摘要
伴随着云计算的发展,以及软件即服务(SaaS)、方法即服务(FaaS)等服务框架的提出,数据中心作为服务的提供商,面临着持续性的资源管理挑战:一方面需要保证服务质量(quality of service,QoS),另一方面又需要控制资源成本.为了在提升资源使用率的同时确保负载压力在可承受范围内波动,一种精确衡量当前算力消耗程度的方法成为关键性的研究问题.传统的评估指标CPU利用率,由于虚拟化技术的成熟以及并行技术的发展,无法应对资源竞争所产生的干扰,失去了评估精度.而当前数据中心的主流处理器基本都开启了超线程技术,这导致评估超线程处理器算力消耗程度的需求亟待解决.为了应对这一评估挑战,基于超线程机制的理解以及线程行为的建模,提出一种评估超线程处理器算力消耗的方法APU.同时考虑到不同权限的用户能访问的系统层级不同,还提出了两种实现方案:一种基于硬件层支持的实现,以及一种基于操作系统层支持的实现.APU方法利用传统CPU利用率指标作为输入,没有其他维度的需求,免去了新监测工具的开发部署代价,也无需特殊硬件体系结构的支持,确保该方法的通用性和易用性.最后通过SPEC基准测试程序进一步证明该方法提升了算力评估的精度,分别将3种基准程序运行情况的算力评估误差从原先的20%,50%,以及20%下降至5%以内.为了进一步证明APU的实际应用能力,将其运用在了字节跳动的集群中,在案例研究中展示了它的应用效果.
With the development of cloud computing and service architectures including software as a service(SaaS)and function as a service(FaaS),data centers,as the service provider,constantly face resource management.The quality of service(QoS)should be guaranteed,and the resource cost should be controlled.Therefore,a method to accurately measure computing power consumption becomes a key research issue for improving resource utilization and keeping the load pressure in the acceptable range.Due to mature virtualization technologies and developing parallel technologies,the traditional estimation metric CPU utilization fails to address interference caused by resource competition,thus leading to accuracy loss.However,the hyper-threading(HT)technology is employed as the main data center processor,which makes it urgent to estimate the computing power of HT processors.To address this estimation challenge,this study proposes the APU method to estimate the computing power consumption for HT processors based on the understanding of the HT running mechanism and thread behavior modeling.Considering that users with different authorities can access different system levels,two implementation schemes are put forward:one based on the hardware support and the other based on the operating system(OS).The proposed method adopts CPU utilization as the input without demands for other dimensions.Additionally,it reduces the development and deployment costs of new monitoring tools without the support of special hardware architectures,thereby making the method universal and easy to apply.Finally,SPEC benchmarks further prove the effectiveness of the method.The estimation errors of the three benchmarks are reduced from 20%,50%,and 20%to less than 5%.For further proving the applicability,the APU method is leveraged to ByteDance clusters for showing its effects in case studies.
作者
温盈盈
程冠杰
邓水光
尹建伟
WEN Ying-Ying;CHENG Guan-Jie;DENG Shui-Guang;YIN Jian-Wei(College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China)
出处
《软件学报》
EI
CSCD
北大核心
2023年第12期5887-5904,共18页
Journal of Software
基金
国家自然科学基金(61825205)
浙江省重点研发计划(2021C01017)。
关键词
超线程
数据中心
算力评估
CPU利用率
系统性能分析
hyper-threading(HT)
data centers
computing power evaluation
CPU utilization
system performance analysis
