2 citations · 3 across the 6 of their papers we have counts for
9 papers
Profiling the Energy Consumption of Serverless Functions with Joule Profiler
Jérémy Woirhaye, François Gibier, Anderson Andrei Da Silva +3
Cloud providers and customers have widely adopted serverless computing as a convenient paradigm for deploying and executing functions on demand. To do so, serverless platforms requ…
Roomie: Interference-Aware Colocation for Efficient Model Serving
Youssouph Faye, Francescomaria Faticanti, Shubham Jain +1
As demand for DNN inference grows, GPU capacity is increasingly oversubscribed, forcing operators to colocate multiple models on the same device in both cloud and edge deployments.…
Efficient and Optimal No-Regret Caching under Partial Observation
Younes Ben Mazziane, Francescomaria Faticanti, Sara Alouf +1
Online learning algorithms have been successfully used to design caching policies with sublinear regret in the total number of requests, with no statistical assumption about the re…
Optimal Flow Admission Control in Edge Computing via Safe Reinforcement Learning
A. Fox, F. De Pellegrini, F. Faticanti +2
With the uptake of intelligent data-driven applications, edge computing infrastructures necessitate a new generation of admission control algorithms to maximize system performance…
Optimistic Online Caching for Batched Requests
Francescomaria Faticanti, Giovanni Neglia
In this paper we study online caching problems where predictions of future requests, e.g., provided by a machine learning model, are available. Typical online optimistic policies a…
No-Regret Caching with Noisy Request Estimates
Younes Ben Mazziane, Francescomaria Faticanti, Giovanni Neglia +1
Online learning algorithms have been successfully used to design caching policies with regret guarantees. Existing algorithms assume that the cache knows the exact request sequence…
Federated Learning under Heterogeneous and Correlated Client Availability
Angelo Rodio, Francescomaria Faticanti, Othmane Marfoq +2
The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collabora…
A Cost-Effective Workload Allocation Strategy for Cloud-Native Edge Services
Valentino Armani, Francescomaria Faticanti, Silvio Cretti +2
Nowadays IoT applications consist of a collection of loosely coupled modules, namely microservices, that can be managed and placed in a heterogeneous environment consisting of priv…
Cutting Throughput on the Edge:App-Aware Placement in Fog Computing
Francescomaria Faticanti, Francesco De Pellegrini, Domenico Siracusa +2
Fog computing extends cloud computing technology to the edge of the infrastructure to let IoT applications access objects' data with reduced latency, location awareness and dynamic…