大规模网络服务系统能耗控制与性能优化
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摘要
由于人们对网络服务的需求急剧增长,网络服务提供系统的数量和规模出现爆炸式增长,同样网络服务系统的耗电量也在快速增加。网络服务系统耗电量的增加,不仅提高了系统运行代价,而且制约着系统性能的提升,同时还会产生大量的碳排放。无论从环境角度还是经济角度,网络服务系统的能耗控制日益成为系统设计的重要因素。在所有网络服务中,多媒体应用占据了网络流量的统治地位,而且还在迅速增加,因此多媒体集群系统的能耗控制和性能优化具有重要的学术和实际应用价值。
本文针对大规模多媒体服务系统的能耗控制和性能优化进行研究,主要围绕多媒体集群和多媒体网关,利用成比例计算(proportional computing)思想,在实际工程背景下,通过建立描述系统的动态演化的数学模型,在模型下推导系统最优控制策略以实现资源与负载的自适应匹配。本文的分析思路和数学模型,具有较好的一般性和实用价值,能够应用到其它网络服务。这些创新工作可以简要概括如下：
针对、oD(video on demand)服务集群的能耗控制,本文在系统结构上提出使用分层优化机制进行能耗控制和性能优化,在集群端优化集群系统资源的单位收益率,在客户端利用客户端自适应技术补偿由于集群端调节带来的性能降低。在集群端使用自底向上的思路进行分析,并提出了一种两层Markov切换状态空间控制过程模型来描述集群系统分层动态特性。该模型将系统分为两个层级,下层为QoS层,优化目标为提高系统的QoS,上层为能量层,优化目标为降低系统能耗。两层结构总的优化目标为性能/能耗比的最大化。本文提出了一种递阶协同优化算法来求解最优控制策略。在客户端本文研究自适应播放控制技术,来对抗网络抖动或者服务器端资源受限可能带来的缓冲区下溢。针对该问题首先提出了一种统计模型来估计缓冲区的下溢时间,然后在下溢时间估计基础上,提出了一种基于双门限的帧率调节算法,其中双门限会根据网络状态和缓存状态动态调节从而实现速率调节的及时性。算法在调节帧率时,考虑播放帧的运动强度,从而提供更好的用户体验(QoE)。
针对目前流行的时延电视集群系统能耗控制问题,首先建立了具有能耗控制功能的时延电视集群系统结构,它能够动态监测系统资源利用率等状态信息,并能通过能耗控制器执行控制策略,动态调整集群系统可用资源数量。在该结构下,本文分别考虑使用平稳确定和随机性策略对系统优化。当使用平稳确定性策略时,本文将集群系统动态配置问题建模成Markov切换状态控制过程模型,通过该模型来优化集群服务器的动态开启和关闭。结合性能势理论和性能势的在线估计,提出了集群动态能耗控制的在线优化算法。该算法通过样本轨道求解最优策略,不需要系统参数信息。当使用平稳随机性策略时,将节目和连接数分等级,实现状态空间的大幅度下降。随机模型同样采用Markov切换状态空间控制过程模型,系统的能耗控制被建模为一个带约束的随机优化问题,优化目标为在满足阻塞率约束下,最小化系统能耗。结合Lagrange乘子法和性能梯度的在线估计,提出了一种策略迭代算法求解最优控制策略,并在理论上证明了算法的收敛性。
考虑多媒体服务提供时网络层的能耗控制,研究利用自适应链路速率调节技术,通过控制多媒体网关的带宽达到能耗控制的目的。首先对多媒体网络流量进行分析,包括并发流量数、连接持续时间、多媒体数据特性等；然后引入MMPP模型对多媒体数据流进行建模,在该模型基础上,本文将多媒体网关能耗控制建模为一个带约束的半Markov决策过程。推导了基于嵌入Markov链的性能灵敏度公式,并在样本轨道上通过再生周期估计性能梯度,最后提出在线性能优化算法。相比于传统的线性规划和基于拟无穷小矩阵的性能优化算法,本文优化算法中的样本轨道可以直接通过半Markov核生成,相当于直接利用半Markov核进行性能优化,避免基于拟无穷小矩阵优化的缺点。
As the demand on internet services dramatically increases, the number and scale of internet service provide systems have been skyrocketing, as is the electricity con-sumption. Higher electricity consumption not only results in boosted operation costs, but also restricts the system performance improvement and produces more carbon e-mission. There is no doubt that energy consumption of network service systems is a pressing factor in system design both from an economic and environmental point of view. Of all the internet services, multimedia application has been a dominant source of internet traffic and still keeps rapid growth. Hence, power management and per-formance optimization for multimedia service system have important academic and practical significance.
This work focuses on the power control and performance optimization for large s-cale multimedia service systems, including multimedia service clusters and multimedia gateways. Using the proportional computing idea, this dissertation gives the mathemat-ical models to describe system dynamic evolution with the consideration of practical engineering background, and derives the optimal control policy to achieve the adap-tion between system resource and actual workload. The analytical approach and model possess generality and could be applied to other internet services. The main work is as follows:
For the power control problem of video on demand (VoD) server clusters, a hier-archical optimization mechanism for system structure is adopted to achieve power con-trol and performance optimization, i.e. increasing the profit rate per system resource in cluster and applying client adaptation technology to compensate performance degrada-tion due to cluster adjustment in client. In cluster side, we use a bottom-up approach to analyze the system evolution and propose a two-level Markov switching state-space control processes model to describe the hierarchical dynamic structure of the cluster. This model has two levels, the lower level is named as QoS level corresponding to in-crease the QoS and the upper level is named as power level corresponding to decrease the system power consumption. The optimization object is to maximize the ratio of performance to power consumption. Then we propose a hierarchical cooperation algo-rithm to search the optimal policy. In client side, we employ adaptive palyout control technology to combat cache outage caused by network jitter or limited cluster resource. Firstly, an statistical model is established to estimate the underflow time of the cache. Based on the estimation, an adaptive playout algorithm with dual thresholds to adjust the frame rate is presented. This approach adjusts the threshold dynamically accord-ing to the status of both network and cache, and thus regulates the frame rate timely. Moreover, the algorithm possesses scene aware characteristic, since it adjusts the play-out rate according to the motion intensity of the playout scenes to reduce the underflow probability of cache and provide better quality of experience (QoE).
For the power control problem of popular time shifted TV cluster system, we set up a time shifted TV cluster architecture with power management function. The core function of this architecture is to monitor system status of the utilization of system re-source, etc., and execute the control policy to adjust the number of system available resource dynamically by power controller. Based on this system structure, we con-sider system optimization with stationary determined policy and stationary stochastic policy respectively. When the stationary determined policy is adopted, the system re-configuration problem is modeled as a Markov switching state space control process to dynamically powering on or off server optimally. Based on the theory and online estimation approach of performance potential, an online adaptive policy iteration algo-rithm is presented. The algorithm solves the optimal policy based on a sample path and the solving procedure does not depend on any prior knowledge of system parameters. When the stationary stochastic policy is adopted, we firstly group the system resource to form different configuration schemes, and quantize the connection number and channel number to different levels. In this way, the dimension of state space is reduced largely. Then the same control model is adopted to describe the system evolution and the prob-lem of power conservation is posed as a constrained stochastic optimization problem with the goal of minimizing the average power consumption subject to the constraint on the average blocking ratio. Applying lagrange approach and online estimation of the performance gradient, a policy iteration algorithm is proposed to search the optimal policy online. The convergence of the algorithm is theoretically analyzed.
For the power control problem of multimedia gateway, we employ adaptive link rate technology to adjust the bandwidth of gateway so as to reduce the power con-sumption. Firstly, we analyzed the multimedia network traffic, including concurrent connection number, sojourn time and the characteristics of multimedia data. Then the Markov Modulated Poisson Process (MMPP) model is introduced to model the mul-timedia traffic and the power control of multimedia gateway is modeled as a semi-Markov decision process. Then we derive the performance sensitivity formulas based on embedded Markov chain. And the performance gradient is estimated through re-generative cycles on the sample path. The online optimization algorithm is presented in the final. Unlike the conventional optimization algorithm to search optimal policy, such as linear programming or infinitesimal generator based performance optimization, the sample path needed in the algorithm can be generated by semi-Markov kennel ma-trix directly, hence this optimization mode is equivalent to search the optimal based on semi-Markov kernel matrix, avoiding the drawback of infinitesimal generator based optimization algorithm.
