Related papers: SeSeMI: Secure Serverless Model Inference on Sensi…

Safety-critical designs need to ensure reliable operations under hostile conditions with a certain degree of confidence. The continuously higher complexity of these designs makes them more susceptible to the risk of failure. ISO26262…

Serverless computing abstracts infrastructure management but also obscures system-level behaviors that can introduce security risks. Prior work has shown that serverless platforms are vulnerable to attacks exploiting shared execution…

Serverless Function-as-a-Service (FaaS) platforms provide applications with resources that are highly elastic, quick to instantiate, accounted at fine granularity, and without the need for explicit runtime resource orchestration. This…

While the security of the cloud remains a concern, a common practice is to encrypt data before outsourcing them for utilization. One key challenging issue is how to efficiently perform queries over the ciphertext. Conventional crypto-based…

We introduce Version 2 of SPECI, a system for predictive simulation modeling of large-scale data-centres, i.e. warehouse-sized facilities containing hundreds of thousands of servers, as used to provide cloud services.

Integrity is critical for maintaining system security, as it ensures that only genuine software is loaded onto a machine. Although confidential virtual machines (CVMs) function within isolated environments separate from the host, it is…

ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives allow inference without revealing users' inputs to a model…

The rise of serverless computing provides an opportunity to rethink cloud security. We present an approach for securing serverless systems using a novel form of dynamic information flow control (IFC). We show that in serverless…

Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models. In line with this goal, we present consistency models for posterior…

Serverless computing has become an important model in cloud computing and influenced the design of many applications. Here, we provide our perspective on how the recent landscape of serverless computing for scientific applications looks…

Serverless computing is an emerging service model in distributed computing systems. The term captures cloud-based event-driven distributed application design and stems from its completely resource-transparent deployment model, i.e.…

Serverless computing has grown rapidly for serving Large Language Model (LLM) inference due to its pay-as-you-go pricing, fine-grained GPU usage, and rapid scaling. However, our analysis reveals that current serverless can effectively serve…

Developing accurate and extendable performance models for serverless platforms, aka Function-as-a-Service (FaaS) platforms, is a very challenging task. Also, implementation and experimentation on real serverless platforms is both costly and…

Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high…

Cloud-based serverless computing systems, either public or privately provisioned, aim to provide the illusion of infinite resources and abstract users from details of the allocation decisions. With the goal of providing a low cost and a…

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning…

Serverless computing offers an event driven pay-as-you-go framework for application development. A key selling point is the concept of no back-end server management, allowing developers to focus on application functionality. This is…

Cooperative inference across independently deployed machine learning models is increasingly desirable in distributed environments, as there is a growing need to leverage multiple models while keeping their data and model parameters private.…

Large language models (LLMs) have emerged as a powerful foundation for intelligent reasoning and decision-making, demonstrating substantial impact across a wide range of domains and applications. However, their massive parameter scales and…

Although serverless computing offers compelling cost and deployment simplicity advantages, a significant challenge remains in securely managing sensitive data as it flows through the network of ephemeral function executions in serverless…