Online algorithms with predictions is a popular and elegant framework for bypassing
pessimistic lower bounds in competitive analysis. In this model, online algorithms are …

There has been recent interest in using machine-learned predictions to improve the worst-
case guarantees of online algorithms. In this paper we continue this line of work by studying …

Recent work has shown that leveraging learned predictions can improve the running time of
algorithms for bipartite matching and similar combinatorial problems. In this work, we build …

An important goal of modern scheduling systems is to efficiently manage power usage. In
energy-efficient scheduling, the operating system controls the speed at which a machine is …

We consider non-clairvoyant scheduling with online precedence constraints, where an
algorithm is oblivious to any job dependencies and learns about a job only if all of its …

Improving algorithms via predictions is a very active research topic in recent years. This
paper initiates the systematic study of mechanism design in this model. In a number of well …

In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a
priori unknown processing requirements with the objective to minimize the total (weighted) …

Packet buffers in datacenter switches are shared across all the switch ports in order to
improve the overall throughput. The trend of shrinking buffer sizes in datacenter switches …

We consider the classic online problem of scheduling on a single machine to minimize total
flow time. In STOC 2021, the concept of robustness to distortion in processing times was …

This paper presents an overview of scheduling research done over the last half century. The
main focus is on what is typically referred to as machine scheduling. The first section …