本文针对大规模多媒体服务系统的能耗控制和性能优化进行研究,主要围绕多媒体集群和多媒体网关,利用成比例计算(proportional computing)思想,在实际工程背景下,通过建立描述系统的动态演化的数学模型,在模型下推导系统最优控制策略以实现资源与负载的自适应匹配。本文的分析思路和数学模型,具有较好的一般性和实用价值,能够应用到其它网络服务。这些创新工作可以简要概括如下：
针对、oD(video on demand)服务集群的能耗控制,本文在系统结构上提出使用分层优化机制进行能耗控制和性能优化,在集群端优化集群系统资源的单位收益率,在客户端利用客户端自适应技术补偿由于集群端调节带来的性能降低。在集群端使用自底向上的思路进行分析,并提出了一种两层Markov切换状态空间控制过程模型来描述集群系统分层动态特性。该模型将系统分为两个层级,下层为QoS层,优化目标为提高系统的QoS,上层为能量层,优化目标为降低系统能耗。两层结构总的优化目标为性能/能耗比的最大化。本文提出了一种递阶协同优化算法来求解最优控制策略。在客户端本文研究自适应播放控制技术,来对抗网络抖动或者服务器端资源受限可能带来的缓冲区下溢。针对该问题首先提出了一种统计模型来估计缓冲区的下溢时间,然后在下溢时间估计基础上,提出了一种基于双门限的帧率调节算法,其中双门限会根据网络状态和缓存状态动态调节从而实现速率调节的及时性。算法在调节帧率时,考虑播放帧的运动强度,从而提供更好的用户体验(QoE)。
针对目前流行的时延电视集群系统能耗控制问题,首先建立了具有能耗控制功能的时延电视集群系统结构,它能够动态监测系统资源利用率等状态信息,并能通过能耗控制器执行控制策略,动态调整集群系统可用资源数量。在该结构下,本文分别考虑使用平稳确定和随机性策略对系统优化。当使用平稳确定性策略时,本文将集群系统动态配置问题建模成Markov切换状态控制过程模型,通过该模型来优化集群服务器的动态开启和关闭。结合性能势理论和性能势的在线估计,提出了集群动态能耗控制的在线优化算法。该算法通过样本轨道求解最优策略,不需要系统参数信息。当使用平稳随机性策略时,将节目和连接数分等级,实现状态空间的大幅度下降。随机模型同样采用Markov切换状态空间控制过程模型,系统的能耗控制被建模为一个带约束的随机优化问题,优化目标为在满足阻塞率约束下,最小化系统能耗。结合Lagrange乘子法和性能梯度的在线估计,提出了一种策略迭代算法求解最优控制策略,并在理论上证明了算法的收敛性。
考虑多媒体服务提供时网络层的能耗控制,研究利用自适应链路速率调节技术,通过控制多媒体网关的带宽达到能耗控制的目的。首先对多媒体网络流量进行分析,包括并发流量数、连接持续时间、多媒体数据特性等；然后引入MMPP模型对多媒体数据流进行建模,在该模型基础上,本文将多媒体网关能耗控制建模为一个带约束的半Markov决策过程。推导了基于嵌入Markov链的性能灵敏度公式,并在样本轨道上通过再生周期估计性能梯度,最后提出在线性能优化算法。相比于传统的线性规划和基于拟无穷小矩阵的性能优化算法,本文优化算法中的样本轨道可以直接通过半Markov核生成,相当于直接利用半Markov核进行性能优化,避免基于拟无穷小矩阵优化的缺点。
As the demand on internet services dramatically increases, the number and scale of internet service provide systems have been skyrocketing, as is the electricity con-sumption. Higher electricity consumption not only results in boosted operation costs, but also restricts the system performance improvement and produces more carbon e-mission. There is no doubt that energy consumption of network service systems is a pressing factor in system design both from an economic and environmental point of view. Of all the internet services, multimedia application has been a dominant source of internet traffic and still keeps rapid growth. Hence, power management and per-formance optimization for multimedia service system have important academic and practical significance.
This work focuses on the power control and performance optimization for large s-cale multimedia service systems, including multimedia service clusters and multimedia gateways. Using the proportional computing idea, this dissertation gives the mathemat-ical models to describe system dynamic evolution with the consideration of practical engineering background, and derives the optimal control policy to achieve the adap-tion between system resource and actual workload. The analytical approach and model possess generality and could be applied to other internet services. The main work is as follows:
For the power control problem of video on demand (VoD) server clusters, a hier-archical optimization mechanism for system structure is adopted to achieve power con-trol and performance optimization, i.e. increasing the profit rate per system resource in cluster and applying client adaptation technology to compensate performance degrada-tion due to cluster adjustment in client. In cluster side, we use a bottom-up approach to analyze the system evolution and propose a two-level Markov switching state-space control processes model to describe the hierarchical dynamic structure of the cluster. This model has two levels, the lower level is named as QoS level corresponding to in-crease the QoS and the upper level is named as power level corresponding to decrease the system power consumption. The optimization object is to maximize the ratio of performance to power consumption. Then we propose a hierarchical cooperation algo-rithm to search the optimal policy. In client side, we employ adaptive palyout control technology to combat cache outage caused by network jitter or limited cluster resource. Firstly, an statistical model is established to estimate the underflow time of the cache. Based on the estimation, an adaptive playout algorithm with dual thresholds to adjust the frame rate is presented. This approach adjusts the threshold dynamically accord-ing to the status of both network and cache, and thus regulates the frame rate timely. Moreover, the algorithm possesses scene aware characteristic, since it adjusts the play-out rate according to the motion intensity of the playout scenes to reduce the underflow probability of cache and provide better quality of experience (QoE).
For the power control problem of popular time shifted TV cluster system, we set up a time shifted TV cluster architecture with power management function. The core function of this architecture is to monitor system status of the utilization of system re-source, etc., and execute the control policy to adjust the number of system available resource dynamically by power controller. Based on this system structure, we con-sider system optimization with stationary determined policy and stationary stochastic policy respectively. When the stationary determined policy is adopted, the system re-configuration problem is modeled as a Markov switching state space control process to dynamically powering on or off server optimally. Based on the theory and online estimation approach of performance potential, an online adaptive policy iteration algo-rithm is presented. The algorithm solves the optimal policy based on a sample path and the solving procedure does not depend on any prior knowledge of system parameters. When the stationary stochastic policy is adopted, we firstly group the system resource to form different configuration schemes, and quantize the connection number and channel number to different levels. In this way, the dimension of state space is reduced largely. Then the same control model is adopted to describe the system evolution and the prob-lem of power conservation is posed as a constrained stochastic optimization problem with the goal of minimizing the average power consumption subject to the constraint on the average blocking ratio. Applying lagrange approach and online estimation of the performance gradient, a policy iteration algorithm is proposed to search the optimal policy online. The convergence of the algorithm is theoretically analyzed.
For the power control problem of multimedia gateway, we employ adaptive link rate technology to adjust the bandwidth of gateway so as to reduce the power con-sumption. Firstly, we analyzed the multimedia network traffic, including concurrent connection number, sojourn time and the characteristics of multimedia data. Then the Markov Modulated Poisson Process (MMPP) model is introduced to model the mul-timedia traffic and the power control of multimedia gateway is modeled as a semi-Markov decision process. Then we derive the performance sensitivity formulas based on embedded Markov chain. And the performance gradient is estimated through re-generative cycles on the sample path. The online optimization algorithm is presented in the final. Unlike the conventional optimization algorithm to search optimal policy, such as linear programming or infinitesimal generator based performance optimization, the sample path needed in the algorithm can be generated by semi-Markov kennel ma-trix directly, hence this optimization mode is equivalent to search the optimal based on semi-Markov kernel matrix, avoiding the drawback of infinitesimal generator based optimization algorithm.
引文
Data Center Knowledge. http://www.datacenterknowledge.com/archives/2011/08/01/report-google-uses-about-900000-servers.
中国IDC圈. http://www.idcquan.com/special/2011baogao/.
Dennis Bouley. Apr.2011. Estimating a Data Center's Electrical Carbon Footprint [R].
Global Action Play.2007. An inefficient truth, http://www.globalaction.org.uk/, Global Action Plan Rep. [R].
