Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach

TL;DR

This paper introduces Armol, a reinforcement learning framework that optimally federates multiple MLaaS providers to enhance accuracy while significantly reducing inference costs.

Contribution

It presents a novel combinatorial reinforcement learning approach for selecting and aggregating MLaaS providers to optimize performance and cost efficiency.

Findings

Achieves 67% reduction in inference cost while maintaining accuracy.

Develops a word grouping algorithm for label unification across providers.

Demonstrates effectiveness through real-world trace-driven evaluation.

Abstract

With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public. According to our measurement, for the same task, these MLaaSes from different providers have varying performance due to the proprietary datasets, models, etc. Federating different MLaaSes together allows us to improve the analytic performance further. However, naively aggregating results from different MLaaSes not only incurs significant momentary cost but also may lead to sub-optimal performance gain due to the introduction of possible false-positive results. In this paper, we propose Armol, a framework to federate the right selection of MLaaS providers to achieve the best possible analytic performance. We first design a word grouping algorithm to unify the output labels across different providers. We then present a deep combinatorial reinforcement learning based-approach to maximize the accuracy while minimizing the cost. The predictions from the selected providers are then aggregated together using carefully chosen ensemble strategies. The real-world trace-driven evaluation further demonstrates that Armol is able to achieve the same accuracy results with $67\%$ less inference cost.