  Understanding inter-VM interference is of paramount importance to provide a
sound knowledge and understand where performance degradation comes from in the
current public cloud. With this aim, this paper devises a workload taxonomy
that classifies applications according to how the major system resources affect
their performance (e.g., tail latency) as a function of the level of load
(e.g., QPS). After that, we present three main studies addressing three major
concerns to improve the cloud performance: impact of the level of load on
performance, impact of hyper-threading on performance, and impact of limiting
the major system resources (e.g., last level cache) on performance. In all
these studies we identified important findings that we hope help cloud
providers improve their system utilization.
