It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

Modern society is increasingly moving toward complex and distributed computing systems.
The increase in scale and complexity of these systems challenges O&M teams that perform …

Disk drives are one of the most commonly replaced hardware components and continue to
pose challenges for accurate failure prediction. In this work, we present analysis and …

Disk failure has become a major concern with the rapid expansion of storage systems in
data centers. Based on SMART (Self-Monitoring, Analysis and Reporting Technology) …

High service availability is crucial for cloud systems. A typical cloud system uses a large
number of physical hard disk drives. Disk errors are one of the most important reasons that …

Modern datacenters increasingly use flash-based solid state drives (SSDs) for high
performance and low energy cost. However, SSD introduces more complex failure modes …

Large-scale cluster storage systems typically consist of a heterogeneous mix of storage
devices with significantly varying failure rates. Despite such differences among devices …

Solid state drives (SSDs) play an important role in large-scale data centers. SSD failures
affect the stability of storage systems and cause additional maintenance overhead. To …

AIOps (Artificial Intelligence for IT Operations) leverages machine learning models to help
practitioners handle the massive data produced during the operations of large-scale …

Large-scale cluster storage systems use redundancy (via erasure coding) to ensure data
durability. Disk-adaptive redundancy—dynamically tailoring the redundancy scheme to …