LoadIQ: Learning to Identify Workload Phases From a Live Storage Trace
Source: Indian Institute of Science
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.