云数据中心资源调度机制研究
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摘要
近年来,随着互联网技术的飞速发展和虚拟化技术的普遍应用,云计算已经成为研究热点,用户可以从云中按需获得服务。云数据中心是在云计算环境下,由硬件和软件组成的松耦合资源共享架构。现有云数据中心在以下三方面存在不足之处：1.由于现有云数据中心没有实现充分有效的全局双向选择交易,因此导致了它没有满足大多数云市场参与者的交易需求并且它的大多数云资源提供者收益偏低。2.由于现有云数据中心没有充分考虑多维云资源的调度,因此其多维云资源利用率偏低。3.由于现有云数据中心没有充分考虑云任务间的通信能耗,因此它的能耗偏高。为了解决上述问题,本文提出了一种新颖的云数据中心资源调度机制。该机制基于可以满足大多数云市场交易者交易需求的改进双向拍卖竞价调度策略,利用融合了具有启发式优化能力的粒子群算法、遗传算法和蚁群算法的多维云资源高效调度方法,并通过虚拟机迁移方式实现了在提供原有系统性能的前提下关闭闲置的处理机的融合调度方案。通过大量的实验验证,该机制具有云资源提供者高收益、多维云资源高利用率和云数据中心低能耗的优点。本文主要研究成果如下：
1.基于双向拍卖理论的竞价调度策略。现有云数据中心没有实现充分有效的全局双向选择交易,大多数云市场参与者的交易请求并未得到满足,云资源提供者的收益偏低且存在严重的云市场交易不均衡问题。针对已有研究成果中云市场交易不均衡的局限性,本文通过分析云数据中心资源竞价调度问题中客观存在的灵活性和动态性特点,提出了融合双向拍卖理论、粒子群优化算法和遗传优化算法的竞价调度策略。该调度策略可以满足大多数云市场交易者的交易需求,并提高了云资源提供者的收益。本文借助于CloudSim实验体系架构验证了基于双向拍卖理论的竞价调度策略的可行性,与现有的调度策略相比,本调度策略使得云资源提供者收益增长率达到45%且交易量增长率达到37.1%。
2.基于整数马尔可夫理论的多维云资源高效调度方法。现有云数据中心没有真正的实现多维云资源调度,多维云资源(计算、存储和带宽)的资源利用率偏低且存在严重的碎片资源浪费问题。针对已有研究成果中多维碎片资源利用率偏低的局限性,本文通过总结影响多维云资源调度方法的因素,并以参数的形式反映其影响,提出了基于整数马尔可夫理论并结合改进的遗传算法和并行蚁群算法的计算、存储和带宽资源高效调度方法。该调度方法实现了多维云资源调度,并提高了多维碎片资源利用率。本文借助于CloudSim实验体系架构验证了基于整数马尔可夫理论的多维云资源高效调度方法的有效性,与现有的调度方法相比,本调度方法能够使得多维云资源利用率的增长率达到50.31%。
3.基于云任务的低能耗融合调度方案。随着云数据中心的规模不断扩大,云任务间的通信能耗不断提高,云数据中心的能量消耗偏高问题日益严重。针对已有研究成果并没有充分考虑云任务间通信能耗较高的局限性,本文对低能耗调度方案的内在规律进行抽象,提出了结合资源融合原理、粒子群和禁忌算法并融合调度计算、存储和带宽资源的低能耗融合调度方案。该调度方案充分考虑了云任务间的通信带宽能量消耗,并降低了云数据中心的能量消耗。本文借助于CloudSim实验体系架构验证了该方案的稳定性,与现有的调度方案相比,本调度方案能够使得云数据中心能耗下降率达到60.81%。
In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects:1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low.2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low.3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. In order to solve the above problems, this thesis proposes a novel resource scheduling mechanism inside the datacenter. This mechanism is based on the improved double auction bidding scheduling strategy which could satisfy most of the cloud market traders'trading needs. It also makes use of high efficient multi-dimensional cloud resource scheduling method which is integrated of heuristic particle swarm algorithm, genetic algorithm and ant colony algorithm. In addition, this mechanism achieves integrated scheduling scheme which could shut down idle processors under the premise of original system performance by the migration of virtual machines. Verified by a large number of experiments, this mechanism has advantages of cloud resource providers' high yield, multi-dimensional cloud resources' high utilization and cloud datacenters' low power consumption. And the main research results are as follows:
1. Bidding scheduling strategy based on double auction theory. Not only the existing cloud datacenter is not the real adequate and effective transaction of global bidirectional-way selection, but also most of the cloud market participants'trading requests have not been met. In addition, the revenue of cloud resources provider is too low and there is a serious problem of cloud market trading imbalance. For the limitation of transactions'unbalance in existing cloud market, this thesis analyzes the flexible and dynamic nature of cloud datacenters'bidding scheduling, and proposes a bidding scheduling strategy based on double auction mechanism, particle swarm optimal algorithm and genetic optimal algorithm This scheduling strategy could meet most of the cloud market traders' trading needs, and improve cloud resource providers' revenue. In this thesis, we verify the feasibility of bidding scheduling strategy based on double auction theory by CloudSim experimental architecture. Compared with the existing scheduling strategy, this scheduling strategy promotes cloud resources providers' revenue growth rate to45%and trading volume growth rate to37.1%.
