Resource pre-allocation algorithms for low-energy task scheduling of cloud computing
Resource pre-allocation algorithms for low-energy task scheduling of cloud computing
复制标题
云计算低能耗任务调度的资源预分配算法
DOI：
10.1109/jsee.2016.00047
复制
发表时间：
2016-04
影响因子：
2.1
通讯作者：
WANG Xinheng
中科院分区：
文献类型：
--
作者：
XU Xiaolong;CAO Lingling;WANG Xinheng
为了降低现有云计算系统的功耗，提高资源利用率，本文提出了两种基于“关闭冗余，打开需要的”策略的资源预分配算法。首先提出了一种绿色云计算模型，利用虚拟化技术将任务调度问题抽象为虚拟机部署问题。其次，需要预测系统未来的工作负载：提出一种基于保守控制（CESCC）策略的三次指数平滑算法，结合系统当前状态和资源分布，计算下一周期任务请求的资源需求。然后，提出了功耗多目标约束优化模型和基于概率匹配的低能耗资源分配算法（RA-PM）。为了进一步降低功耗，利用改进模拟退火算法，设计了基于改进模拟退火的资源分配算法（RA-ISA）。实验结果表明，预测和保守控制策略使资源预分配赶上需求，提高了实时响应效率和系统稳定性。 RA-PM和RA-ISA都可以激活更少的主机，在高适用主机集合之间实现更好的负载均衡，最大限度地提高资源利用率，并大大降低云计算系统的功耗。
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.