  Cache plays an important role to maintain high and stable performance (i.e.
high throughput, low tail latency and throughput jitter) in storage systems.
Existing rule-based cache management methods, coupled with engineers' manual
configurations, cannot meet ever-growing requirements of both time-varying
workloads and complex storage systems, leading to frequent cache overloading.
In this paper, we for the first time propose a light-weight learning-based
cache bandwidth control technique, called \LQoCo which can adaptively control
the cache bandwidth so as to effectively prevent cache overloading in storage
systems. Extensive experiments with various workloads on real systems show that
LQoCo, with its strong adaptability and fast learning ability, can adapt to
various workloads to effectively control cache bandwidth, thereby significantly
improving the storage performance (e.g. increasing the throughput by 10\%-20\%
and reducing the throughput jitter and tail latency by 2X-6X and 1.5X-4X,
respectively, compared with two representative rule-based methods).
