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 …

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN)
inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of …

Although serverless computing is a popular paradigm, current serverless environments have
high overheads. Recently, it has been shown that serverless workloads frequently exhibit …

Recent advances in deep learning (DL) have spawned various intelligent cloud services
with well-trained DL models. Nevertheless, it is nontrivial to maintain the desired end-to-end …

Serverless Function-as-a-Service (FaaS) offers improved programmability for customers, yet
it is not server-" less" and comes at the cost of more complex infrastructure management (eg …

The wide adoption of Cloud Native shifts the boundary between cloud users and CSPs
(Cloud Service Providers) from VM-based infrastructure to container-based applications …

In recent years, serverless computing has garnered significant attention owing to its high
scalability, pay-as-you-go billing model, and efficient resource management provided by …

Serverless ML inference is an emerging cloud computing paradigm for low-cost, easy-to-
manage inference services. In serverless ML inference, each call is executed in a container; …

As Internet of Things (IoT) keeps growing, IoT-side intelligence services, such as intelligent
personal assistant, healthcare surveillance, and smart home service, offload more and more …

Field-Programmable Gate Arrays (FPGAs) are more energy efficient and cost effective than
CPUs for a wide variety of datacenter applications. Yet, for latency-sensitive and bursty …