2. Multi-dimensional cloud resource efficient scheduling method based on integer Markov theory. Not only the existing cloud datacenter is not the real multi-dimensional cloud resource scheduling, but also the resource utilization of multi-dimensional cloud resources (computing, storage and bandwidth) is too low. In addition, there is a serious problem of fragments resource waste existing in the cloud resources. For the limitation of the low multi-dimensional fragments resource utilization, this thesis summarizes the factors of affecting the multi-dimensional cloud resource scheduling method, reflects factors' impact in form of parameters, and proposes a multi-dimensional cloud resource efficient scheduling method based on integer markov theory and combined with genetic algorithm and parallel ant colony algorithm. In addition, this scheduling method is truly multi-dimensional cloud resource scheduling and improves the utilization of multi-dimensional fragments resource. In this thesis, we verify the effectiveness of efficient scheduling method based on integer markov theory by CloudSim experimental architecture. Compared with the existing scheduling method, we find that this scheduling method promotes growth rate of multi-dimensional cloud resource utilization to50.31%.
3. Low-power integrated scheduling scheme based on cloud tasks. With the expanding of the size of cloud datacenter, the energy consumption of communication between cloud tasks is continuously mushrooming. For the limitation of without considering the high energy consumption of communication between cloud tasks in existing research result, this thesis abstracts the inherent law of low-power scheduling scheme, and proposes a low-power integrated scheduling scheme combined with principles of resource integration, particle swarm optimization and tabu algorithm. In addition, this scheduling scheme also takes full account of the energy consumption of communication between cloud tasks, and reduces the energy consumption of the cloud datacenter. In this thesis, we verify the stability of low-power integrated scheduling scheme by CloudSim experimental architecture. By comparing with the existing scheduling scheme, we find that this scheduling scheme makes decline rate of energy consumption in cloud datacenter to60.81%.
1.基于双向拍卖理论的竞价调度策略。现有云数据中心没有实现充分有效的全局双向选择交易,大多数云市场参与者的交易请求并未得到满足,云资源提供者的收益偏低且存在严重的云市场交易不均衡问题。针对已有研究成果中云市场交易不均衡的局限性,本文通过分析云数据中心资源竞价调度问题中客观存在的灵活性和动态性特点,提出了融合双向拍卖理论、粒子群优化算法和遗传优化算法的竞价调度策略。该调度策略可以满足大多数云市场交易者的交易需求,并提高了云资源提供者的收益。本文借助于CloudSim实验体系架构验证了基于双向拍卖理论的竞价调度策略的可行性,与现有的调度策略相比,本调度策略使得云资源提供者收益增长率达到45%且交易量增长率达到37.1%。
2.基于整数马尔可夫理论的多维云资源高效调度方法。现有云数据中心没有真正的实现多维云资源调度,多维云资源(计算、存储和带宽)的资源利用率偏低且存在严重的碎片资源浪费问题。针对已有研究成果中多维碎片资源利用率偏低的局限性,本文通过总结影响多维云资源调度方法的因素,并以参数的形式反映其影响,提出了基于整数马尔可夫理论并结合改进的遗传算法和并行蚁群算法的计算、存储和带宽资源高效调度方法。该调度方法实现了多维云资源调度,并提高了多维碎片资源利用率。本文借助于CloudSim实验体系架构验证了基于整数马尔可夫理论的多维云资源高效调度方法的有效性,与现有的调度方法相比,本调度方法能够使得多维云资源利用率的增长率达到50.31%。
3.基于云任务的低能耗融合调度方案。随着云数据中心的规模不断扩大,云任务间的通信能耗不断提高,云数据中心的能量消耗偏高问题日益严重。针对已有研究成果并没有充分考虑云任务间通信能耗较高的局限性,本文对低能耗调度方案的内在规律进行抽象,提出了结合资源融合原理、粒子群和禁忌算法并融合调度计算、存储和带宽资源的低能耗融合调度方案。该调度方案充分考虑了云任务间的通信带宽能量消耗,并降低了云数据中心的能量消耗。本文借助于CloudSim实验体系架构验证了该方案的稳定性,与现有的调度方案相比,本调度方案能够使得云数据中心能耗下降率达到60.81%。
In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects:1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low.2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low.3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. In order to solve the above problems, this thesis proposes a novel resource scheduling mechanism inside the datacenter. This mechanism is based on the improved double auction bidding scheduling strategy which could satisfy most of the cloud market traders'trading needs. It also makes use of high efficient multi-dimensional cloud resource scheduling method which is integrated of heuristic particle swarm algorithm, genetic algorithm and ant colony algorithm. In addition, this mechanism achieves integrated scheduling scheme which could shut down idle processors under the premise of original system performance by the migration of virtual machines. Verified by a large number of experiments, this mechanism has advantages of cloud resource providers' high yield, multi-dimensional cloud resources' high utilization and cloud datacenters' low power consumption. And the main research results are as follows:
1. Bidding scheduling strategy based on double auction theory. Not only the existing cloud datacenter is not the real adequate and effective transaction of global bidirectional-way selection, but also most of the cloud market participants'trading requests have not been met. In addition, the revenue of cloud resources provider is too low and there is a serious problem of cloud market trading imbalance. For the limitation of transactions'unbalance in existing cloud market, this thesis analyzes the flexible and dynamic nature of cloud datacenters'bidding scheduling, and proposes a bidding scheduling strategy based on double auction mechanism, particle swarm optimal algorithm and genetic optimal algorithm This scheduling strategy could meet most of the cloud market traders' trading needs, and improve cloud resource providers' revenue. In this thesis, we verify the feasibility of bidding scheduling strategy based on double auction theory by CloudSim experimental architecture. Compared with the existing scheduling strategy, this scheduling strategy promotes cloud resources providers' revenue growth rate to45%and trading volume growth rate to37.1%.
