Abstract-Distributed storage systems often employ erasure codes to achieve high data reliability while attaining space efficiency. Such storage systems are known to be susceptible to long tails in response time. It has been shown that in modern online applications such as Bing, Facebook, and Amazon, the long tail of latency is of particular concern, with 99.9th percentile response times that are orders of magnitude worse than the mean. Taming tail latency is very challenging in erasure-coded storage systems since quantify tail latency (i.e., xth-percentile latency for arbitrary x ∈ [0, 1]) has been a long-standing open problem. In this paper, we propose a mathematical model to quantify tail index of service latency for arbitrary erasure-coded storage systems, by characterizing the asymptotic behavior of latency distribution tails. When file size has a heavy tailed distribution, we find tail index, defined as the exponent at which latency tail probability diminishes to zero, in closed-form, and further show that a family of probabilistic scheduling algorithms are (asymptotically) optimal since they are able to achieve the exact tail index.
The probabilistic scheduling has also been shown to be optimal for tail latency index when the file sizes are heavy-tailed REF .
Tail Index for a Distributed Storage System with Pareto File Size Distribution
ArXiv
ArXiv
Mathematics Computer Science