Unlike conventional CPU caches,
non-datapath caches, such as host-side flash caches which are extensively used as storage caches, have distinct requirements. While every cache miss results in a cache update in a conventional cache, non-datapath caches allow for the flexibility of
selective
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Unlike conventional CPU caches,
non-datapath caches, such as host-side flash caches which are extensively used as storage caches, have distinct requirements. While every cache miss results in a cache update in a conventional cache, non-datapath caches allow for the flexibility of
selective caching, i.e., the option of not having to update the cache on each miss. We propose a new, generalized, bimodal caching algorithm,
Fear Of Missing Out (
FOMO), for managing non-datapath caches. Being generalized has the benefit of allowing any datapath cache replacement policy, such as LRU, ARC, or LIRS, to be augmented by
FOMO to make these datapath caching algorithms better suited for non-datapath caches. Operating in two states,
FOMO is selective—it selectively disables cache insertion and replacement depending on the learned
behavior of the workload.
FOMO is lightweight and tracks inexpensive metrics in order to identify these workload
behaviors effectively.
FOMO is evaluated using three different cache replacement policies against the current state-of-the-art non-datapath caching algorithms, using five different storage system workload repositories (totaling 176 workloads) for six different cache size configurations, each sized as a percentage of each workload’s footprint. Our extensive experimental analysis reveals that
FOMO can improve upon other non-datapath caching algorithms across a range of production storage workloads, while also reducing the write rate.
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