Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In dat...Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.展开更多
With the sky-rocketing development of Internet services, the power usage in data centers has been signifi- cantly increasing. This ever increasing energy consumption leads to negative environmental impact such as glob...With the sky-rocketing development of Internet services, the power usage in data centers has been signifi- cantly increasing. This ever increasing energy consumption leads to negative environmental impact such as global warming. To reduce their carbon footprints, large Internet service operators begin to utilize green energy. Since green energy is currently more expensive than the traditional brown one, it is important for the operators to maximize the green en- ergy usage subject to their desired long-term （e.g., a month） cost budget constraint. In this paper, we propose an online algorithm GreenBudget based on the Lyapunov optimization framework. We prove that our algorithm is able to achieve a delicate tradeoff between the green energy usage and the en- forcement of the cost budget constraint, and a control parameter V is the knob to arbitrarily tune such a tradeoff. We evaluate GreenBudget utilizing real-life traces of user requests, cooling efficiency, electricity price and green energy avail- ability. Experimental results demonstrate that under the same cost budget constraint, GreenBudget can increase the green energy usage by 11.55% compared with the state-of-the-art work, without incurring any performance violation of user requests.展开更多
针对云数据中心重调度算法大多是以牺牲云任务的完成时间或者增加云服务提供商的损失为代价的问题,系统分析了虚拟机故障所带来的性能损失和云数据中心容错策略的重调度过程。参考了Amazon云数据中心的真实定价机制,并对Amazon的多个云...针对云数据中心重调度算法大多是以牺牲云任务的完成时间或者增加云服务提供商的损失为代价的问题,系统分析了虚拟机故障所带来的性能损失和云数据中心容错策略的重调度过程。参考了Amazon云数据中心的真实定价机制,并对Amazon的多个云数据中心的虚拟机价格进行分析。同时,对失效的云任务根据截止时间进行了分类。利用不同配置虚拟机的定价差异提出了面向云数据中心的成本感知容错算法(Cost-aware Fault-tolerant Algorithm for Cloud Data Centers,CAFT)。该调度算法通过提高云任务的故障修复率从而降低云服务提供商的损失。实验仿真结果显示,相比IRW、RI和DTRDT算法,所提算法的故障修复率明显提升,同时,云服务提供商的损失平均降低了1.3%、13%和0.5%。展开更多
近年来,数据中心庞大的能源开销问题引起广泛关注.虚拟化管理平台可以通过虚拟机迁移技术将虚拟机整合到更少的服务器上,从而提高数据中心能源有效性.对面向数据中心节能的虚拟机整合研究工作进行调研,并总结虚拟机整合研究存在的3个挑...近年来,数据中心庞大的能源开销问题引起广泛关注.虚拟化管理平台可以通过虚拟机迁移技术将虚拟机整合到更少的服务器上,从而提高数据中心能源有效性.对面向数据中心节能的虚拟机整合研究工作进行调研,并总结虚拟机整合研究存在的3个挑战.针对已有工作未考虑虚拟机等待资源调度带来的服务器资源额外开销这种现象,开展了资源调度等待开销感知的虚拟机整合研究.从理论和实验上证明了在具有实际意义的约束条件下,存在着虚拟机等待资源调度带来的服务器资源额外开销,且随着整合虚拟机数量的增长保持稳定.基于典型工作负载的实验结果表明,这个额外开销平均占据了11.7%的服务器资源开销.此外,提出了资源预留整合(MRC)算法,用于改进已有的虚拟机整合算法.算法模拟实验结果表明,MRC算法相比于常用的虚拟机整合算法FFD(first fit decreasing),明显降低了服务器资源溢出概率.展开更多
基金supported in part by the following funding agencies of China:National Natural Science Foundation under Grant 61602050 and U1534201National Key Research and Development Program of China under Grant 2016QY01W0200
文摘Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.
文摘With the sky-rocketing development of Internet services, the power usage in data centers has been signifi- cantly increasing. This ever increasing energy consumption leads to negative environmental impact such as global warming. To reduce their carbon footprints, large Internet service operators begin to utilize green energy. Since green energy is currently more expensive than the traditional brown one, it is important for the operators to maximize the green en- ergy usage subject to their desired long-term （e.g., a month） cost budget constraint. In this paper, we propose an online algorithm GreenBudget based on the Lyapunov optimization framework. We prove that our algorithm is able to achieve a delicate tradeoff between the green energy usage and the en- forcement of the cost budget constraint, and a control parameter V is the knob to arbitrarily tune such a tradeoff. We evaluate GreenBudget utilizing real-life traces of user requests, cooling efficiency, electricity price and green energy avail- ability. Experimental results demonstrate that under the same cost budget constraint, GreenBudget can increase the green energy usage by 11.55% compared with the state-of-the-art work, without incurring any performance violation of user requests.
文摘针对云数据中心重调度算法大多是以牺牲云任务的完成时间或者增加云服务提供商的损失为代价的问题,系统分析了虚拟机故障所带来的性能损失和云数据中心容错策略的重调度过程。参考了Amazon云数据中心的真实定价机制,并对Amazon的多个云数据中心的虚拟机价格进行分析。同时,对失效的云任务根据截止时间进行了分类。利用不同配置虚拟机的定价差异提出了面向云数据中心的成本感知容错算法(Cost-aware Fault-tolerant Algorithm for Cloud Data Centers,CAFT)。该调度算法通过提高云任务的故障修复率从而降低云服务提供商的损失。实验仿真结果显示,相比IRW、RI和DTRDT算法,所提算法的故障修复率明显提升,同时,云服务提供商的损失平均降低了1.3%、13%和0.5%。
文摘近年来,数据中心庞大的能源开销问题引起广泛关注.虚拟化管理平台可以通过虚拟机迁移技术将虚拟机整合到更少的服务器上,从而提高数据中心能源有效性.对面向数据中心节能的虚拟机整合研究工作进行调研,并总结虚拟机整合研究存在的3个挑战.针对已有工作未考虑虚拟机等待资源调度带来的服务器资源额外开销这种现象,开展了资源调度等待开销感知的虚拟机整合研究.从理论和实验上证明了在具有实际意义的约束条件下,存在着虚拟机等待资源调度带来的服务器资源额外开销,且随着整合虚拟机数量的增长保持稳定.基于典型工作负载的实验结果表明,这个额外开销平均占据了11.7%的服务器资源开销.此外,提出了资源预留整合(MRC)算法,用于改进已有的虚拟机整合算法.算法模拟实验结果表明,MRC算法相比于常用的虚拟机整合算法FFD(first fit decreasing),明显降低了服务器资源溢出概率.
