Disk and memory faults are the leading causes of server breakdown. A proactive solution is
to predict such hardware failure at the runtime and then isolate the hardware at risk and …

DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and
sustainable service of large-scale data centers. However, limited work has been done on …

Dynamic Random Access Memory (DRAM) errors are prevalent and lead to server failures
in production data centers. However, little is known about the correlation between DRAM …

Today's systems have diverse needs that are difficult to address using one-size-fits-all
commodity DRAM. Unfortunately, although system designers can theoretically adapt …

Storage, as a core function and fundamental component of computers, provides services for
saving and reading digital data. The increasing complexity of data operations and storage …

This paper presents and evaluates a method to predict DRAM uncorrected errors, a leading
cause of hardware failures in large-scale HPC clusters. The method uses a random forest …

Uncorrectable memory errors are one of the major failure causes in datacenters. In this
paper, we present an empirical study correlating correctable errors (CEs) and uncorrectable …

In large-scale datacenters, memory failure is one of the leading causes of server crashes,
and uncorrectable error (UCE) is the major fault type indicating defects of memory modules …

Uncorrectable memory errors are the leading causes of server failures in datacenters.
Predicting uncorrectable errors (UEs) using the historical correctable error (CE) information …

The aggressive scaling of technology may have helped to meet the growing demand for
higher memory capacity and density, but has also made DRAM cells more prone to errors …