J. F. Kurose and K. W. Ross. May 2004. Computer Networking:A Top-Down Ap-proach Featuring the Internet [M]. Addison Weslye; Third edition.
Cisco. Mar.2010. Cisco Data Center Infrastructure 2.5 Design Guide [M]. Cisco Press.
D. Klizaovich, P. bouvry, and S. U. Khan.2012 DENS:data center energy efficient network aware scheduling [J]. Cluster Computing, pp.1-11.
P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan. May 2009. A power bench-marking framework for network devices [C]. IEEE INFOCOM Workshops, pp.1-6.
D. Thaler, and C. Hopps. Nov.2000. Multipath issues in unicast and multicast nexthop selection [S]. Internet Engineering Task Force Request for Comments 2991.
IEEE std 802.3ba-2010. Jun.2010. Media access control parameters, physical layers and management parameters for 40Gb/s and 100Gb/s operation [S].
C. Guo, H. Wu, K. Tan, L. Shiy, Y. Zhang, and S. Luz.2008. DCell:a scalable and fault-tolerant network structure for data centers [C]. ACM SIGCOMM, Seattle, Washing-ton, USA.
C. Guo, H. Wu, K. Tan, L. Shiy, Y. Zhang, and S. Luz.2009. BCube:a high per-formance, server-centric network architecture for modular data centers [C]. ACM SIGCOMM, Barcelona, Spain.
CNNIC. Jan.2012. http://www.cnnic.net.cn/dtygg/dtgg/201201/W020120116337628870651.pdf.
IETF RFC 2326. Real Time Streaming Protocol (RTSP) [S]. http://www.ietf.org.
IETF RFC 1889. Real-time Transport Protocol (RTSP) [S]. http://www.ietf.org.
IETF RFC 4961. Real-time Transport Control Protocol (RTCP) [S]. http://www.ietf.org.
3GPP SA4, S4-090230. HTTP Streaming[S]. http://www.3gpp.org.
Adobe Systems.2009. Inc. Real Time Messaging Protocol Tunnelled [S]. http://www.adobe.com.
W3C. www.w3c.org/TR/html5 [S].
Canada's Advanced Research and Innovation Network (CANARIE). http://canarie.ca.
R. H. Kata. Feb.2009. Tech titans building boom [J]. IEEE Spectrum, vol.46, pp. 40-54.
A. P. Bianzino, C. Chaudet, D. Rossi, J. L. Rougier. First Quarter,2012. A survey of green networking research [J]. IEEE Communications Surveys & Tutorials, vol.14, no.1, pp.3-20.
A, Qureshi, R. Weber, H. Balakrishnan, J. Guttag and B. Maggs. Aug.2009. Cutting the Electric Bill for Internet-Scale Systems [C]. ACM SIGCOMM 2009.
J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and P. R. Doyle. Dec.2001. Managing energy and server resources in hosting centers [C]. ACM SIGOPS Oper-ating Systems Review, vol.35, pp.103-116.
K. Christensen, C. Gunaratne, B. Nordman, and A. D. George. Dec.2004 The next frontier for communication networks:power management [J]. Compuer Communi-cations, vol.27, pp.1758-1770.
M. Allman, K. Christensen, B. Nordman, and V. Paxson. Nov.2007. Enableing an energy-efficient future Internet through selectively connected end systems [C].6th HotNets-Ⅵ.
S. Nanda and T. C. Chiueh. Feb.2008. A Survey on Virtualization Technologies [R]. Tech. Rep. TR179, Department of Computer Science, SUNY at Stony Brook.
N. M. Kabir and R. Boutaba. Oct.2008 A Survey of Network Virtualization [R]. Tech. Rep. CS-2008-25, University of Waterloo.
L. A. Barroso and U. Holzle. Dec.2007 The Case for Energy-Proportional Computing [J]. IEEE Computer, vol.40, pp.33-37.
M. Weiser, B. Welch, A. Demers, and S. Shenker.1984. Scheduling for Reduced CPU Energy [C]. OSDI 1984.
J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang, and S. Wright. Apr.2008. Power Awareness in Network Design and Routing [C]. INFOCOM 2008.
K. W. Roth, F. Goldstein, and J. Kleinman. Jan.2002. Energy Consumption by Of-fice and Telecommunications Equipment in Commercial Buildings Volume Ⅰ:Energy Consumption Baseline [R]. Tech. Rep. Vol Ⅰ, National Technical Information Service (NTIS), US Department of Commerce.
B. Nordman and K. Christensen. Jul.2005. Reducing the Energy Consumption of Network Devices [S]. IEEE 802.3 Tutorial.
R. Bolla, R. Bruschi, K. Christensen, F. Cucchietti, F. Davoli, and S. Singh.2010. The Potential Impact of Green Technologies in Next Generation Wireline Networks-Is There Room for Energy Savings Optimization [J]. IEEE Commun. Mag..
IEEE P802.3az Energy Efficient Ethernet Task Force. Jul.2011. IEEE 802.3 Energy Efficient Ethernet Study Group [S]. Sep.21,2007. Retrieved Jul.5,2011.
H. Hlavacs, G. Da Costa, and J.-M. Pierson. Aug.2009. Energy Consumption of Resi-dential and Professional Switches [C]. IEEE ICCSE.
P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan. May 2009. A Power Benchmarking Framework for Network Devices [C]. Proc. IFIP Networking.
M. Gupta and S. Singh. Aug.2003. Greening of the Internet [C]. SIGCOMM 2003.
M. Gupta, S. Grover, and S. Singh. Oct.2004. A Feasibility Study for Power Manage-ment in LAN Switches [C]. ICNP 2004.
C. Gunaratne, K. Christensen, and B. Nordman. Sep.2005. Managing energy consump-tion costs in desktop PCs and LAN switches with proxying, split TCP connections and scaling of link speed [J]. International Journal of Network Management, vol.15, pp.297-310.
C. Gunaratne, K. Christensen, and S. W. Suen. Nov.2006. Ethernet Adaptive Link Rate (ALR):Analysis of a buffer threshold policy [C]. GLOBECOM 2006.
C. Gunaratne, K. Christensen, B. Nordman, and S. Suen. Apr.2008. Reducing the Energy Consumption of Ethernet with Adaptive Link Rate (ALR) [J]. IEEE Trans. Comp., vol.57, pp.448-461.
S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, and D. Wetherall. Apr.2008. Re-ducing Network Energy Consumption via Sleeping and Rate-Adaptation [C]. ND-SI2008.
S. Nedevschi, J. Chandrashekar, J. Liu, B. Nordman, S. Ratnasamy, and N. Taft. Apr. 2009. Skilled in the Art of Being Idle:Reducing Energy Waste in Networked Systems [C]. NSDI2009.
Y. Agarwal, S. Hodges, R. Chandra, J. Scott, P. Bahl, and R. Gupta. Apr.2009. Som-niloquy:Augmenting Network Interfaces to Reduce PC Energy Usage [C]. NSDI 2009.
M. Jimeno and K. Christensen. Oct.2007. A Prototype Power Management Proxy for Gnutella Peer-to-Peer File Sharing [C]. LCN 2007.
G. D. Costa, J. P. Gelas, Y. Georgiou, L. Lefevre, A. C. Orgerie, J. M. Pierson, O. Richard, and K. Sharma. May 2009. The GREEN-NET Framework:Energy Effi-ciency in Large Scale Distributed Systems [C]. HPPAC 2009.
M. Baldi and Y. Ofek. Jun.2009. Time for a "Greener" Internet [C]. GreenComm 2009.
B. Sanso and H. Mellah. Oct.2009. On Reliability, Performance and Internet Power Consumption [C]. DRCN 2009.
W. Fisher, M. Suchara, and J. Rexford. Aug.2010. Greening Backbone Networks: Reducing Energy Consumption by Shutting Off Cables in Bundled Links [C]. SIG-COMM 2010.
A. P. Bianzino, C. Chaudet, F. Larrocca, D. Rossi, and J. L. Rougier. Dec.2010. Energy-Aware Routing:a Reality Check [C]. GreenComm 2010.
J. Blackburn and K. Christensen. Oct.2008. Green Telnet:Modifying a Client-Server Application to Save Energy [J]. Dr. Dobb's Journal.
