Superior task scheduling scheme is able to improve the performance in achieving shorter
task completion time in multi-processor computing system. Large scale applications are …

We consider the problem of scheduling jobs on unrelated machines so as to minimize the
sum of weighted completion times. Our main result is a (3/2-c)-approximation algorithm for …

This paper proposes HeteroG, an automatic module to accelerate deep neural network
training in heterogeneous GPU clusters. To train a deep learning model with large amounts …

Distributed machine learning (ML) has played a key role in today's proliferation of AI
services. A typical model of distributed ML is to partition training datasets over multiple …

Distributed machine learning (ML) has played a key role in today's proliferation of AI
services. A typical model of distributed ML is to partition training datasets over multiple …

We study the data offloading problem in space-air-ground integrated networks (SAGINs) by
jointly optimizing task scheduling and power control to balance the total energy consumption …

Applications in data-parallel computing typically consist of multiple stages. In each stage, a
set of intermediate parallel data flows (Coflow) is produced and transferred between servers …

We study the problem of scheduling an arbitrary computational DAG on a fixed number of
processors while minimizing the makespan. While previous works have mostly studied this …

We revisit two well-studied scheduling problems in the unrelated machines setting where
each job can have a different processing time on each machine. For minimizing total …

This paper establishes performance guarantees for online algorithms that schedule
stochastic, nonpreemptive jobs on unrelated machines to minimize the expected total …