2. Multi-dimensional cloud resource efficient scheduling method based on integer Markov theory. Not only the existing cloud datacenter is not the real multi-dimensional cloud resource scheduling, but also the resource utilization of multi-dimensional cloud resources (computing, storage and bandwidth) is too low. In addition, there is a serious problem of fragments resource waste existing in the cloud resources. For the limitation of the low multi-dimensional fragments resource utilization, this thesis summarizes the factors of affecting the multi-dimensional cloud resource scheduling method, reflects factors' impact in form of parameters, and proposes a multi-dimensional cloud resource efficient scheduling method based on integer markov theory and combined with genetic algorithm and parallel ant colony algorithm. In addition, this scheduling method is truly multi-dimensional cloud resource scheduling and improves the utilization of multi-dimensional fragments resource. In this thesis, we verify the effectiveness of efficient scheduling method based on integer markov theory by CloudSim experimental architecture. Compared with the existing scheduling method, we find that this scheduling method promotes growth rate of multi-dimensional cloud resource utilization to50.31%.
3. Low-power integrated scheduling scheme based on cloud tasks. With the expanding of the size of cloud datacenter, the energy consumption of communication between cloud tasks is continuously mushrooming. For the limitation of without considering the high energy consumption of communication between cloud tasks in existing research result, this thesis abstracts the inherent law of low-power scheduling scheme, and proposes a low-power integrated scheduling scheme combined with principles of resource integration, particle swarm optimization and tabu algorithm. In addition, this scheduling scheme also takes full account of the energy consumption of communication between cloud tasks, and reduces the energy consumption of the cloud datacenter. In this thesis, we verify the stability of low-power integrated scheduling scheme by CloudSim experimental architecture. By comparing with the existing scheduling scheme, we find that this scheduling scheme makes decline rate of energy consumption in cloud datacenter to60.81%.
引文
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[46]Laiping Zhao, Yizhi Ren, Sakurai K. A Resource Minimizing Scheduling Algorithm with Ensuring the Deadline and Reliability in Heterogeneous Systems. IEEE International Conference on Advanced Information Networking and Applications (AINA),2011, pp.275-282.
[47]Hui Chen, Meina Song, Junde Song, et al. HEaRS:A Hierarchical Energy-Aware Resource Scheduler for Virtualized Data Centers. IEEE International Conference on Cluster Computing (CLUSTER),2011, pp.508-512.
[48]Che-Yuan Tu, Wen-Chieh Kuo, Wei-Hua Teng, et al. A Power-Aware Cloud Architecture with Smart Metering.39th International Conference on Parallel Processing Workshops (ICPPW),2011, pp.497-500.
[1]Sugang Ma. A Review on Cloud Computing Development. Journal of Networks, Vol.7, No.2,2012, pp.305-310.
[2]Ldszlo Gyarmati, Tuan Anh Trinh. Scafida:A Scale-Free Network Inspired Data Center Architecture. ACM SIGCOMM Computer Communication Review, Vol.40, No.5,2010.
[3]Linlin Wu, Saurabh Kumar Garg, Rajkumar Buyya. SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011, pp.195-204.
[4]Ding Ding, S.L.A.Z. A Greedy Double Auction Mechanism for Grid Resource Allocation. Journal of cloud computing Vol.6253,2010, pp.35-50.
[5]Dusit Niyato, Athanasios V. Vasilakos, Zhu Kun. Resource and Revenue Sharing with Coalition Formation of Cloud Providers:Game Theoretic Approach.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011,pp.215-224.