J. Blackburn and K. Christensen. Jun.2009. A Simulation Study of a New Green BitTorrent [C]. GreenComm 2009.
A. Kansal and F. Zhao. Sep.2008. Fine-grained Energy Profiling for Power-aware Application Design [J]. SIGMETRICS Performance Evaluation Review, vol.36, pp. 26-31.
W. Baek and T. Chilimbi. Jul.2009. Green:A System for Supporting Energy-Conscious Programming using Principled Approximation [R]. Tech. Rep. MSR-TR-2009-89, Microsoft Research.
L. Irish and K. J. Christensen. Apr.1998. A "Green TCP/IP" to Reduce Electricity Consumed by Computers [C]. Proc. IEEE Southeastern 1998 Engineering for a New Era, pp.302-305.
B. Wang and S. Singh. Mar.2004. Computational Energy Cost of TCP [C]. INFOCOM 2004.
L. Minas, and B. Ellison.2009. Energy Efficiency for Information Technology:How to Reduce Power Consumption in Servers and Data Centers [M]. Intel Press, USA.
X. Fan, W.D. Weber, and L.A. Barroso.2007. Power provisioning for a warehouse-sized computer [C]. ISCA 2007.
G. Dhiman, K. Mihic, and T. Rosing.2010. A system for online power prediction in virtualized environments using gaussian mixture models [C]. Proceedings of the 47th ACM/IEEE Design Automation Conference.
P. Ranganathan, P. Leech, D. Irwin, and J. Chase.2006. Ensemble-level power man-agement for dense blade servers [C]. ISCA 2006.
S. Devadas, S. Malik, S. Devadas, abd S. Malik.1995. A survey of optimization tech-niques targeting low power VLSI circuits [C]. Proceedings of the 32nd ACM/IEEE Conference on Design Automation,1995.
S. Albers.2010. Energy-efficient algorithms [J]. Commun. ACM, vol.53, no.5, pp. 86-96.
M. B. Srivastava, A. P. Chandrakasan, and R. W. Brodersen.1996. Predictive sys-tem shutdown and other architectural techniques for energy efficient programmable computation [J]. IEEE Trans. VLSI Syst., vol.4, no.1, pp.42-55.
C. H. Hwang, and A. C. Wu.2000. A predictive system shutdown method for energy saving of event-driven computation [J]. ACM Trans. Des. Autom. Electron. Syst., vol.5 no.2, pp.241.
F. Douglis, P. Krishnan, and B. Bershad.1995. Adaptive disk spin-down policies for mobile computers [J]. Comput. Syst., vol.8 no.4, pp.381-413.
G. Buttazzo.2000. Scalable applications for energy-aware processors [J]. Embedded Software, pp.153-165.
L. L. Andrew, M. Lin, and A. Wierman.2010. Optimality, fairness, and robustness in speed scaling designs [C]. SIGMETRICS 2010.
K. Flautner, S. Reinhardt, and T. Mudge, Automatic performance setting for dynamic voltage scaling [J]. Wireless Netw., vol.8, no.5, pp.507-520.
J. R. Lorch, A. J. Smith.2001. Improving dynamic voltage scaling algorithms with PACE [J]. ACM SIGMETRICS Perform. Eval. Rev., vol.29 no.1 pp.61.
V. Pallipadi, A. Starikovskiy.2006. The ondemand governor [C]. Proceedings of the Linux Symposium, vol.2.
R. Rajkumar, K. Juvva, A. Molano, and S. Oikawa.2001. Resource kernels:a resource-centric approach to real-time and multimedia systems [J]. Readings in Multimedia Computing and Networking, Morgan Kaufmann Publishers Inc.,2001, pp.476-490.
H. Zeng, C. S. Ellis, and A.R. Lebeck.2005. Experiences in managing energy with ecosystem [J]. IEEE Pervasive Comput., vol.4, no.1, pp.62-68.
R. Neugebauer, and D. McAuley.2001. Energy is just another resource:energy ac-counting and energy pricing in the nemesis OS [C]. Proceedings of the 8th IEEE Workshop on Hot Topics in Operating Systems, Elmau/Oberbayern, Germany, pp. 59-64.
V. Vardhan, D. G. Sachs, W. Yuan, A. F. Harris, S. V. Adve, and D.L. Jones.2005. In-tegrating finegrained application adaptation with global adaptation for saving energy [C]. International Workshop on Power-Aware Real-Time Computing, Jersey City, NJ.
J. Flinn, and M. Satyanarayanan.2004. Managing battery lifetime with energy-aware adaptation [J]. ACM Trans. Comput. Syst., vol.22 no.2, pp.179.
E. Elnozahy, M. Kistler, and R. Rajamony.2003. Energy-efficient server clusters [J]. Power Aware Comput. Syst., vol.2325, pp.179-197.
A. Gandhi, M. Harchol-Balter, R. Das, and C. Lefurgy.2009. Optimal power allocation in server farms [C]. Proceedings of the 11th International Joint Conference on Measurement and Modeling of Computer Systems. pp.157-168.
S. K. Garg, C. S. Yeo, A. Anandasivam, and R. Buyya.2010. Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers [J]. J. Parallel Distributed Comput..
R. Nathuji, and K. Schwan.2007. Virtualpower:coordinated power management in virtualized enterprise systems [J]. ACM SIGOPS Oper. Syst. Rev., vol.41, no.6, pp.265-278.
D. Kusic, J.O. Kephart, J.E. Hanson, N. Kandasamy, and G. Jiang.2009. Power and performance management of virtualized computing environments via lookahead control [J]. Cluster Comput., vol.12, no.1, pp.1-15.
M. Stillwell, D. Schanzenbach, F. Vivien, and H. Casanova.2009. Resource allocation using virtual clusters [C]. CCGrid 2009.
Y. Song, H. Wang, Y. Li, B. Feng, and Y. Sun.2009. Multi-Tiered On-Demand resource scheduling for VM-Based data center [C]. CCGrid 2009.
A. Verma, P. Ahuja, and A. Neogi.2008. pMapper:power and migration cost aware application placement in virtualized systems [C]. Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp.243-264.
D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper.2009. Resource pool management: reactive versus proactive or let's be friends [J]. Comput. Netw., vol.53, no.17, pp. 2905-2922.
R. Buyya, A. Beloglazov, and J. Abawajy.2010. Energy-efficient management of data center resources for cloud computing:a vision, architectural elements, and open challenges [C]. PDPTA 2010.
M. L. Puteman.1994. Markov decision processes:discrete stochastic dynamic pro-gramming [M]. New York:Wiley.
X. R. Cao.1998. The relation among potentials, perturbation analysis, Markov decision processes, and other topics [J]. Journal of Discrete Event Dynamic Systems, vol.8, pp.71-87.
X. R. Cao and X. P. Guo.2004. A unified approach to Markov decision problems and performance sensitivity analysis with discounted and average criteria:Multichain cases [J]. Automatica, vol.40, pp.1749-1759.
X. R. Cao.2003. From perturbation analysis to Markov decision processes and rein-forcement learning[J]. Discrete Event Dynamic Systems:Theory and Applications, vol.13, pp.9-39.
X. R. Cao and H. F. Chen.1997. Perturbation realization, potentials, and sensitivity analysis of Markov processes[J]. IEEE Transactions on Automation and Control, vol.42, no.10, pp.1382-1393.
X. R. Cao.2007. Stochastic learning and optimization:a sensitivity-based ap-proach [M]. New York:Springer.
P. Marbach and J. N. Tsitsiklis.2001. Simulation-based optimization of Markov reward processes[J]. IEEE Transactions on Automatic Control, vol.46, no.2, pp.191-209.
H. T. Fang and X. R. Cao.2004. Potential-Based On-line Policy Iteration Algorithms for Markov Decision Processes[J]. IEEE Transactions on Automatic Control, vol. 49, no.4, pp.493-505.
S. Boyd and L. Valldenberghe. Convex Optimization [M]. New York, NY, USA: Cambridge UniVersity Press,2004.
X. P. Guo. Jun.2007. Constrained otimization for average cost continuous-time Markov decision processes[J]. IEEE Trans. Automatic Control, vol.52, no.6, p-p.1139-1143.
F. J. Beutle and K, W. Ross.1985. Optimal policies for controlledmarkov chains with a constraint[J]. Journal of Mathematical Analysis and Application, vol.112, no.1, pp.236-252.
J. Forestier, and P. Varaiya.1978. Multilayer control of large Markovchains [J]. IEEE Transactions on Automatic Control, vol.23, no.2, pp.298-304.
