006.
[68] P. Mahadevan, P. Sharma, S. Banerjee, et al. Energy aware network operations[C].Proceedings of IEEE INFOCOM’09, Rio de Janeiro,2009,1-6.
[69] T. Brunschwiler, B. Smith, E. Ruetsche, et al. Toward zero-emission data centers throughdirect reuse of thermal energy[J]. IBM Journal of Research and Development,2009,53(3):11.1-11.12.
[70] H. F. Hamann, T. G. Kessel, M. Iyengar, et al. Uncovering energy efficiency opportunities indata centers[J]. IBM Journal of Research and Development,2009,53(3):10.1-10.12.
[71] J. Moore, J. Chase, P. Ranganathan. Weatherman: Automated, online, and predictive thermalmapping and management for data center[C]. Proceedings of the International Conference onAutonomic Computing (ICAC’06),2006,155-164.
[72] J. Moore, J. Chase, P. Ranganathan. Making scheduling cool: Temperature-aware workloadplacement in data centers[C]. Proceedings of the annual conference on USENIX AnnualTechnical Conference (ATC’05),2005.
[73] Q. Tang, S. Kumar, S. Gupta. Energy-Efficient Thermal-Aware Task Scheduling forHomogeneous High-Performance Computing Data Center: A Cyber-Physical Approach[J].IEEE Trans. on Parallel and Distribution Systems,2008,19(11):1458-1472.
[74] E. Pakbaznia, M. Ghasemazar, M. Pedram. Temperature-aware dynamic resourceprovisioning in a power-optimized datacenter[C]. Proceedings of the Conference on Design,Automation and Test in Europe (DATE’10), Germany,2010,124-129.
[75] F. Ahmad, T. N. Vijaykumar. Joint optimization of idle and cooling power in data centerswhile maintaining response time[C]. Proceedings of the15thinternational conference onArchitectural support for programming languages and operating systems (ASPLOS’10),2010,243-256.
[76] S. Y. Jing, S. Ali, K. She, et al. State-of-the-art research study for green cloud computing[J].Journal of Supercomputing,2011.
[77] Y. Aijiro, A. Tanaka. A Combinational Optimization Algorithm for Server Consolidation [C].Proceedings of the21stAnnual Conference of Japanese Society for Artificial Intelligence,2007.
[78] S. Agrawal, S. K. Bose, S. Sundarrajan. Grouping genetic algorithm for solving the serverconsolidation problem with conflicts [C]. Proceedings of the first ACM/SIGEVO Summit onGenetic and Evolutionary Computation, Shanghai, China,2009,1-8.
[79] S. Y. Jing, K. She. A Genetic Approach to Achieve Optimal Server Consolidation by UsingExisting Servers in Virtualized Data Center[J]. World Academy of Science, Engineering andTechnology,2011,51(1):1177-1183.
[80]张伟,宋莹,阮利,等.面向Internet数据中心的资源管理[J].软件学报,2012,23(2):179-199.
[81] X. Y. Zhu，D.Young，B. J. Watson，et al.1000islands: an integrated approach to resourcemanagement for virtualized data centers[J]. Cluster Computing,2009,12(1):45-57.
[82] VMWare. Resource management with VMware DRS.2006.
[83] D. Nurmi, R. Wolski, C. Grzegorczyk. The Eucalyptus Open-source Cloud-computingSystem[C]. Proceedings of the9th IEEE/ACM International Symposium on ClusterComputing and the Grid(CCGRID '09),2009,124-131.
[84] T. Wood, P. Shenoy, A. Venkataramani. Sandpiper: Black-box and gray-box resourcemanagement for virtual machines[C]. migration. Proceedings of the4thUSENIX symposiumon Networked Systems Design and Implementation(NSDI’07), Cambridge,2007,229-242.
[85] J. Xu, M. Zhao, J. Fortes, et al. On the use offuzzy modeling in virtualized data centermanagement[C]. Proceedings of the4thInternational conference on autonomiccomputing(ICAC’07),2007,25-38.
[86] N. Huber, F. brosig, S. Kounev. Model-based Self-Adaptive Resource Allocation inVirtualized Environments[C]. Proceedings of the6thInternational Symposium on SoftwareEngineering for Adaptive and Self-Managing Systems,2011,90-99.
[87] I. Subramanian, C. McCarthy, M. Murphy. Meeting Performance Goals with the HP-UXWorkload Manager[C]. Proceedings of the1stConference on Industrial Experiences withSystems Software,2000,10-19.
[88] P. Padala, K. G. Shin, X. Zhu, et al. Adaptive control of virtualized resources in utilitycomputing environments[C]. Proceedings of the2ndACM SIGOPS/EuroSys EuropeanConference on Computer Systems,2007,289-302.