[6]Ishai Menache, Asuman Ozdaglar, Nahum Shimkin. Socially Optimal Pricing of Cloud Computing Resources. Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools,2011, pp.16-25.
[7]Makhlouf Hadji, Wajdi Louati, Djamal Zeghlache. Constrained Pricing for Cloud Resource Allocation.10th IEEE International Symposium on Network Computing and Applications (NCA),2011, pp.359-365.
[8]Marian Mihailescu, Yong Meng Teo. Dynamic Resource Pricing on Federated Clouds.10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid),2010, pp.513-517.
[9]Wei-Yu Lin, Guan-Yu Lin, Hung-Yu Wei. Dynamic Auction Mechanism for Cloud Resource Allocation.10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid),2010, pp.591-592.
[10]Hongyi Wang, Qingfeng Jing, Rishan Chen, et al. Distributed Systems Meet Economics:Pricing in the Cloud. Proceedings of the 2nd USENIX conference on Hot topics in cloud computing,2010, pp.6-14.
[11]Shifeng Shang, Jinlei Jiang, Yongwei Wu, et al. DABGPM:A Double Auction Bayesian Game-Based Pricing Model in Cloud Market. Network and Parallel Computing. Vol.6289,2010, pp.155-164.
[12]Tim Pueschel, Fabian Putzke, Dirk Neumann. Revenue Management for Cloud Providers--A Policy-Based Approach under Stochastic Demand.45th Hawaii International Conference on System Science (HICSS),2012, pp.1583-1592.
[13]Xi Chen, Haopeng Chen, Qing Zheng, et al. Characterizing web application performance for maximizing service providers' profits in clouds. International Conference on Cloud and Service Computing (CSC),2011, pp.191-198.
[14]Liu Shi, Guang Wang, Ping Xu. TODA:Truthful Online Double Auction for Spectrum Allocation in Wireless Networks. IEEE Symposium on New Frontiers in Dynamic Spectrum,2010, pp.1-10.
[15]Wei Liu, Jiuping Xu. Some Properties on Expected Value Operator for Uncertain Variables. Information:An International Interdisciplinary Journal,2010, pp.808-811.
[16]Michele Mazzucco, Marlon Dumas. Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers. IEEE International Conference on Cloud Computing (CLOUD),2011, pp.428-435.
[17]Xueyong Li, Guohong Gao, Jiaxia Sun, et al. Fuzzy Clustering methods Based on Modified PSO and Its Application. Journal of Information-an International Interdisciplinary, No.14,2011, pp.925-930.
[18]Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, et al. CloudSim:A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. Journal of Software:Practice and Experience, Vol.41, No.1,2011, pp.23-50.
[19]Saurabh Kumar Garg, Rajkumar Buyya. NetworkCloudSim:Modelling Parallel Applications in Cloud Simulations. Fourth IEEE International Conference on Utility and Cloud Computing,2011, pp.105-113.
[20]Yong Beom Ma, Sung Ho Jang, Jong Sik Lee. QoS and Ontology-based Resource Management in Cloud Computing Environment. Journal of Information-an International Interdisciplinary, Vol.14,2011, pp.3707-3716.
[21]Chandramani Singh, SaswatiSarkar, Alireza Aram, et al. Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks, IEEE/ACM Transactions on Networking, Vol.20, No.1,2011, pp.69-83.
[1]Laszlo Gyarmati, Tuan Anh Trinh. Scafida:A Scale-Free Network Inspired Data Center Architecture. ACM SIGCOMM Computer Communication Review, Vol.40, No.5,2010.
[2]Sugang Ma. A Review on Cloud Computing Development. Journal of Networks, Vol.7, No.2,2012, pp.305-310.
[3]Simon Ostermann, Alexandria Iosup, Nezih Yigitbasi, et al. A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing. Computer Science Cloud Computing, Vol.34, No.4,2010, pp.115-131.
[4]Mario Mac'ias, Jordi Guitart. A Genetic Model for Pricing in Cloud Computing Markets. ACM Symposium on Applied Computing 2011, pp.113-118.
[5]Moschakis, Ioannis Karatza Helen. Evaluation of gang scheduling performance and cost in a cloud computing system. The Journal of Supercomputing, Vol.59, No.2,2012, pp.975-992.
[6]Hyewon Song, Chang, Seok Bae, et al. Utility adaptive service brokering mechanism for personal cloud service. MILITARY COMMUNICATIONS CONFERENCE,2011, pp.1622-1627.
[7]M. Mac'ias, J. Guitart. Maximising revenue in cloud computing markets by means of economically enhanced SLA management. Computer Architecture Department, Universitat Politecnica de Catalunya, Tech. Rep, UPC-DAC-RR-CAP-2010-22, 2010.
[8]Linlin Wu, Saurabh Kumar Garg, Rajkumar Buyya. SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011, pp.195-204.
[9]Minarolli, D. Freisleben B. Utility-based resource allocation for virtual machines in Cloud computing. IEEE Symposium on Computers and Communications (ISCC),2011, pp.410-417.