X. R. Cao, Z. Y. Ren, S. Bhatnagar, M. Fu, and S. Marcus.2002. A time aggregation approach to Markov decision processes [J]. Automatica, vol.38, no.6, pp.929-943.
H. S. Chang, P. J. Fard, S. I. Marcus, and M. Shayman.2003. Multitimescale Markov decision processes [J]. IEEE Trans. on Automatic Control, vol.48, no.6, pp.976-987.
Y. Wan, and X. R. Cao. The control of a two-level Markov decision process by time aggregation [J]. Automatica, vol.42, no.3, pp.393-403.
A. Dua and N. Bambos.2007. Buffer management for wireless media streaming [C]. IEEE GLOBECOM 2007.
S. G. Deshpande.2008. High quality video streaming using content-aware adaptive frame scheduling with explicit deadline adjustment [C]. ACM Multimedia.2008.
T. Stockhammer, H. Jenkac, and G. Kuhn.2004. Streaming video over variable bit-rate wireless channels [J]. IEEE Trans. Multimedia, vol.6, no.2, pp.268-277.
M. Kalman, E. Steinbach, and B. Girod. Jun.2004. Adaptive media playout for low-delay video streaming over error-prone channels [J]. IEEE Trans. Circuits and Syst. Video Technol., vol.14, no.6, pp.841-851.
H. C. Chuan, C. Y. Huang, and T. Chiang. May 2005. A novel adaptive video playout control for video streaming over mobile cellular environment [C]. IEEE ISCAS.
D. Tao, H. Hoang, and J. Cai.2007. Optimal frame selection with adaptive playout for delivering stored video under constrained resources [C]. IEEE ICME.
H. C. Chuang, C. Y. Huang, and T. H. Chiang.2007. Content-aware adaptive media playout controls for wireless video streaming [J]. IEEE Trans. Multimedia, vol.9, no.6, pp.1273-1283.
Y. F. Su, Y. H. Yang, M. T. Lu, and H. H. Chen.2009. Smooth control of adaptive media playout for video streaming [J]. IEEE Trans. Multimedia, vol.11, no.7, pp. 1331-1339.
Y. Li, A. Markopoulou, J. Apostolopoulos, and N. Bambos.2008. Content-aware play-out and packet scheduling for video streaming over wireless links [J]. IEEE Trans. Multimedia, vol.10, no.5, pp.885-895.
P.A. Chou and M. Zhourong.2006. Rate-distortion optimized streaming of packetized media [J]. IEEE Trans. Multimedia, vol.8, no.2, pp.390-404.
C. H. Liang and C. L. Huang.2004. Content-based adaptive media player for network video [C]. IEEE ISCAS,2004.
H.264/AVC Reference Software Version:JM 13.0. http://iphome.hhi.de/suehring/tml/index.htm.
P. Seeling, M. Reisslein, and B. Kulapala.2004. Network performance evaluation with frame size and quality traces of single-layer and two-layer video:a tutorial [J]. IEEE Communications Surveys and Tutorials, vol.6, no.3, pp.58-78.
G. V. Auwera, P. T. David, and M. Reisslein.2008. Traffic and quality characterization of single-layer video streams encoded with H.264/MPEG-4 advanced video coding standard and scalable video coding extension [J]. IEEE Trans. Broadcasting, vol. 54, no.3, pp.698-718.
L. Benini, A. Bogliolo, and G. D. Micheli.2000. A survey of design techniques for system level dynamic power management [J]. IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol.8, no.3, pp.299-316.
G. Semeraro, G. Magklis, R. Balasubramonian, D. H. Albonesi, S. Dwarkadas, and M. L. Scott. Energy efficient processor design using multiple clock domains with dynamic voltage and frequency scaling [C]. HPCA'02.
E. Pinheiro, R. Bianchini, E. V. Carrera, and T. Heath.2003. Dynamic Cluster Re-configuration for Power and Performance [J]. Compliers and Operating Systems for Low Power, Kluwer Academic Publishers, pp.75-93.
J. Slegers, N. Thomas, and I. Mitrani.2008. Dynamic Server Allocation for Power and Performance [C]. Proc. of the SPEC International Performance Evaluation Workshop on Performance Evaluation:Metrics, Models and Benchmarks, pp.247-261.
J. Slegers, I. Mitrani, and Thomas N.2009. Static and dynamic server allocation in systems with ON/OFF sources [J]. Annals of Operations Research, vol.170, pp.251-263.
C. Santana, J. C. B. Leite, and D. Mosse.2010. Load forecasting applied to soft real-time web clusters [C]. Proc. of the ACM Symposium on Applied Computing, pp. 346-350.
Y. J. Hong, J. C. Xue, and M. Thottethodi. Dynamic Server Provisioning to Minimize Cost in an IaaS Cloud[C]. Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems.
X. R. Wang, and Y. F. Wang. Coordinating Power Control and Performance Man-agement for Virtualized Server Clusters[J]. IEEE Trans. Parallel and Distributed Systems, vol.22, no.2, pp.245-259.
H. L. Yu, D. D. Zheng, B. Y. Zhao, and W. M. Zheng.2006. Understanding User Behavior in Large-Scale Video-on-Demand Systems[C]. Proceedings of the ACM SIGOPS/EuroSys European Conference on Computer Systems.
H. J. Kushner, G. Yin.2003. Stochastic Approximation and Recursive Algorithms and Applications [M]. New York:Springer.
V. Djonin V, and V. Krishnamurthy. Q-learning Algorithms for Constrained Markov Decision Process with Randomized Monotone Policies:Application to MIMO Transmission Control[J]. IEEE Transactions on Signal Processing, vool.55, no. 5, pp.2170-2181.
D. Bertsekas.2000. Nonlinear programming [M]. Athena Scientific:Belmont. MA.
L. Ljung.1977. Analysis of recursive stochastic algorithms [J]. IEEE Trans. on Auto-matic Control, Vol.22, No.4, pp.551-575.
O. Tamm, C. Hermsmeyer, and A. M. Rush.2010. Eco-sustainable System and Net-work Architectures for Future Transport Networks[J]. Bell Labs Technical Journal, vol.14, pp.311-327.
P. Mahadevan, S. Banerjee, and P. Sharma. Energy Proportionality of an Enterprise Network[C]. Proc. of the first ACM SIGCOMM workshop on Green Networking 2010.
M. Zink, K. Suh, and Yu Gu.2008. Watch Global, Cache Local:YouTube Network Traces at a Campus Network-Measurements and Implications[C]. MMCN'08.
M. Krunz, R. Saas, and H. Hughes.1995. Statistical characteristics and multiplexing of MPEG streams [C]. IEEE INFOCOM'95.
F. Yu, V. Krishnamurthy, and V. C. M. Leung.2006. Cross layer optimal connection admission control for variable bit rate multimedia traffic in packet wireless CDMA networks [J]. IEEE Trans. Signal Processing, vol.54, no.2, pp.542-555.
P. Skelly, M. Schwartz, and S. Dixit.1993. A histogram-based model for video traffic behavior in an ATMmultiplexer [J]. IEEE/ACM Trans. Netw., vol.1, pp.446-459.
奚宏生.2009.随机过程引论[M].合肥：中国科学技术大学出版社.
胡奇英,刘建墉.2000.马尔可夫决策过程引论[M].西安：西安电子科技大学出版社.
唐吴.2002MarrkoV控制过程的优化理论和算法[D].博士.合肥：中国科学技术大学.
李衍杰.2006.扩展MarkoV决策过程的性能灵敏度分析与优化[D].博士.合肥：中国科学技术大学.
徐陈峰.2008.面向P2P的Markov模型[D].博士.合肥：中国科学技术大学.
江琦.2008.半Markov切换空间控制过程及其应用[D].博士.合肥：中国科学技术大学.
江琦,奚宏生,殷保群.2007.动态电源管理的随机切换模型与在线优化[J].自动化学报,vol.33,no.1,pp.66-71.
卓居超.2010.时移电视集群系统缓存调度研究[D].博士.合肥：中国科学技术大学.
许书彬.2009.时移电视点播系统交互技术研究[D].博士.合肥：中国科学技术大学.
中国IDC圈. http://www.idcquan.com/special/2011baogao/.
Dennis Bouley. Apr.2011. Estimating a Data Center's Electrical Carbon Footprint [R].
