Achieving application-centric performance targets via consolidation on multicores: myth or reality?

Consolidation of multiple applications with diverse and changing resource requirements is common in multicore systems as hardware resources are abundant and opportunities for better system usage are plenty. Can we maximize resource usage in such a system while respecting individual application performance targets or is it an oxymoron to simultaneously meet such conflicting measures? In this work we provide a solution to the above difficult problem by constructing a queueing-theory based tool that we use to accurately predict application scalability on multicores and that can also provide the optimal consolidation suggestions to maximize system resource usage while meeting simultaneously application performance targets. The proposed methodology is light-weight and relies on capturing application resource demands using standard tools, via nonintrusive low-level measurements. We evaluate our approach on an IBM Power7 system using the DaCapo and SPECjvm benchmark suites where each benchmark exhibits different patterns of parallelism. From 900 different consolidations of application instances, our tool accurately predicts the average iteration time of allocated applications with an average error below 10%.