We propose CXL-ANNS, a software-hardware collaborative approach to enable highly
scalable approximate nearest neighbor search (ANNS) services. To this end, we first …

The emergence of ML in various cloud system management tasks (eg, workload autoscaling
and job scheduling) has become a core driver of ML-centric cloud platforms. However, there …

Flash caches are used to reduce peak backend load for throughput-constrained data center
services, reducing the total number of backend servers required. Bulk storage systems are a …

Emerging hybrid memory systems that comprise technologies such as Intel's Optane DC
Persistent Memory, exhibit disparities in the access speeds and capacity ratios of their …

An emerging trend in cloud deployments is to adopt machine learning (ML) models to
characterize end-to-end system performance. Despite early success, such methods can …

Driven by the increasing degree of automation, industrial production plants have become
very data reliant, which poses a great potential for machine learning applications. AutoML is …

We present Phronesis, a learning framework for efficiently modeling the performance of data
analytic workloads as a function of their high-dimensional software configuration …

Real-time inference is the key technology that enables a variety of latency-critical intelligent
services such as autonomous driving and augmented reality. Heterogeneous embedded …

To bridge the performance gap between network and storage, performance tuning of file
systems according to different workloads has become an important work on the Internet of …

We propose CXL-ANNS, a software-hardware collaborative approach to enable scalable
approximate nearest neighbor search (ANNS) services. To this end, we first disaggregate …