Towards Inference Delivery Networks: Distributing Machine Learning with Optimality Guarantees

TL;DR

This paper introduces inference delivery networks (IDNs), a novel distributed system that optimally balances latency and accuracy for machine learning inference tasks across multiple infrastructure tiers.

Contribution

It proposes a new distributed policy for ML model allocation in IDNs with strong performance guarantees and practical improvements over existing heuristics.

Findings

The policy achieves near-optimal trade-offs between latency and accuracy.

IDNs outperform greedy heuristics in realistic scenarios.

Strong theoretical guarantees are provided in adversarial settings.

Abstract

An increasing number of applications rely on complex inference tasks that are based on machine learning (ML). Currently, there are two options to run such tasks: either they are served directly by the end device (e.g., smartphones, IoT equipment, smart vehicles), or offloaded to a remote cloud. Both options may be unsatisfactory for many applications: local models may have inadequate accuracy, while the cloud may fail to meet delay constraints. In this paper, we present the novel idea of inference delivery networks (IDNs), networks of computing nodes that coordinate to satisfy ML 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 dynamically updates its local set of inference models based on requests observed during the recent past plus limited information exchange with its neighboring nodes. Our policy offers strong performance guarantees in an adversarial setting and shows improvements over greedy heuristics with similar complexity in realistic scenarios.