LoadIQ: Learning to Identify Workload Phases from a Live Storage Trace

Storage infrastructure in large-scale cloud data center environments must support applications with diverse, time-varying data access patterns while observing the quality of service. Deeper storage hierarchies induced by solid state and rotating media are enabling new storage management tradeoffs that do not apply uniformly to all application phases at all times. To meet service level requirements in such heterogeneous application phases, storage management needs to be phase-aware and adaptive, i.e., to identify specific storage access patterns of applications as they occur and customize their handling accordingly.

This paper presents LoadIQ, a novel, versatile, adaptive, application phase detector for networked (file and block) storage systems. In a live deployment, LoadIQ analyzes traces and emits phase labels learnt on the fly by using Support Vector Machines(SVM), a state of the art classifier. Such labels could be used to generate alerts or to trigger phase-specific system tuning. Our results show that LoadIQ is able to identify workload phases (such as in TPC-DS) with accuracy > 93%.