动态基础设施中服务干扰的建模与表征
Modeling and Characterizing Service Interference in Dynamic Infrastructures
AI总结：
本文提出基于验证性因子分析（CFA）的模型，用于估计云系统中Docker容器间因CPU、内存和IO使用导致的性能干扰，并通过时间序列指标预测整体执行时间，以支持干扰感知的资源调度。
摘要：
在云系统中，当各种服务在同一物理基础设施上执行时，可能会发生性能干扰。与单独执行服务相比，这可能导致性能下降。本研究提出了一种基于验证性因子分析（CFA）的模型，用于估计由多个共置应用对CPU、内存和IO的使用所引起的跨容器性能干扰。该方法通过以时间序列形式呈现的人类可理解的指标来提供资源表征，从而可以分析服务整个执行生命周期中的干扰。我们的实验基于在Docker容器中执行的不同配置的真实服务的组合，表明该模型能够准确预测不同服务组合下的整体执行时间。该方法可供服务设计者用于识别服务执行生命周期中可能导致更大程度干扰的阶段，并确保只有互补的服务托管在同一物理机器上。这种干扰感知能力将使云系统实现更智能的资源管理和调度，并可用于动态修改调度决策。
英文摘要：
Performance interference can occur when various services are executed over the same physical infrastructure in a cloud system. This can lead to performance degradation compared to the execution of services in isolation. This work proposes a Confirmatory Factor Analysis (CFA)-based model to estimate performance interference across containers, caused by the use of CPU, memory and IO across a number of co-hosted applications. The approach provides resource characterization through human comprehensible indices expressed as time series, so the interference in the entire execution lifetime of a service can be analyzed. Our experiments, based on the combination of real services with different profiles executed in Docker containers, suggest that our model can accurately predict the overall execution time, for different service combinations. The approach can be used by a service designer to identify phases, during the execution life-cycle of a service, that are likely to lead to a greater degree of interference, and to ensure that only complementary services are hosted on the same physical machine. Interference-awareness of this kind will enable more intelligent resource management and scheduling for cloud systems, and may be used to dynamically modify scheduling decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
