In this work we study the problem of using machine-learned predictions to improve
performance of online algorithms. We consider two classical problems, ski rental and non …

In online scenarios requests arrive over time, and each request must be serviced in an
irrevocable manner before the next request arrives. Online algorithms with advice is an area …

The k-server problem is perhaps the most influential online problem: natural, crisp, with a
surprising technical depth that manifests the richness of competitive analysis. The k-server …

The extension of classical online algorithms when provided with predictions is a new and
active research area. In this paper, we extend the primal-dual method for online algorithms …

Power consumption imposes a significant cost for data centers implementing cloud services,
yet much of that power is used to maintain excess service capacity during periods of low …

As technology continues to improve people's quality of life, there is a large, unfulfilled market
worldwide for on-demand meal delivery services. The competitive edge of the business is …

The most exciting development in parallel computer architecture is the convergence of
traditionally disparate approaches on a common machine structure. This book explains the …

The memory coherence problem in designing and implementing a shared virtual memory on
loosely coupled multiprocessors is studied in depth. Two classes of algorithms, centralized …

An on-line problem is one in which an algorithm must handle a sequence of requests,
satisfying each request without knowledge of the future requests. Examples of on-line …

Most shared-memory multiprocessor architectures provide hardware support for making
mutually exclusive accesses to shared data structures. Thii support usually consists of …