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
An Energy-Harvesting Wireless Sensor Network （EH-WSN） depends on harvesting energy from the environment to prolong network lifetime. Subjected to limited energy in complex environments, an EH-WSN encounters difficul...An Energy-Harvesting Wireless Sensor Network （EH-WSN） depends on harvesting energy from the environment to prolong network lifetime. Subjected to limited energy in complex environments, an EH-WSN encounters difficulty when applied to real environments as the network efficiency is reduced. Existing EH-WSN studies are usually conducted in assumed conditions in which nodes are synchronized and the energy profile is knowable or calculable. In real environments, nodes may lose their synchronization due to lack of energy. Furthermore, energy harvesting is significantly affected by multiple factors, whereas the ideal hypothesis is difficult to achieve in reality. In this paper, we introduce a general Intermittent Energy-Aware （lEA） EH-WSN platform. For the first time, we adopted a double-stage capacitor structure to ensure node synchronization in situations without energy harvesting, and we used an integrator to achieve ultra-low power measurement. With regard to hardware and software, we provided an optimized energy management mechanism for intermittent functioning. This paper describes the overall design of the lEA platform, and elaborates the energy management mechanism from the aspects of energy management, energy measurement, and energy prediction. In addition, we achieved node synchronization in different time and energy environments, measured the energy in reality, and proposed the light weight energy calculation method based on measured solar energy. In real environments, experiments are performed to verify the high performance of lEA in terms of validity and reliability. The lEA platform is shown to have ultra-low power consumption and high accuracy for energy measurement and prediction.展开更多
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.
基金supported in part by the Key Program of National Natural Science Foundation of China (No. 61632010)Harbin Municipal Science and Technology Innovation Talent Research Funded Project (No. 2014RFQXJ027)
文摘An Energy-Harvesting Wireless Sensor Network （EH-WSN） depends on harvesting energy from the environment to prolong network lifetime. Subjected to limited energy in complex environments, an EH-WSN encounters difficulty when applied to real environments as the network efficiency is reduced. Existing EH-WSN studies are usually conducted in assumed conditions in which nodes are synchronized and the energy profile is knowable or calculable. In real environments, nodes may lose their synchronization due to lack of energy. Furthermore, energy harvesting is significantly affected by multiple factors, whereas the ideal hypothesis is difficult to achieve in reality. In this paper, we introduce a general Intermittent Energy-Aware （lEA） EH-WSN platform. For the first time, we adopted a double-stage capacitor structure to ensure node synchronization in situations without energy harvesting, and we used an integrator to achieve ultra-low power measurement. With regard to hardware and software, we provided an optimized energy management mechanism for intermittent functioning. This paper describes the overall design of the lEA platform, and elaborates the energy management mechanism from the aspects of energy management, energy measurement, and energy prediction. In addition, we achieved node synchronization in different time and energy environments, measured the energy in reality, and proposed the light weight energy calculation method based on measured solar energy. In real environments, experiments are performed to verify the high performance of lEA in terms of validity and reliability. The lEA platform is shown to have ultra-low power consumption and high accuracy for energy measurement and prediction.
