Storage Workload Isolation via Tier Warming: How Models Can Help.

Abstract

Storage systems are often deployed in a tiered form to enable high performance and availability. These tiers utilize all possible volatile and non-volatile storage technologies, including DRAM, SSD, and HDD. The tradeoffs among their cost, features, and capabilities can make their effective integration into a single storage entity complex. Here, we propose an autonomic technique that learns user traffic patterns in a storage system over long time-scales to optimize user performance but also volume of completed system work. Our purpose is to multiplex as best as possible user workload with storage system features (e.g., voluminous internal system work) such that the latter is not starved but rather completed with minimal impact on user performance. Key to achieving the above is to use an autonomic learning engine to predict when the user workload intensity increases/decreases and then proactively stop/start bulky internal system work. Being proactive allows the system to effectively bring into the fast tier the active user working set just-in-time and right before it is needed most, i.e., when user traffic suddenly peaks. We illustrate the effectiveness of this mechanism by using both trace driven simulations from production systems as well experiments on a real testbed.