  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.
