With the increasing popularity of large deep learning model-serving workloads, there is a
pressing need to reduce the energy consumption of a model-serving cluster while …

To mitigate climate change, we must reduce carbon emissions from hyperscale cloud
computing. We find that cloud compute servers cause the majority of emissions in a general …

Current serverless platforms struggle to optimize resource utilization due to their dynamic
and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall …

The emergence of ML in various cloud system management tasks (eg, workload autoscaling
and job scheduling) has become a core driver of ML-centric cloud platforms. However, there …

Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …

Autoscaling is a critical mechanism in cloud computing, enabling the autonomous
adjustment of computing resources in response to dynamic workloads. This is particularly …

Multi-agent reinforcement learning (MARL) has primarily focused on solving a single task in
isolation, while in practice the environment is often evolving, leaving many related tasks to …

Microservice has become a de facto standard for building large-scale cloud applications.
Overload control is essential in preventing microservice failures and maintaining system …

Serverless computing provides developers with a maintenance-free approach to resource
usage, but it also transfers resource management responsibility to the cloud platform …

Service Level Objectives (SLOs) aim to set threshold for service time in cloud services to
ensure acceptable quality of service (QoS) and user satisfaction. Currently, many studies …