Sprout: A Functional Caching Approach to Minimize Service Latency in Erasure-Coded Storage

The rapid growth of data traffic in storage systems has put a significant burden on the underlying networks of cloud storage systems. Historically, a key solution to relieve this traffic burden is caching [1]. Many companies have adopted erasure-coded storage systems. However, caching for data centers when the files are encoded with an erasure code has not been studied to the best of our knowledge. This paper proposes a new functional caching approach called Sprout that can efficiently capitalize on existing file coding in erasure-coded storage systems. In contrast to exact caching that stores chunks identical to original copies, our functional caching approach forms d new data chunks, which together with the existing n chunks satisfy the property of being an (n + d, k) MDS code. Thus, the file can now be recovered from any out of n + d chunks (rather than k out of n under exact caching), effectively extending coding redundancy, as well system diversity for scheduling file access requests. The proposed functional caching approach saves latency due to more flexibility to obtain k-d chunks from the storage system a very minimal additional computational cost of creating the coded cached chunks. While quantifying service latency erasure-coded storage systems is an open problem, we generalize previous results on probabilistic scheduling policy [2,3] that distributes file requests to cache and storage nodes with optimized probabilities, and derive a closed-form upper bound on mean service latency for the proposed functional caching approach.