Cross-application interference can affect drastically performance of HPC applications when running in clouds. This problem is caused by concurrent access performed by co-located applications to shared and non-sliceable resources such as cache and memory. In order to address this issue, some works adopted a qualitative approach that does not take into account the amount of access to shared resources. In addition, a few works, even considering the amount of access, evaluated just the SLLC access contention as the root of this problem. However, our experiments revealed that interference is intrinsically related to the amount of simultaneous access to shared resources, besides showing that another shared resources, apart from SLLC, can also influence the interference suffered by co-located applications. In this paper, we present a quantitative model for predicting cross-application interference in virtual environments. Our proposed model takes into account the amount of simultaneous access to SLLC, DRAM and virtual network, and the similarity of application's access burden to predict the level of interference suffered by applications when co-located in a same physical machine. Experiments considering a real petroleum reservoir simulator and applications from HPCC benchmark showed that our model reached an average and maximum prediction errors around 4\% and 12\%, besides achieving an error less than 10\% in approximately 96\% of all tested cases.

翻译：在云层中运行时,应用交叉干扰会极大地影响HPC应用的功能。这个问题是由于同时使用同一地点的应用程序对共享和不可过滤的资源(如缓存和记忆等)同时使用共同使用的资源造成的。为了解决这一问题,一些作品采用了不考虑共享资源使用量的质量方法。此外,一些作品,即使考虑到访问量,也仅仅评价了SLLC访问争议,将SLLC访问争议作为这一问题的根源。然而,我们的实验显示,干扰与同时获取共享资源的数量有着内在的联系,此外,还表明除SLLC之外,其他共享资源也可影响共同使用的共同使用资源。在本文中,我们提出了一个量化模型,用于预测虚拟环境中交叉应用干扰。我们提议的模型考虑到同时使用SLLC、DRAM和虚拟网络的数量,以及应用的相似性负担,以预测同一机器同时使用应用时受到的干扰程度。在考虑真正的石油储油层模拟器和HPCC基准应用的实验显示,我们的模型在大约12个案例中达到平均和最高误差率,大约是96个案例。