Autonomic computing investigates how systems can achieve (user) specified “control”
outcomes on their own, without the intervention of a human operator. Autonomic computing …

Over the past six decades, the computing systems field has experienced significant
transformations, profoundly impacting society with transformational developments, such as …

Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a
machine learning model. It has evolved over the last years and has started attracting many …

In the era of the IoT revolution, applications are becoming ever more sophisticated and
accompanied by diverse functional and non-functional requirements, including those related …

The rapid growth in the number of interconnected devices on the Internet (referred to as the
Internet of Things—IoT), along with the huge volume of data that are exchanged and …

In this paper, we present energy-aware scheduling for Serverless edge computing. Energy
awareness is critical since edge nodes, in many Internet of Things (IoT) domains, are meant …

The Industry 4.0 revolution has been made possible via AI-based applications (eg, for
automation and maintenance) deployed on the serverless edge (aka fog) computing …

This work introduces ECOLIFE, the first carbon-aware serverless function scheduler to co-
optimize carbon footprint and performance. ECOLIFE builds on the key insight of intelligently …

In recent years, serverless computing has received significant attention due to its innovative
approach to cloud computing. In this novel approach, a new payment model is presented …

In recent years, the adoption of serverless computing has surged due to the ease of
deployment, attractive pay-per-use pricing, and transparent horizontal auto-scaling. At the …