In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the r...In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.展开更多
无线供能移动边缘计算(wireless powered-mobile edge computing,WP-MEC)集成了移动边缘计算和无线功率传输技术,旨在解决移动设备计算能力不足和持续能源供应问题。然而由于WP-MEC中不同边缘服务器供电能力和计算能力不同、移动设备需...无线供能移动边缘计算(wireless powered-mobile edge computing,WP-MEC)集成了移动边缘计算和无线功率传输技术,旨在解决移动设备计算能力不足和持续能源供应问题。然而由于WP-MEC中不同边缘服务器供电能力和计算能力不同、移动设备需执行任务的延迟忍耐时间异构,以及移动设备与服务器之间时变的无线信道给系统时间资源分配和任务处理带来了巨大挑战。基于此,从WP-MEC网络的异构服务器选择、计算卸载和资源分配联合优化的角度开展研究,为提高系统有效计算率,提出了基于延迟敏感性任务加权平均的坐标下降(joint optimization scheduling algorithm with weighted average of delay-sensitive tasks and coordinate descent,WADT_CD)联合调度算法。首先,综合考虑时变无线信道增益、异构任务延迟、异构边缘服务器的发射功率和计算能力,设计基于延迟敏感性任务加权平均(scheme of weighted average of delay-sensitive tasks,WADT)的异构服务器选择策略。其次,考虑WP-MEC网络模型特性,设计基于一维时间变量二分搜索的坐标下降(method of coordinate descent,CD)算法解决移动设备卸载决策和时间资源分配问题。最后,通过仿真实验与多种算法进行对比,验证了所提方法的优越性,并且分析了在不同规模边缘设备、异构任务比例时所提算法的有效性。展开更多
基金supported by the National Natural Science Foundation of China(6147219261202004)+1 种基金the Special Fund for Fast Sharing of Science Paper in Net Era by CSTD(2013116)the Natural Science Fund of Higher Education of Jiangsu Province(14KJB520014)
文摘In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.
文摘无线供能移动边缘计算(wireless powered-mobile edge computing,WP-MEC)集成了移动边缘计算和无线功率传输技术,旨在解决移动设备计算能力不足和持续能源供应问题。然而由于WP-MEC中不同边缘服务器供电能力和计算能力不同、移动设备需执行任务的延迟忍耐时间异构,以及移动设备与服务器之间时变的无线信道给系统时间资源分配和任务处理带来了巨大挑战。基于此,从WP-MEC网络的异构服务器选择、计算卸载和资源分配联合优化的角度开展研究,为提高系统有效计算率,提出了基于延迟敏感性任务加权平均的坐标下降(joint optimization scheduling algorithm with weighted average of delay-sensitive tasks and coordinate descent,WADT_CD)联合调度算法。首先,综合考虑时变无线信道增益、异构任务延迟、异构边缘服务器的发射功率和计算能力,设计基于延迟敏感性任务加权平均(scheme of weighted average of delay-sensitive tasks,WADT)的异构服务器选择策略。其次,考虑WP-MEC网络模型特性,设计基于一维时间变量二分搜索的坐标下降(method of coordinate descent,CD)算法解决移动设备卸载决策和时间资源分配问题。最后,通过仿真实验与多种算法进行对比,验证了所提方法的优越性,并且分析了在不同规模边缘设备、异构任务比例时所提算法的有效性。
