论文标题
成本效益的MLAAS联合会：组合加强学习方法
Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach
论文作者
论文摘要
随着深度学习技术的发展，主要的云提供商和利基机器学习服务提供商开始向公众提供基于云的机器学习工具，也称为机器学习（MLAAS）。根据我们的测量，对于同一任务，由于专有数据集，模型等，来自不同提供商的这些MLAASE具有不同的性能。将不同的MLAASE联合在一起，使我们能够进一步提高分析性能。然而，由于引入可能的假阳性结果，不同MLAASE的天真汇总结果不仅会产生明显的瞬时成本，而且可能导致次优的性能增长。在本文中，我们提出了ARMOL，这是一个框架，以结合正确选择MLAAS提供商，以实现最佳的分析性能。我们首先设计一个单词分组算法，以统一不同提供商的输出标签。然后，我们提出了基于深层的增强学习的深度辅助，以最大程度地提高准确性，同时最大程度地降低成本。然后，使用精心选择的合奏策略将所选提供者的预测共同汇总。现实世界中的痕量驱动评估进一步表明，Armol能够以$ 67 \％$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $。
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.
