The high computational and memory requirements of generative large language models
(LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary …

Deep neural networks (DNNs) are becoming progressively large and costly to train. This
paper aims to reduce DNN training costs by leveraging preemptible instances on modern …

With the rapid development of cloud systems, an increasing number of service workloads
are deployed in the private cloud and/or public cloud. Although large cloud providers such …

The continued exponential growth of cloud datacenter capacity has increased awareness of
the carbon emissions when executing large compute-intensive workloads. To reduce carbon …

Public cloud computing providers offer a surplus of computing resources at a lower price
with a service of a spot instance. Despite the possible great cost savings from using spot …

Cloud computing provides a variety of distinct computing resources on demand. Supporting
live migration in the cloud can be beneficial to dynamically build a reliable and cost-optimal …

Microservices have gained popularity as an architectural approach for developing scalable
and modular applications. Traditionally, microservice deployment relies on virtual machines …

In an era where digital infrastructure increasingly relies on cloud computing, the need for
flexible workload migration across clouds has become crucial. This need is particularly …

Recent years have witnessed an explosive growth of large AI models, characterized by the
high cost of hosting AI services, and their demanding service requirement. We explore two …