[10]Buyya, R, Garg, S K, Calheiros, R N. SLA-oriented resource provisioning for cloud computing:Challenges, architecture, and solutions. International Conference on Cloud and Service Computing (CSC),2011, pp.1-10.
[11]Tammaro D, Doumith E, A ZahrS, et al. Dynamic Resource Allocation in Cloud Environment Under Time-variant Job Requests. IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom),2011, pp.592-598.
[12]Michael Armbrust, Armando Fox Rean Griffith. A view of cloud computing. Communications of the ACM, Vol.53, No.4,2010, pp.50-58.
[13]Jia-Bin, Yuan.Yi-Ching, Lee.Wudy, et al. Building an intelligent provisioning engine for IaaS cloud computing services.13th Asia-Pacific Network Operations and Management Symposium (APNOMS),2011, pp.1-6.
[14]Sungkap, Yeo.Lee, H H S. Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment, vol.44, No.8,2011, pp.55-62.
[15]Zhu Q, Agrawal G Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments. IEEE Transactions on Services Computing, No.99,2010, pp.304-307.
[16]Dorian Minarolli, Bernd Freisleben. Utility-based resource allocation for virtual machines in Cloud computing. IEEE Symposium on Computers and Communications (ISCC),2011, pp.410-417.
[17]Breskovic, I.Maurer, M.Emeakaroha, et al. Cost-Efficient Utilization of Public SLA Templates in Autonomic Cloud Markets. Fourth IEEE International Conference on Utility and Cloud Computing (UCC),2011, pp.229-236.
[18]David Bernstein, Deepak Vij, Stephen Diamond. An Extensible Cloud Platform Inspired by Operating Systems. Fourth IEEE International Conference on Utility and Cloud Computing (UCC),2011, pp.306-311.
[19]Vijayakumar, S Qian, Zhu Agrawal, et al. Automated and Dynamic application accuracy management and resource provisioning in a cloud environment.11th IEEE/ACM International Conference on Grid Computing (GRID),2010, pp. 33-40.
[1]I. Rodero, J. Jaramillo, A. Quiroz, et al. Energy-efficient application-aware online provisioning for virtualized clouds and data centers. International Green Computing Conference,2010, pp.31-45.
[2]Michael Armbrust, Armando Fox Rean Griffith. A view of cloud computing. Communications of the ACM, Vol.53, No.4,2010, pp.50-58.
[3]Dung-Hai Liang, Dong-Shong Liang, I-Jyh Wen. Applications of Both Cloud Computing and E-government in Taiwan. JDCTA:International Journal of Digital Content Technology and its Applications, Vol.5, No.5,2011, pp.376-386.
[4]Andrew J. Younge, Gregorvon Laszewski, Lizhe Wang. Sonia Lopez-Alarcon., Warren Carithers. Efficient resource management for Cloud computing environments. International Green Computing Conference,2010, pp.357-364.
[5]Jing ling, Yuan Xing, Jiang Luo, et al. Energy Aware Resource Scheduling Algorithm for Data Center Using Reinforcement Learning. Fifth International Conference on Intelligent Computation Technology and Automation (ICICTA), 2012, pp.435-438.
[6]Shuo Liu, Gang Quan, Shangping Ren. On-Line Real-Time Service Allocation and Scheduling for Distributed Data Centers. IEEE International Conference on Services Computing Services (SCC),2011, pp.528-535.
[7]Li Xu, Guozhen Tan, Xia Zhang et al. Energy aware cloud application management in private cloud data center. International Conference on Cloud and Service Computing (CSC),2011, pp.274-279.
[8]C. Peoples, G. Parr, S. McClean. Energy-aware data centre management. National conference on Communications (NCC),2011, pp.1-5.
[9]Hui Chen, Meina Song, Junde Song, et al. HEaRS:A Hierarchical Energy-Aware Resource Scheduler for Virtualized Data Centers. IEEE International Conference on Cluster Computing (CLUSTER),2011, pp.508-512.
[10]I~nigo Goiri, Ferran Julia, Ram'on Nou, et al. Energy-aware Scheduling in Virtualized Datacenters. IEEE International Conference on Cluster Computing, 2010, pp.58-67.
[11]Ali Khajeh-Hosseini, Ian Sommerville, Ilango Sriram. Research Challenges for Enterprise Cloud Computing.1 st ACM Symposium on Cloud Computing, SOCC 2010,pp.1-11.
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[2]Simon Ostermann, Alexandria Iosup, Nezih Yigitbasi, et al. A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing. Computer Science Cloud Computing, Vol.34, No.4,2010, pp.115-131.
[3]Laszlo Gyarmati, Tuan Anh Trinh. Scafida:A Scale-Free Network Inspired Data Center Architecture. ACM SIGCOMM Computer Communication Review, Vol.40, No.5,2010, pp.4-12.
