  This paper summarizes our work on characterizing application memory error
vulnerability to optimize datacenter cost via Heterogeneous-Reliability Memory
(HRM), which was published in DSN 2014, and examines the work's significance
and future potential. Memory devices represent a key component of datacenter
total cost of ownership (TCO), and techniques used to reduce errors that occur
on these devices increase this cost. Existing approaches to providing
reliability for memory devices pessimistically treat all data as equally
vulnerable to memory errors. Our key insight is that there exists a diverse
spectrum of tolerance to memory errors in new data-intensive applications, and
that traditional one-size-fits-all memory reliability techniques are
inefficient in terms of cost. This presents an opportunity to greatly reduce
server hardware cost by provisioning the right amount of memory reliability for
different applications.
  Toward this end, in our DSN 2014 paper, we make three main contributions to
enable highly-reliable servers at low datacenter cost. First, we develop a new
methodology to quantify the tolerance of applications to memory errors. Second,
using our methodology, we perform a case study of three new data-intensive
workloads (an interactive web search application, an in-memory key--value
store, and a graph mining framework) to identify new insights into the nature
of application memory error vulnerability. Third, based on our insights, we
propose several new hardware/software heterogeneous-reliability memory system
designs to lower datacenter cost while achieving high reliability and discuss
their trade-offs. We show that our new techniques can reduce server hardware
cost by 4.7% while achieving 99.90% single server availability.
