One of the main objectives of Cloud Providers (CP) is to guarantee the
Service-Level Agreement (SLA) of customers while reducing operating costs. To
achieve this goal, CPs have built large-scale datacenters. This leads, however,
to underutilized resources and an increase in costs. A way to improve the
utilization of resources is to reclaim the unused parts and resell them at a
lower price. Providing SLA guarantees to customers on reclaimed resources is a
challenge due to their high volatility. Some state-of-the-art solutions
consider keeping a proportion of resources free to absorb sudden variation in
workloads. Others consider stable resources on top of the volatile ones to fill
in for the lost resources. However, these strategies either reduce the amount
of reclaimable resources or operate on less volatile ones such as Amazon Spot
instance. In this paper, we proposed RISCLESS, a Reinforcement Learning
strategy to exploit unused Cloud resources. Our approach consists of 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 CPs' profits by an average of 15.9%
compared to state-of-the-art strategies. It also reduced the SLA violation time
by an average of 36.7% while increasing the amount of used ephemeral resources
by 19.5% on average

RISCLESS 提供一种使用强化学习策略来利用未使用云资源的方法，它通过使用小比例的稳定按需资源与临时资源相结合，以保证客户的 SLA 并降低成本，使云提供商的利润平均增加 15.9％，违反 SLA 的时间平均减少 36.7％，并平均增加 19.5％使用的临时资源。