[4]Sugang Ma. A Review on Cloud Computing Development. Journal of Networks, Vol.7, No.2,2012, pp.305-310.
[5]Brandon Heller, Srini Seetharaman, Priya Mahadevan, et al. Elastic Tree:saving energy in data center networks. Proceedings of the 7th USENIX conference on Networked systems design and implementation,2010, pp.17-33.
[6]Jaliya Ekanayake, Geoffrey Fox. High Performance Parallel Computing with Clouds and Cloud Technologies. Computer Science Cloud Computing, Vol.34, No.1,2010, pp.20-38.
[7]Moschakis, Ioannis Karatza Helen. Evaluation of gang scheduling performance and cost in a cloud computing system. The Journal of Supercomputing, Vol.59, No.2,2012, pp.975-992.
[8]Hyewon Song, Chang, Seok Bae, et al. Utility adaptive service brokering mechanism for personal cloud service. MILITARY COMMUNICATIONS CONFERENCE,2011, pp.1622-1627.
[9]Cedric F. Lam. Optical network technologies for datacenter networks. Opticle Electronics and Communications Conference (OECC),2011, pp.1-3.
[10]Yang Zhenghong, Zhou Fawu. Cloud Computing and The internet of things. The Press of Tsinghua University,2011.
[11]Hui Chen, Meina Song, Junde Song, et al. HEaRS:A Hierarchical Energy-Aware Resource Scheduler for Virtualized Data Centers. IEEE International Conference on Cluster Computing (CLUSTER),2011, pp.508-512.
[1]Simon Ostermann, Alexandria Iosup, Nezih Yigitbasi, et al. A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing. Computer Science Cloud Computing, Vol.34, No.4,2010, pp.115-131.
[2]Laszlo Gyarmati. Tuan Anh Trinh. Scafida:A Scale-Free Network Inspired Data Center Architecture. ACM SIGCOMM Computer Communication Review, Vol.40, No.5,2010.
[3]Mario Mac'ias, Jordi Guitart. A Genetic Model for Pricing in Cloud Computing Markets. ACM Symposium on Applied Computing,2011, pp.113-118.
[4]Ishai Menache, Asuman Ozdaglar, Nahum Shimkin. Socially Optimal Pricing of Cloud Computing Resources. Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools,2011, pp.16-25.
[5]Dusit Niyato, Athanasios V. Vasilakos, Zhu Kun Resource and Revenue Sharing with Coalition Formation of Cloud Providers:Game Theoretic Approach.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011, pp.215-224.
[6]Makhlouf Hadji, Wajdi Louati, Djamal Zeghlache. Constrained Pricing for Cloud Resource Allocation.10th IEEE International Symposium on Network Computing and Applications (NCA),2011, pp.359-365.
[7]Marian Mihailescu, Yong Meng Teo. Dynamic Resource Pricing on Federated Clouds.10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid),2010, pp.513-517.
[8]M. Mac'ias, J. Guitart. Maximising revenue in cloud computing markets by means of economically enhanced SLA management. Computer Architecture Department, Universitat Politecnicade Catalunya Tech Rep, UPC-DAC-RR-CAP-2010-22, 2010.
[9]Marian Mihailescu, Yong Meng Teo. Strategy-Proof Dynamic Resource Pricing of Multiple Resource Types on Federated Clouds. Algorithms and Architectures for Parallel Processing, Vol.6081,2010, pp.337-350.
[10]Fei Teng, Fr'ed'eric Magoul es. Resource Pricing and Equilibrium Allocation Policy in Cloud Computing. IEEE 10th International Conference on Computer and Information Technology (CIT),2010, pp.195-202.
[11]Hongyi Wang, Qingfeng Jing, Rishan Chen, et al. Distributed Systems Meet Economics:Pricing in the Cloud. Proceedings of the 2nd USENIX conference on Hot topics in cloud computing,2010, pp.6-14.
[12]Wei-Yu Lin, Guan-Yu Lin, Hung-Yu Wei. Dynamic Auction Mechanism for Cloud Resource Allocation.10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid),2010, pp.591-592.
[13]Shifeng Shang, Jinlei Jiang, Yongwei Wu, et al. DABGPM:A Double Auction Bayesian Game-Based Pricing Model in Cloud Market. Network and Parallel Computing, Vol.6289,2010, pp.155-164.
[14]Sharrukh Zaman, Daniel Grosu. Combinatorial Auction-Based Allocation of Virtual Machine Instances in Clouds. IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom),2010, pp.127-134.
[15]Tim Pueschel, Fabian Putzke, Dirk Neumann. Revenue Management for Cloud Providers--A Policy-Based Approach under Stochastic Demand.45th Hawaii International Conference on System Science (HICSS),2012, pp.1583-1592.
[16]Michele Mazzucco, Marlon Dumas. Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers. IEEE International Conference on Cloud Computing (CLOUD),2011, pp.428-435.