[89] P. Padala, K. Y. Hou, K. G. Shin et al. Automated control of multiple virtualized resources[C].Proceedings of the4thACM European conference on Computer systems,2009,13-26.
[90] Z. K. Wang, Y. Chen, D. Gmach, et al. Appraise: Application-Level performancemanagement in virtualized server environments[J]. IEEE Trans. on Networking and ServiceManagement,2009,6(4):240—254.
[91] X. Y. Wang, Z. H. Du, Y. N. Chen et al. Virtualization-Based autonomic resourcemanagement for multi-tier Web applications in shared data center[J]. The Journal of Systemsand Software,2008,8l(9):1591-1608.
[92] A. Beloglazov, J. Abawajy, R. Buyya. Energy-aware resource allocation heuristics forefficient management of data centers for cloud computing[J]. Future Generation ComputerScience,2012,28(5):755-768.
[93] L. Zhang, D. Ardagna. SLA based profit optimization in autonomic computing systems[C].Proceedings of the2ndinternational conference on Service oriented computing,2004,173-182.
[94] D. Ardagna, B. Panicucci, M. Trubian, et al. Energy-Aware Autonomic Resource Allocationin Multi-tier Virtualized Environments[J]. IEEE Trans. on Service Computing,2012,5(1):2-19.
[95] M. N. Bennani, D. A. Menasc. Resource allocation for autonomic data centers using analyticperformance models[C]. Proceedings of the2ndInternational Conference on AutonomicComputing (ICAC’05),2005.
[96] G. Tesauro, N. K. Jong, R. Das, et al. A Hybrid Reinforcement Learning Approach toAutonomic Resource Allocation[C]. Proceedings of the IEEE International Conference onAutonomic Computing (ICAC’06),2006,13-16.
[97] DMTF.开放虚拟格式标准[S]. http://www.dmtf.org/standards/ovf,2009.
[98] Z. Pawlak. Rough sets[J]. International Journal of Computer and information Science,1982,11(5):341-356.
[99]刘清,孙辉,王洪发.粒计算研究现状及基于Rough逻辑语义的粒计算研究[J].计算机学报,2008,31(4):543-555.
[100]刘清. Rough集和Rough推理[M].北京：科学出版社,2005.
[101]刘清,刘群.粒及粒计算在逻辑推理中的应用[J].计算机研究与发展,2004,41(4):546-551.
[102]刘清,刘少辉,郑非. Rough逻辑及其在数据挖掘中的应用[J].软件学报,2001,12(3):415-419.
[103]陈波,周明天.粒度粗糙理论研究[J].软件学报,2008,19(3):565583.
[104] A. Spellmann, K. Erickson, J. Reynolds. Server consolidation using performance modeling[J].IT Professional,2003,5(5):31-36.
[105] K. Parent. Server Consolidation Improves IT’s Capacity Utilization. Court Square Data Group,2005.
[106] C. Chekuri, S. Khanna. On Multi-Dimensional Packing Problems[C]. Proceedings of the10thAnnual ACM-SIAM Symposium on Discrete Algorithms,1999,185-194.
[107] M. R. Garey, D. S. Johnson. Computer and Interactability: A Guide to the Theory ofNP-Completeness [M]. W.H. Freeman and Company,1979.
[108]陈宝林.最优化理论与算法[M].北京：清华大学出版社,2005.
[109]汪定伟,王俊伟,王洪峰,等.智能优化方法[M].北京：高等教育出版社,2007.
[110] E. Falkekenauer. Genetic Algorithm and Grouping Problems[M]. John Wiley&Sons Ltd.,1998.
[111] E. Falkenauer. A Hybrid Group Genetic Algorithm for Bin Packing[J]. Journal of Heuristic,2004,2(1):5-30.
[112] S. Y. Jing, S. Ali, K. She. Minimization of VM Placement Change in Energy-Aware ResourceProvisioning for Cloud Data Center.(Accepted by IWMME2012)
[113] Libvirt. http://libvirt.org/,2012.
[114] X. Liu, X. Zhu, S. Singhal, et al. Adaptive entitlement control of resource containers onshared servers[C]. Proceedings of the9th IFIP/IEEE International Symposium on IntegratedNetwork Management,2005,163-175.
[115] C. Isci, G. Contreras, M. Martonosi. Live, runtime phase monitoring and prediction on realsystems with application to dynamic power management[C]. Proceedings of the39thAnnualIEEE/ACM International Symposium on Microarchitecture, Orlando USA,2006.
[116] E. Kalyvianaki, T. Charalambous, S. Hand. Self-Adaptive and Self-Configured CPU ResourceProvisioning for Virtualized Servers Using Kalman Filters[C]. Proceedings of the6thinternational conference on Autonomic computing(ICAC’06), Barcelona Spain,2009.