Global Action Play.2007. An inefficient truth, http://www.globalaction.org.uk/, Global Action Plan Rep. [R].
J. F. Kurose and K. W. Ross. May 2004. Computer Networking:A Top-Down Ap-proach Featuring the Internet [M]. Addison Weslye; Third edition.
Cisco. Mar.2010. Cisco Data Center Infrastructure 2.5 Design Guide [M]. Cisco Press.
D. Klizaovich, P. bouvry, and S. U. Khan.2012 DENS:data center energy efficient network aware scheduling [J]. Cluster Computing, pp.1-11.
P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan. May 2009. A power bench-marking framework for network devices [C]. IEEE INFOCOM Workshops, pp.1-6.
D. Thaler, and C. Hopps. Nov.2000. Multipath issues in unicast and multicast nexthop selection [S]. Internet Engineering Task Force Request for Comments 2991.
IEEE std 802.3ba-2010. Jun.2010. Media access control parameters, physical layers and management parameters for 40Gb/s and 100Gb/s operation [S].
C. Guo, H. Wu, K. Tan, L. Shiy, Y. Zhang, and S. Luz.2008. DCell:a scalable and fault-tolerant network structure for data centers [C]. ACM SIGCOMM, Seattle, Washing-ton, USA.
C. Guo, H. Wu, K. Tan, L. Shiy, Y. Zhang, and S. Luz.2009. BCube:a high per-formance, server-centric network architecture for modular data centers [C]. ACM SIGCOMM, Barcelona, Spain.
CNNIC. Jan.2012. http://www.cnnic.net.cn/dtygg/dtgg/201201/W020120116337628870651.pdf.
IETF RFC 2326. Real Time Streaming Protocol (RTSP) [S]. http://www.ietf.org.
IETF RFC 1889. Real-time Transport Protocol (RTSP) [S]. http://www.ietf.org.
IETF RFC 4961. Real-time Transport Control Protocol (RTCP) [S]. http://www.ietf.org.
3GPP SA4, S4-090230. HTTP Streaming[S]. http://www.3gpp.org.
Adobe Systems.2009. Inc. Real Time Messaging Protocol Tunnelled [S]. http://www.adobe.com.
W3C. www.w3c.org/TR/html5 [S].
Canada's Advanced Research and Innovation Network (CANARIE). http://canarie.ca.
R. H. Kata. Feb.2009. Tech titans building boom [J]. IEEE Spectrum, vol.46, pp. 40-54.
A. P. Bianzino, C. Chaudet, D. Rossi, J. L. Rougier. First Quarter,2012. A survey of green networking research [J]. IEEE Communications Surveys & Tutorials, vol.14, no.1, pp.3-20.
A, Qureshi, R. Weber, H. Balakrishnan, J. Guttag and B. Maggs. Aug.2009. Cutting the Electric Bill for Internet-Scale Systems [C]. ACM SIGCOMM 2009.
J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and P. R. Doyle. Dec.2001. Managing energy and server resources in hosting centers [C]. ACM SIGOPS Oper-ating Systems Review, vol.35, pp.103-116.
K. Christensen, C. Gunaratne, B. Nordman, and A. D. George. Dec.2004 The next frontier for communication networks:power management [J]. Compuer Communi-cations, vol.27, pp.1758-1770.
M. Allman, K. Christensen, B. Nordman, and V. Paxson. Nov.2007. Enableing an energy-efficient future Internet through selectively connected end systems [C].6th HotNets-Ⅵ.
S. Nanda and T. C. Chiueh. Feb.2008. A Survey on Virtualization Technologies [R]. Tech. Rep. TR179, Department of Computer Science, SUNY at Stony Brook.
N. M. Kabir and R. Boutaba. Oct.2008 A Survey of Network Virtualization [R]. Tech. Rep. CS-2008-25, University of Waterloo.
L. A. Barroso and U. Holzle. Dec.2007 The Case for Energy-Proportional Computing [J]. IEEE Computer, vol.40, pp.33-37.
M. Weiser, B. Welch, A. Demers, and S. Shenker.1984. Scheduling for Reduced CPU Energy [C]. OSDI 1984.
J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang, and S. Wright. Apr.2008. Power Awareness in Network Design and Routing [C]. INFOCOM 2008.
K. W. Roth, F. Goldstein, and J. Kleinman. Jan.2002. Energy Consumption by Of-fice and Telecommunications Equipment in Commercial Buildings Volume Ⅰ:Energy Consumption Baseline [R]. Tech. Rep. Vol Ⅰ, National Technical Information Service (NTIS), US Department of Commerce.
B. Nordman and K. Christensen. Jul.2005. Reducing the Energy Consumption of Network Devices [S]. IEEE 802.3 Tutorial.
R. Bolla, R. Bruschi, K. Christensen, F. Cucchietti, F. Davoli, and S. Singh.2010. The Potential Impact of Green Technologies in Next Generation Wireline Networks-Is There Room for Energy Savings Optimization [J]. IEEE Commun. Mag..
IEEE P802.3az Energy Efficient Ethernet Task Force. Jul.2011. IEEE 802.3 Energy Efficient Ethernet Study Group [S]. Sep.21,2007. Retrieved Jul.5,2011.
H. Hlavacs, G. Da Costa, and J.-M. Pierson. Aug.2009. Energy Consumption of Resi-dential and Professional Switches [C]. IEEE ICCSE.
P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan. May 2009. A Power Benchmarking Framework for Network Devices [C]. Proc. IFIP Networking.
M. Gupta and S. Singh. Aug.2003. Greening of the Internet [C]. SIGCOMM 2003.
M. Gupta, S. Grover, and S. Singh. Oct.2004. A Feasibility Study for Power Manage-ment in LAN Switches [C]. ICNP 2004.
C. Gunaratne, K. Christensen, and B. Nordman. Sep.2005. Managing energy consump-tion costs in desktop PCs and LAN switches with proxying, split TCP connections and scaling of link speed [J]. International Journal of Network Management, vol.15, pp.297-310.
C. Gunaratne, K. Christensen, and S. W. Suen. Nov.2006. Ethernet Adaptive Link Rate (ALR):Analysis of a buffer threshold policy [C]. GLOBECOM 2006.
C. Gunaratne, K. Christensen, B. Nordman, and S. Suen. Apr.2008. Reducing the Energy Consumption of Ethernet with Adaptive Link Rate (ALR) [J]. IEEE Trans. Comp., vol.57, pp.448-461.
S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, and D. Wetherall. Apr.2008. Re-ducing Network Energy Consumption via Sleeping and Rate-Adaptation [C]. ND-SI2008.
S. Nedevschi, J. Chandrashekar, J. Liu, B. Nordman, S. Ratnasamy, and N. Taft. Apr. 2009. Skilled in the Art of Being Idle:Reducing Energy Waste in Networked Systems [C]. NSDI2009.
Y. Agarwal, S. Hodges, R. Chandra, J. Scott, P. Bahl, and R. Gupta. Apr.2009. Som-niloquy:Augmenting Network Interfaces to Reduce PC Energy Usage [C]. NSDI 2009.
M. Jimeno and K. Christensen. Oct.2007. A Prototype Power Management Proxy for Gnutella Peer-to-Peer File Sharing [C]. LCN 2007.
G. D. Costa, J. P. Gelas, Y. Georgiou, L. Lefevre, A. C. Orgerie, J. M. Pierson, O. Richard, and K. Sharma. May 2009. The GREEN-NET Framework:Energy Effi-ciency in Large Scale Distributed Systems [C]. HPPAC 2009.
M. Baldi and Y. Ofek. Jun.2009. Time for a "Greener" Internet [C]. GreenComm 2009.
B. Sanso and H. Mellah. Oct.2009. On Reliability, Performance and Internet Power Consumption [C]. DRCN 2009.
W. Fisher, M. Suchara, and J. Rexford. Aug.2010. Greening Backbone Networks: Reducing Energy Consumption by Shutting Off Cables in Bundled Links [C]. SIG-COMM 2010.
A. P. Bianzino, C. Chaudet, F. Larrocca, D. Rossi, and J. L. Rougier. Dec.2010. Energy-Aware Routing:a Reality Check [C]. GreenComm 2010.
J. Blackburn and K. Christensen. Oct.2008. Green Telnet:Modifying a Client-Server Application to Save Energy [J]. Dr. Dobb's Journal.
