riseless:一种强化学习策略以保证云上短暂和稳定资源的SLA

2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) Pub Date : 2022-03-01 DOI:10.1109/pdp55904.2022.00021

SidAhmed Yalles, Mohamed Handaoui, Jean-Emile Dartois, Olivier Barais, Laurent d'Orazio, Jalil Boukhobza

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引用次数: 3

摘要

在本文中，我们提出了risless，一种利用未使用的云资源的强化学习策略。我们的方法是使用一小部分稳定的按需资源和临时资源，以保证客户的SLA并降低总体成本。该方法决定何时以及分配多少稳定资源以满足客户需求。与过去的策略相比，risless使云提供商(CPs)的利润平均提高了15.9%。它还减少了36.7%的SLA违规时间，同时增加了19.5%的临时资源使用量。

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RISCLESS: A Reinforcement Learning Strategy to Guarantee SLA on Cloud Ephemeral and Stable Resources

In this paper, we propose RISCLESS, a Reinforcement Learning strategy to exploit unused Cloud resources. Our approach consists in using a small proportion of stable on-demand resources alongside the ephemeral ones in order to guarantee customers SLA and reduce the overall costs. The approach decides when and how much stable resources to allocate in order to fulfill customers’ demands. RISCLESS improved the Cloud Providers (CPs)’ profits by an average of 15.9% compared to past strategies. It also reduced the SLA violation time by 36.7% while increasing the amount of used ephemeral resources by 19.5%.

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2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)

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