[17]Xi Chen, Haopeng Chen, Qing Zheng, et al. Characterizing web application performance for maximizing service providers' profits in clouds. International Conference on Cloud and Service Computing (CSC),2011, pp.191-198.
[18]Jose Orlando Melendez, Shikharesh Majumdar. Utilizing "Opaque" Resources for Revenue Enhancement on Clouds and Grids.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011, pp.576-584.
[19]Ravin Ahuja, Asok De, Goldie Gabrani. SLA Based Scheduler for Cloud for Storage and Computational Services. International Conference on Computational Science and Its Applications (ICCSA),2011, pp.258-262.
[20]J. Oriol Fit'o,'I-nigo Goiri, Jordi Guitart. SLA-driven Elastic Cloud Hosting Provider.18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP),2010, pp.111-118.
[21]Linlin Wu, Saurabh Kumar Garg, Rajkumar Buyya. SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011, pp.195-204.
[22]Qian zhu, Gagan Agrawal. Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments. IEEE Transactions on Services Computing No.99,2010, pp.304-307.
[23]Dorian Minarolli, Bernd Freisleben. Utility-based resource allocation for virtual machines in Cloud computing. IEEE Symposium on Computers and Communications (ISCC),2011, pp.410-417.
[24]Smita Vijayakumar, Qian Zhu, Gagan Agrawal. Automated and dynamic application accuracy management and resource provisioning in a cloud environment.11th IEEE/ACM International Conference on Grid Computing (GRID),2012, pp.33-40.
[25]Davide Tammaro, Elias A. Doumith, Sawsan Al Zahr, et al. Dynamic Resource Allocation in Cloud Environment Under Time-variant Job Requests. IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom),2011, pp.592-598.
[26]Joseph Idziorek, Mark Tannian. Exploiting Cloud Utility Models for Profit and Ruin. IEEE International Conference on Cloud Computing (CLOUD),2011, pp. 33-40.
[27]Dorian Minarolli, Bernd Freisleben. Utility-driven Allocation of Multiple Types of Resources to Virtual Machines in Clouds. IEEE 13th Conference on Commerce and Enterprise Computing (CEC),2011, pp.137-144.
[28]Mukundan Sridharan, Prasad Calyam, Aishwarya Venkataraman, et al. Defragmentation of Resources in Virtual Desktop Clouds for Cost-Aware Utility-Optimal Allocation. Fourth IEEE International Conference on Utility and Cloud Computing (UCC),2011, pp.253-260.
[29]Hyewon Song, Chang Seok Bae, Jeun Woo Lee, et al. Utility adaptive service brokering mechanism for personal cloud service. MILITARY COMMUNICATIONS CONFERENCE,2011, pp.1622-1627.
[30]Rajkumar Buyya, Saurabh Kumar Garg, Rodrigo N. Calheiros. SLA-oriented resource provisioning for cloud computing:Challenges, architecture, and solutions. International Conference on Cloud and Service Computing (CSC), 2011, pp.1-10.
[31]David Bernstein, Deepak Vij, Stephen Diamond. An Extensible Cloud Platform Inspired by Operating Systems. Fourth IEEE International Conference on Utility and Cloud Computing (UCC),2011, pp.306-311.
[32]Damian A. Tamburri, Patricia Lago., Satisfying Cloud Computing Requirements with Agile Service Networks. IEEE World Congress on Services (SERVICES), 2011,pp.501-506.
[33]Jia-Bin Yuan, Yi-Ching Lee, Wudy Wu, et al. Building an intelligent provisioning engine for IaaS cloud computing services.13th Asia-Pacific Network Operations and Management Symposium (APNOMS),2011, pp.1-6.
[34]Jing ling, Yuan Xing, Jiang Luo, et al. Energy Aware Resource Scheduling Algorithm for Data Center Using Reinforcement Learning. Fifth International Conference on Intelligent Computation Technology and Automation (ICICTA), 2012, pp.435-438.
[35]Ching-Chi Lin, Pangfeng Liu, Jan-Jan Wu. Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing. IEEE International Conference on Cloud Computing (CLOUD),2011, pp.736-737.
[36]H. Viswanathan, E.K. Lee, I. Rodero, et al. Energy-Aware Application-Centric VM Allocation for HPC Workloads. IEEE International Symposium on Parallel & Distributed Processing SymposiumParallel and Distributed Processing Workshops and Phd Forum (IPDPSW),2011, pp.890-897.
[37]Shuo Liu, Gang Quan, Shangping Ren. On-Line Real-Time Service Allocation and Scheduling for Distributed Data Centers. IEEE International Conference on Services Computing Services (SCC),2011, pp.528-535.
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[41]Li Xu, Guozhen Tan, Xia Zhang, et al. Energy aware cloud application management in private cloud data center. International Conference on Cloud and Service Computing (CSC),2011, pp.274-279.
[42]Ata E Husain Bohra, Vipin Chaudhary. VMeter:Power modelling for virtualized clouds. IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW),2011, pp.1-8.
