  As consumers are increasingly engaged in social networking and E-commerce
activities, businesses grow to rely on Big Data analytics for intelligence, and
traditional IT infrastructures continue to migrate to the cloud and edge, these
trends cause distributed data storage demand to rise at an unprecedented speed.
Erasure coding has seen itself quickly emerged as a promising technique to
reduce storage cost while providing similar reliability as replicated systems,
widely adopted by companies like Facebook, Microsoft and Google. However, it
also brings new challenges in characterizing and optimizing the access latency
when erasure codes are used in distributed storage. The aim of this monograph
is to provide a review of recent progress (both theoretical and practical) on
systems that employ erasure codes for distributed storage.
  In this monograph, we will first identify the key challenges and taxonomy of
the research problems and then give an overview of different approaches that
have been developed to quantify and model latency of erasure-coded storage.
This includes recent work leveraging MDS-Reservation, Fork-Join, Probabilistic,
and Delayed-Relaunch scheduling policies, as well as their applications to
characterize access latency (e.g., mean, tail, asymptotic latency) of
erasure-coded distributed storage systems. We will also extend the problem to
the case when users are streaming videos from erasure-coded distributed storage
systems. Next, we bridge the gap between theory and practice, and discuss
lessons learned from prototype implementation. In particular, we will discuss
exemplary implementations of erasure-coded storage, illuminate key design
degrees of freedom and tradeoffs, and summarize remaining challenges in
real-world storage systems such as in content delivery and caching. Open
problems for future research are discussed at the end of each chapter.
