Distributed computing remains inaccessible to a large number of users, in spite of many
open source platforms and extensive commercial offerings. While distributed computation …

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 …

Large-scale data analytics frameworks are shifting towards shorter task durations and larger
degrees of parallelism to provide low latency. Scheduling highly parallel jobs that complete …

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

Centralized datacenter schedulers can make high-quality placement decisions when
scheduling tasks in a cluster. Today, however, high-quality placements come at the cost of …

Shark is a new data analysis system that marries query processing with complex analytics
on large clusters. It leverages a novel distributed memory abstraction to provide a unified …

Large scale streaming systems aim to provide high throughput and low latency. They are
often used to run mission-critical applications, and must be available 24x7. Thus such …

Scaling model parameters improves model quality at the price of high computation
overhead. Sparsely activated models, usually in the form of Mixture of Experts (MoE) …

As clusters continue to grow in size and complexity, providing scalable and predictable
performance is an increasingly important challenge. A crucial roadblock to achieving …

In big data analytics, timely results, even if based on only part of the data, are often good
enough. For this reason, approximation jobs, which have deadline or error bounds and …