[43]I. Rodero, J. Jaramillo, A. Quiroz, et al. Energy-efficient application-aware online provisioning for virtualized clouds and data centers. International Green Computing Conference,2010, pp.31-45.
[44]Andrew J. Younge, Gregorvon Laszewski, Lizhe Wang, et al. Warren Carithers. Efficient resource management for Cloud computing environments. International Green Computing Conference,2010, pp.357-364.
[45]C. Peoples, G Parr, S. McClean. Energy-aware data centre management. National conference on Communications (NCC),2011, pp.1-5.
[46]Laiping Zhao, Yizhi Ren, Sakurai K. A Resource Minimizing Scheduling Algorithm with Ensuring the Deadline and Reliability in Heterogeneous Systems. IEEE International Conference on Advanced Information Networking and Applications (AINA),2011, pp.275-282.
[47]Hui Chen, Meina Song, Junde Song, et al. HEaRS:A Hierarchical Energy-Aware Resource Scheduler for Virtualized Data Centers. IEEE International Conference on Cluster Computing (CLUSTER),2011, pp.508-512.
[48]Che-Yuan Tu, Wen-Chieh Kuo, Wei-Hua Teng, et al. A Power-Aware Cloud Architecture with Smart Metering.39th International Conference on Parallel Processing Workshops (ICPPW),2011, pp.497-500.
[1]Sugang Ma. A Review on Cloud Computing Development. Journal of Networks, Vol.7, No.2,2012, pp.305-310.
[2]Ldszlo Gyarmati, Tuan Anh Trinh. Scafida:A Scale-Free Network Inspired Data Center Architecture. ACM SIGCOMM Computer Communication Review, Vol.40, No.5,2010.
[3]Linlin Wu, Saurabh Kumar Garg, Rajkumar Buyya. SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011, pp.195-204.
[4]Ding Ding, S.L.A.Z. A Greedy Double Auction Mechanism for Grid Resource Allocation. Journal of cloud computing Vol.6253,2010, pp.35-50.
[5]Dusit Niyato, Athanasios V. Vasilakos, Zhu Kun. Resource and Revenue Sharing with Coalition Formation of Cloud Providers:Game Theoretic Approach.11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),2011,pp.215-224.
[6]Ishai Menache, Asuman Ozdaglar, Nahum Shimkin. Socially Optimal Pricing of Cloud Computing Resources. Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools,2011, pp.16-25.
[7]Makhlouf Hadji, Wajdi Louati, Djamal Zeghlache. Constrained Pricing for Cloud Resource Allocation.10th IEEE International Symposium on Network Computing and Applications (NCA),2011, pp.359-365.
[8]Marian Mihailescu, Yong Meng Teo. Dynamic Resource Pricing on Federated Clouds.10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid),2010, pp.513-517.
[9]Wei-Yu Lin, Guan-Yu Lin, Hung-Yu Wei. Dynamic Auction Mechanism for Cloud Resource Allocation.10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid),2010, pp.591-592.
[10]Hongyi Wang, Qingfeng Jing, Rishan Chen, et al. Distributed Systems Meet Economics:Pricing in the Cloud. Proceedings of the 2nd USENIX conference on Hot topics in cloud computing,2010, pp.6-14.
[11]Shifeng Shang, Jinlei Jiang, Yongwei Wu, et al. DABGPM:A Double Auction Bayesian Game-Based Pricing Model in Cloud Market. Network and Parallel Computing. Vol.6289,2010, pp.155-164.
[12]Tim Pueschel, Fabian Putzke, Dirk Neumann. Revenue Management for Cloud Providers--A Policy-Based Approach under Stochastic Demand.45th Hawaii International Conference on System Science (HICSS),2012, pp.1583-1592.
[13]Xi Chen, Haopeng Chen, Qing Zheng, et al. Characterizing web application performance for maximizing service providers' profits in clouds. International Conference on Cloud and Service Computing (CSC),2011, pp.191-198.
[14]Liu Shi, Guang Wang, Ping Xu. TODA:Truthful Online Double Auction for Spectrum Allocation in Wireless Networks. IEEE Symposium on New Frontiers in Dynamic Spectrum,2010, pp.1-10.
[15]Wei Liu, Jiuping Xu. Some Properties on Expected Value Operator for Uncertain Variables. Information:An International Interdisciplinary Journal,2010, pp.808-811.
[16]Michele Mazzucco, Marlon Dumas. Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers. IEEE International Conference on Cloud Computing (CLOUD),2011, pp.428-435.
[17]Xueyong Li, Guohong Gao, Jiaxia Sun, et al. Fuzzy Clustering methods Based on Modified PSO and Its Application. Journal of Information-an International Interdisciplinary, No.14,2011, pp.925-930.
[18]Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, et al. CloudSim:A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. Journal of Software:Practice and Experience, Vol.41, No.1,2011, pp.23-50.
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