[117] J. Almeida, V. Almeida, D. Ardagna, et al. Joint admission control and resource allocation invirtualized servers[J]. Journal of Parallel and Distributed Computing,2010,70(4):344-362.
[118] C. P. Sapuntzakis, R. Chandra, B. Pfaff, et al. Optimizing the migration of virtualcomputers[C]. Proceedings of the5thsymposium on Operating systems design andimplementation (OSDI’02),2002,377-390.
[119]孙昱.虚拟机Xen及其实时迁移技术研究[D].上海：上海交通大学,2008.
[120] L. Lefèvre, A. Orgerie. Designing and evaluating an energy efficient Cloud[J]. Journal ofSupercomupting,2010,51(3):352-373.
[121] W. Voorsluys, J. Broberg, S. Venugopal, et al. Cost of virtual machine live migration in cloud:A performance evaluation[C]. Lecture Notes in Computer Science,2009,245-265.
[122] J. L. Hellerstein, Y. Diao, S. Parekh, and D. M. Tilbury. Feedback Control of ComputingSystems[M]. IEEE Press/Wiley Interscience,2004.
[123] X. Fan, W. D. Weber, L. A. Barroso. Power provisioning for a warehouse-sized computer[C].Proceedings of the34thannual international symposium on Computer architecture,2007,13-23,.
[124] M. Yue. A simple proof of the inequality FFD (L)<11/9OPT(L)+1, for all l for the FFDbin-packing algorithm[J]. Acta Mathematicae Applicatae Sinica,1991,7(4):321-331.
[125] C. Tang, M. Steinder, M. Spreitzer, et al. A scalable application placement controller forenterprise data centers. Proceedings of the16thinternational conference on World WideWeb(WWW’07),2007,331-340.
[126] A. Karve, T. Kimbrel, G. Pacici, et al. Dynamic Application Placement for Clustered WebApplications[C]. Proceedings of the15thinternational conference on World WideWeb(WWW’06),2006,595-604.
[127] B. Urgaonkar, A. L. Rosenberg, P. Shenoy, et al. Application Placement on a Cluster ofServers[J]. International Journal of Foundations of Computer Science,2007,18(5):1023-1041.
[128] Y. C. Lee, A. Y. Zomaya. Energy efficient utilization of resources in cloud computingsystems[J]. Journal of Supercomputing,2012,60(2):268-280.
[129]林闯,田源,姚敏.绿色网络和绿色评价：节能机制、模型和评价[J].计算机学报,2011,34(4):593-612.
[130] D. S. Liu, K. C. Tan, S. Y. Huang, et al. On solving multiobjective bin packing problemsusing evolutionary particle swarm optimization[J]. European Journal of Operational Research,2008,190(2):357-382.
[131] R. N. Calheiros, R. Ranjan, A. Beloglazov, et al. CloudSim: a toolkit for modeling andsimulation of cloud computing environments and evaluation of resource provisioningalgorithms[J]. Journal of Software: Practice and Experience,2011,41(1):23-50.
[132] C. L. HWang, K. Yoon. Multiple Attributes Decision Making Methods and Applications[M].Berlin: Springer,1981.
[133] P. Padmanabhan, G. S. Kang, Real-time dynamic voltage scaling for low-power embeddedoperating systems [C]. Proceedings of the18th ACM symposium on operating systemsprinciples(SOSP’01),2001,89-102.
[134]韩建军,刘同涛,李庆华,等.硬实时系统中基于任务同步及节能的动态调度算法[J],通信学报,2009,30(11):15-26.
[135]王颖锋,刘志镜.一种变电压多核处理器上的有效节能方法[J],计算机科学,2010,37(9):294-296.
[136]张冬松,陈芳园,金士尧.多核系统中基于动态电压频率调节的实时节能调度研究[J].计算机工程与科学,2010,32(9):157-164.
[137] R. Davis, A. Burns. A Survey of Hard Real-Time Scheduling for Multiprocessor Systems[J].ACM Computing Surveys,2010,43(4):1-41.
[138] C. M. Krishna, G. S. Kang. Real-time Systems [M]. McGraw-Hill,1997:67-72.
[139] X. Y. Zhu, Z. Wang, S. Singhal. Utility-driven workload management using nested controldesign[C]. Proceedings of the American Control Conference (ACC’06),2006.
[140] S. Y. Jing, K. She. A Novel Model for Load Balancing in Cloud Data Center. Journal ofConvergence Information Technology,2011,6(4):171-179.
[141] S. Y. Jing, K. She. A Rough Sets Approach to User Preference Modeling. Proceedings of5thInternational Conference on Rough Set and Knowledge Technology,2010.
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