面向云数据中心的能源感知虚拟机调度方法

IF 3.9 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Jie Li;Yuhui Deng;Zijie Zhong;Zhaorui Wu;Shujie Pang;Lin Cui;Geyong Min
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引用次数: 0

摘要

由于应用数据的爆炸式增长,云数据中心的能耗降低将更加迫切。虚拟机集成是目前应用于数据中心计算设施的一种比较标准的技术。但是,过多的虚拟机整合容易造成局部热点,降低数据中心的能源效率和可靠性。此外,由于数据中心存在热循环的影响,传统的虚拟机调度策略无法全面考虑数据中心整体能耗的优化,包括服务器能耗和冷却能耗。为了解决这些问题,我们提出了一种能量感知的虚拟机调度方法EAVMS,以最大限度地减少数据中心的整体能耗。EAVMS采用两阶段方法,在保证QoS的同时提高能效。首先,EAVMS利用混合遗传算法和模拟退火算法(BGSA)来优化虚拟机的初始位置。其次,EAVMS采用动态迁移算法,在不违反服务水平协议(SLA)的情况下,通过设置最大服务器温度阈值来实现有效的迁移。服务水平协议通过调节服务器的热点来降低能耗。我们使用两个真实世界的轨迹(即PlanetLab和谷歌Cluster数据集)进行了广泛的实验,以评估EAVMS的有效性。实验结果表明,与其他最先进的替代方案(例如,MJPM, GRANITE, TAS, XINT-GA和Random)相比,我们的方法能够在云数据中心的整体能耗中节省3.23 -43.07美元,而服务性能仅略有下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Energy-Aware Virtual Machine Scheduling Approach for Cloud Data Centers
The reduction of energy consumption will be even more urgent in cloud data centers due to the explosive increase of application data. Virtual machine (VM) integration is a relatively standard technology currently applied for computing facilities of data centers. However, excessive VM consolidation can easily lead to local hot spots that lower the energy efficiency and reliability of data centers. In addition, on account of the impact of heat recirculation in data centers, the traditional VM scheduling strategy cannot comprehensively ponder optimizing the holistic data center energy, which encompasses both server energy and cooling energy. To handle these issues, we proposed EAVMS- an Energy-Aware VM Scheduling approach for minimizing the holistic energy consumption of data centers. EAVMS adopts a two-phase approach to gain energy efficiency while guaranteeing QoS. First, EAVMS leverages a Blended Genetic algorithm and Simulated Annealing algorithm (BGSA) to optimize the initial placement of VMs. Second, EAVMS utilizes a dynamic migration algorithm to achieve effective migration by setting a maximum server temperature threshold without violating the service level agreement (SLA) that cuts down energy consumption by moderating the hot spots of servers. We conducted extensive experiments using two real-world traces (i.e., PlanetLab and Google Cluster datasets) to evaluate the effectiveness of EAVMS. The experimental results unveil that our approach is capable of saving 3.23$ \%$–43.07$ \%$ in the holistic energy consumption of cloud data centers with only a tiny service performance degradation compared to other state-of-the-art alternatives (e.g., MJPM, GRANITE, TAS, XINT-GA, and Random).
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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