J. Blackburn and K. Christensen. Jun.2009. A Simulation Study of a New Green BitTorrent [C]. GreenComm 2009.
A. Kansal and F. Zhao. Sep.2008. Fine-grained Energy Profiling for Power-aware Application Design [J]. SIGMETRICS Performance Evaluation Review, vol.36, pp. 26-31.
W. Baek and T. Chilimbi. Jul.2009. Green:A System for Supporting Energy-Conscious Programming using Principled Approximation [R]. Tech. Rep. MSR-TR-2009-89, Microsoft Research.
L. Irish and K. J. Christensen. Apr.1998. A "Green TCP/IP" to Reduce Electricity Consumed by Computers [C]. Proc. IEEE Southeastern 1998 Engineering for a New Era, pp.302-305.
B. Wang and S. Singh. Mar.2004. Computational Energy Cost of TCP [C]. INFOCOM 2004.
L. Minas, and B. Ellison.2009. Energy Efficiency for Information Technology:How to Reduce Power Consumption in Servers and Data Centers [M]. Intel Press, USA.
X. Fan, W.D. Weber, and L.A. Barroso.2007. Power provisioning for a warehouse-sized computer [C]. ISCA 2007.
G. Dhiman, K. Mihic, and T. Rosing.2010. A system for online power prediction in virtualized environments using gaussian mixture models [C]. Proceedings of the 47th ACM/IEEE Design Automation Conference.
P. Ranganathan, P. Leech, D. Irwin, and J. Chase.2006. Ensemble-level power man-agement for dense blade servers [C]. ISCA 2006.
S. Devadas, S. Malik, S. Devadas, abd S. Malik.1995. A survey of optimization tech-niques targeting low power VLSI circuits [C]. Proceedings of the 32nd ACM/IEEE Conference on Design Automation,1995.
S. Albers.2010. Energy-efficient algorithms [J]. Commun. ACM, vol.53, no.5, pp. 86-96.
M. B. Srivastava, A. P. Chandrakasan, and R. W. Brodersen.1996. Predictive sys-tem shutdown and other architectural techniques for energy efficient programmable computation [J]. IEEE Trans. VLSI Syst., vol.4, no.1, pp.42-55.
C. H. Hwang, and A. C. Wu.2000. A predictive system shutdown method for energy saving of event-driven computation [J]. ACM Trans. Des. Autom. Electron. Syst., vol.5 no.2, pp.241.
F. Douglis, P. Krishnan, and B. Bershad.1995. Adaptive disk spin-down policies for mobile computers [J]. Comput. Syst., vol.8 no.4, pp.381-413.
G. Buttazzo.2000. Scalable applications for energy-aware processors [J]. Embedded Software, pp.153-165.
L. L. Andrew, M. Lin, and A. Wierman.2010. Optimality, fairness, and robustness in speed scaling designs [C]. SIGMETRICS 2010.
K. Flautner, S. Reinhardt, and T. Mudge, Automatic performance setting for dynamic voltage scaling [J]. Wireless Netw., vol.8, no.5, pp.507-520.
J. R. Lorch, A. J. Smith.2001. Improving dynamic voltage scaling algorithms with PACE [J]. ACM SIGMETRICS Perform. Eval. Rev., vol.29 no.1 pp.61.
V. Pallipadi, A. Starikovskiy.2006. The ondemand governor [C]. Proceedings of the Linux Symposium, vol.2.
R. Rajkumar, K. Juvva, A. Molano, and S. Oikawa.2001. Resource kernels:a resource-centric approach to real-time and multimedia systems [J]. Readings in Multimedia Computing and Networking, Morgan Kaufmann Publishers Inc.,2001, pp.476-490.
H. Zeng, C. S. Ellis, and A.R. Lebeck.2005. Experiences in managing energy with ecosystem [J]. IEEE Pervasive Comput., vol.4, no.1, pp.62-68.
R. Neugebauer, and D. McAuley.2001. Energy is just another resource:energy ac-counting and energy pricing in the nemesis OS [C]. Proceedings of the 8th IEEE Workshop on Hot Topics in Operating Systems, Elmau/Oberbayern, Germany, pp. 59-64.
V. Vardhan, D. G. Sachs, W. Yuan, A. F. Harris, S. V. Adve, and D.L. Jones.2005. In-tegrating finegrained application adaptation with global adaptation for saving energy [C]. International Workshop on Power-Aware Real-Time Computing, Jersey City, NJ.
J. Flinn, and M. Satyanarayanan.2004. Managing battery lifetime with energy-aware adaptation [J]. ACM Trans. Comput. Syst., vol.22 no.2, pp.179.
E. Elnozahy, M. Kistler, and R. Rajamony.2003. Energy-efficient server clusters [J]. Power Aware Comput. Syst., vol.2325, pp.179-197.
A. Gandhi, M. Harchol-Balter, R. Das, and C. Lefurgy.2009. Optimal power allocation in server farms [C]. Proceedings of the 11th International Joint Conference on Measurement and Modeling of Computer Systems. pp.157-168.
S. K. Garg, C. S. Yeo, A. Anandasivam, and R. Buyya.2010. Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers [J]. J. Parallel Distributed Comput..
R. Nathuji, and K. Schwan.2007. Virtualpower:coordinated power management in virtualized enterprise systems [J]. ACM SIGOPS Oper. Syst. Rev., vol.41, no.6, pp.265-278.
D. Kusic, J.O. Kephart, J.E. Hanson, N. Kandasamy, and G. Jiang.2009. Power and performance management of virtualized computing environments via lookahead control [J]. Cluster Comput., vol.12, no.1, pp.1-15.
M. Stillwell, D. Schanzenbach, F. Vivien, and H. Casanova.2009. Resource allocation using virtual clusters [C]. CCGrid 2009.
Y. Song, H. Wang, Y. Li, B. Feng, and Y. Sun.2009. Multi-Tiered On-Demand resource scheduling for VM-Based data center [C]. CCGrid 2009.
A. Verma, P. Ahuja, and A. Neogi.2008. pMapper:power and migration cost aware application placement in virtualized systems [C]. Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp.243-264.
D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper.2009. Resource pool management: reactive versus proactive or let's be friends [J]. Comput. Netw., vol.53, no.17, pp. 2905-2922.
R. Buyya, A. Beloglazov, and J. Abawajy.2010. Energy-efficient management of data center resources for cloud computing:a vision, architectural elements, and open challenges [C]. PDPTA 2010.
M. L. Puteman.1994. Markov decision processes:discrete stochastic dynamic pro-gramming [M]. New York:Wiley.
X. R. Cao.1998. The relation among potentials, perturbation analysis, Markov decision processes, and other topics [J]. Journal of Discrete Event Dynamic Systems, vol.8, pp.71-87.
X. R. Cao and X. P. Guo.2004. A unified approach to Markov decision problems and performance sensitivity analysis with discounted and average criteria:Multichain cases [J]. Automatica, vol.40, pp.1749-1759.
X. R. Cao.2003. From perturbation analysis to Markov decision processes and rein-forcement learning[J]. Discrete Event Dynamic Systems:Theory and Applications, vol.13, pp.9-39.
X. R. Cao and H. F. Chen.1997. Perturbation realization, potentials, and sensitivity analysis of Markov processes[J]. IEEE Transactions on Automation and Control, vol.42, no.10, pp.1382-1393.
X. R. Cao.2007. Stochastic learning and optimization:a sensitivity-based ap-proach [M]. New York:Springer.
P. Marbach and J. N. Tsitsiklis.2001. Simulation-based optimization of Markov reward processes[J]. IEEE Transactions on Automatic Control, vol.46, no.2, pp.191-209.
H. T. Fang and X. R. Cao.2004. Potential-Based On-line Policy Iteration Algorithms for Markov Decision Processes[J]. IEEE Transactions on Automatic Control, vol. 49, no.4, pp.493-505.
S. Boyd and L. Valldenberghe. Convex Optimization [M]. New York, NY, USA: Cambridge UniVersity Press,2004.
X. P. Guo. Jun.2007. Constrained otimization for average cost continuous-time Markov decision processes[J]. IEEE Trans. Automatic Control, vol.52, no.6, p-p.1139-1143.
F. J. Beutle and K, W. Ross.1985. Optimal policies for controlledmarkov chains with a constraint[J]. Journal of Mathematical Analysis and Application, vol.112, no.1, pp.236-252.
