With the fast development of big data systems in recent years, a variety of open-source
benchmarks have been built to evaluate and compare the workloads on these systems, and …

Codes are widely used in many engineering applications to offer robustness against noise.
In large-scale systems, there are several types of noise that can affect the performance of …

Machine learning inference is becoming a core building block for interactive web
applications. As a result, the underlying model serving systems on which these applications …

Memory-intensive applications suffer large performance loss when their working sets do not
fully fit in memory. Yet, they cannot leverage otherwise unused remote memory when paging …

Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

Cloud computing promises flexibility and high performance for users and high cost-efficiency
for operators. Nevertheless, most cloud facilities operate at very low utilization, hurting both …

There has been much research devoted to improving the performance of data analytics
frameworks, but comparatively little effort has been spent systematically identifying the …

Low latency analytics on geographically distributed datasets (across datacenters, edge
clusters) is an upcoming and increasingly important challenge. The dominant approach of …

Sprocket is a highly configurable, stage-based, scalable, serverless video processing
framework that exploits intra-video parallelism to achieve low latency. Sprocket enables …

Federated learning enables training a global model from data located at the client nodes,
without data sharing and moving client data to a centralized server. Performance of …