The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

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 …

Serverless computing (FaaS) has been extensively utilized for deep learning (DL) inference
due to the ease of deployment and pay-per-use benefits. However, existing FaaS platforms …

Compiler optimization plays an increasingly important role to boost the performance of
machine learning models for data processing and management. With increasingly complex …

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; …

Autoscaling systems provide means to automatically change the resources allocated to a
software system according to the incoming workload and its actual needs. Public cloud …

While (I) serverless computing is emerging as a popular form of cloud execution,
datacenters are going through major changes:(II) storage dissaggregation in the system …

In this paper we report our experiences from the migration of an AI model inference process,
used in the context of an E-health platform to the Function as a Service model. To that …

Parameter-efficient finetuning (PEFT) is a widely used technique to adapt large language
models for different tasks. Service providers typically create separate systems for users to …

Intelligent applications in several areas increasingly rely on big data solutions to improve
their efficiency, but the processing and management of big data incur high costs. Although …