J. Forestier, and P. Varaiya.1978. Multilayer control of large Markovchains [J]. IEEE Transactions on Automatic Control, vol.23, no.2, pp.298-304.
X. R. Cao, Z. Y. Ren, S. Bhatnagar, M. Fu, and S. Marcus.2002. A time aggregation approach to Markov decision processes [J]. Automatica, vol.38, no.6, pp.929-943.
H. S. Chang, P. J. Fard, S. I. Marcus, and M. Shayman.2003. Multitimescale Markov decision processes [J]. IEEE Trans. on Automatic Control, vol.48, no.6, pp.976-987.
Y. Wan, and X. R. Cao. The control of a two-level Markov decision process by time aggregation [J]. Automatica, vol.42, no.3, pp.393-403.
A. Dua and N. Bambos.2007. Buffer management for wireless media streaming [C]. IEEE GLOBECOM 2007.
S. G. Deshpande.2008. High quality video streaming using content-aware adaptive frame scheduling with explicit deadline adjustment [C]. ACM Multimedia.2008.
T. Stockhammer, H. Jenkac, and G. Kuhn.2004. Streaming video over variable bit-rate wireless channels [J]. IEEE Trans. Multimedia, vol.6, no.2, pp.268-277.
M. Kalman, E. Steinbach, and B. Girod. Jun.2004. Adaptive media playout for low-delay video streaming over error-prone channels [J]. IEEE Trans. Circuits and Syst. Video Technol., vol.14, no.6, pp.841-851.
H. C. Chuan, C. Y. Huang, and T. Chiang. May 2005. A novel adaptive video playout control for video streaming over mobile cellular environment [C]. IEEE ISCAS.
D. Tao, H. Hoang, and J. Cai.2007. Optimal frame selection with adaptive playout for delivering stored video under constrained resources [C]. IEEE ICME.
H. C. Chuang, C. Y. Huang, and T. H. Chiang.2007. Content-aware adaptive media playout controls for wireless video streaming [J]. IEEE Trans. Multimedia, vol.9, no.6, pp.1273-1283.
Y. F. Su, Y. H. Yang, M. T. Lu, and H. H. Chen.2009. Smooth control of adaptive media playout for video streaming [J]. IEEE Trans. Multimedia, vol.11, no.7, pp. 1331-1339.
Y. Li, A. Markopoulou, J. Apostolopoulos, and N. Bambos.2008. Content-aware play-out and packet scheduling for video streaming over wireless links [J]. IEEE Trans. Multimedia, vol.10, no.5, pp.885-895.
P.A. Chou and M. Zhourong.2006. Rate-distortion optimized streaming of packetized media [J]. IEEE Trans. Multimedia, vol.8, no.2, pp.390-404.
C. H. Liang and C. L. Huang.2004. Content-based adaptive media player for network video [C]. IEEE ISCAS,2004.
H.264/AVC Reference Software Version:JM 13.0. http://iphome.hhi.de/suehring/tml/index.htm.
P. Seeling, M. Reisslein, and B. Kulapala.2004. Network performance evaluation with frame size and quality traces of single-layer and two-layer video:a tutorial [J]. IEEE Communications Surveys and Tutorials, vol.6, no.3, pp.58-78.
G. V. Auwera, P. T. David, and M. Reisslein.2008. Traffic and quality characterization of single-layer video streams encoded with H.264/MPEG-4 advanced video coding standard and scalable video coding extension [J]. IEEE Trans. Broadcasting, vol. 54, no.3, pp.698-718.
L. Benini, A. Bogliolo, and G. D. Micheli.2000. A survey of design techniques for system level dynamic power management [J]. IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol.8, no.3, pp.299-316.
G. Semeraro, G. Magklis, R. Balasubramonian, D. H. Albonesi, S. Dwarkadas, and M. L. Scott. Energy efficient processor design using multiple clock domains with dynamic voltage and frequency scaling [C]. HPCA'02.
E. Pinheiro, R. Bianchini, E. V. Carrera, and T. Heath.2003. Dynamic Cluster Re-configuration for Power and Performance [J]. Compliers and Operating Systems for Low Power, Kluwer Academic Publishers, pp.75-93.
J. Slegers, N. Thomas, and I. Mitrani.2008. Dynamic Server Allocation for Power and Performance [C]. Proc. of the SPEC International Performance Evaluation Workshop on Performance Evaluation:Metrics, Models and Benchmarks, pp.247-261.
J. Slegers, I. Mitrani, and Thomas N.2009. Static and dynamic server allocation in systems with ON/OFF sources [J]. Annals of Operations Research, vol.170, pp.251-263.
C. Santana, J. C. B. Leite, and D. Mosse.2010. Load forecasting applied to soft real-time web clusters [C]. Proc. of the ACM Symposium on Applied Computing, pp. 346-350.
Y. J. Hong, J. C. Xue, and M. Thottethodi. Dynamic Server Provisioning to Minimize Cost in an IaaS Cloud[C]. Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems.
X. R. Wang, and Y. F. Wang. Coordinating Power Control and Performance Man-agement for Virtualized Server Clusters[J]. IEEE Trans. Parallel and Distributed Systems, vol.22, no.2, pp.245-259.
H. L. Yu, D. D. Zheng, B. Y. Zhao, and W. M. Zheng.2006. Understanding User Behavior in Large-Scale Video-on-Demand Systems[C]. Proceedings of the ACM SIGOPS/EuroSys European Conference on Computer Systems.
H. J. Kushner, G. Yin.2003. Stochastic Approximation and Recursive Algorithms and Applications [M]. New York:Springer.
V. Djonin V, and V. Krishnamurthy. Q-learning Algorithms for Constrained Markov Decision Process with Randomized Monotone Policies:Application to MIMO Transmission Control[J]. IEEE Transactions on Signal Processing, vool.55, no. 5, pp.2170-2181.
D. Bertsekas.2000. Nonlinear programming [M]. Athena Scientific:Belmont. MA.
L. Ljung.1977. Analysis of recursive stochastic algorithms [J]. IEEE Trans. on Auto-matic Control, Vol.22, No.4, pp.551-575.
O. Tamm, C. Hermsmeyer, and A. M. Rush.2010. Eco-sustainable System and Net-work Architectures for Future Transport Networks[J]. Bell Labs Technical Journal, vol.14, pp.311-327.
P. Mahadevan, S. Banerjee, and P. Sharma. Energy Proportionality of an Enterprise Network[C]. Proc. of the first ACM SIGCOMM workshop on Green Networking 2010.
M. Zink, K. Suh, and Yu Gu.2008. Watch Global, Cache Local:YouTube Network Traces at a Campus Network-Measurements and Implications[C]. MMCN'08.
M. Krunz, R. Saas, and H. Hughes.1995. Statistical characteristics and multiplexing of MPEG streams [C]. IEEE INFOCOM'95.
F. Yu, V. Krishnamurthy, and V. C. M. Leung.2006. Cross layer optimal connection admission control for variable bit rate multimedia traffic in packet wireless CDMA networks [J]. IEEE Trans. Signal Processing, vol.54, no.2, pp.542-555.
P. Skelly, M. Schwartz, and S. Dixit.1993. A histogram-based model for video traffic behavior in an ATMmultiplexer [J]. IEEE/ACM Trans. Netw., vol.1, pp.446-459.
奚宏生.2009.随机过程引论[M].合肥：中国科学技术大学出版社.
胡奇英,刘建墉.2000.马尔可夫决策过程引论[M].西安：西安电子科技大学出版社.
唐吴.2002MarrkoV控制过程的优化理论和算法[D].博士.合肥：中国科学技术大学.
李衍杰.2006.扩展MarkoV决策过程的性能灵敏度分析与优化[D].博士.合肥：中国科学技术大学.
徐陈峰.2008.面向P2P的Markov模型[D].博士.合肥：中国科学技术大学.
江琦.2008.半Markov切换空间控制过程及其应用[D].博士.合肥：中国科学技术大学.
江琦,奚宏生,殷保群.2007.动态电源管理的随机切换模型与在线优化[J].自动化学报,vol.33,no.1,pp.66-71.
卓居超.2010.时移电视集群系统缓存调度研究[D].博士.合肥：中国科学技术大学.
许书彬.2009.时移电视点播系统交互技术研究[D].博士.合肥：中国科学技术大学.