Towards inference delivery networks: distributing machine learning with optimality guarantees - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Towards inference delivery networks: distributing machine learning with optimality guarantees

Abstract

We present the novel idea of inference delivery networks (IDN), networks of computing nodes that coordinate to satisfy inference requests achieving the best trade-off between latency and accuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud). We propose a distributed dynamic policy for ML model allocation in an IDN by which each node periodically updates its local set of inference models based on requests observed during the recent past plus limited information exchange with its neighbor nodes. Our policy offers strong performance guarantees in an adversarial setting and shows improvements over greedy heuristics with similar complexity in realistic scenarios.
Fichier principal
Vignette du fichier
sisalem21medcomnet.pdf (517.87 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03376168 , version 1 (13-10-2021)

Identifiers

Cite

Tareq Si Salem, Gabriele Castellano, Giovanni Neglia, Fabio Pianese, Andrea Araldo. Towards inference delivery networks: distributing machine learning with optimality guarantees. MEDCOMNET 2021 - 19th Mediterranean Communication and Computer Networking Conference, Jun 2021, Ibiza (virtual), Spain. pp.1-8, ⟨10.1109/MedComNet52149.2021.9501272⟩. ⟨hal-03376168⟩
75 View
75 Download

Altmetric

Share

Gmail Facebook X LinkedIn More