ReLeaSER：一种优化短暂云资源利用的强化学习策略
ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources
摘要：
云数据中心容量过度配置，以应对需求高峰和硬件故障，导致资源利用率低。提高资源利用率并因此降低总拥有成本的一种方式是以较低的价格提供未使用的资源（称为短暂资源）。但是，转售资源需要满足其客户在服务质量方面的期望。我们的目标是最大化回收资源的数量，同时避免SLA惩罚。为了实现这一目标，云提供商必须估计其未来的利用率，以提供可用性保证。预测应该考虑资源的安全裕度，以应对不可预测的工作负载。面临的挑战是找到安全余量，在回收的资源量和违反SLA的风险之间提供最佳权衡。大多数现有技术的解决方案考虑用于所有类型的度量（例如，CPU、RAM）。然而，唯一的固定余量不考虑随着时间的推移可能导致SLA违规或/和利用率低下的各种工作负载变化。为了应对这些挑战，我们提出了ReLeaSER，一种强化学习策略，用于优化云中临时资源的利用率。ReLeaSER在主机级别为每个资源度量动态调整安全裕度。该策略从过去的预测错误（导致SLA违规）中学习。我们的解决方案将SLA违规处罚平均降低了2.7倍，最高可降低3.4倍。它还大大提高了CP的潜在节省，平均节省27.6%，最高可达43.6%。
英文摘要：
Cloud data center capacities are over-provisioned to handle demand peaks and hardware failures which leads to low resources' utilization. One way to improve resource utilization and thus reduce the total cost of ownership is to offer unused resources (referred to as ephemeral resources) at a lower price. However, reselling resources needs to meet the expectations of its customers in terms of Quality of Service. The goal is so to maximize the amount of reclaimed resources while avoiding SLA penalties. To achieve that, cloud providers have to estimate their future utilization to provide availability guarantees. The prediction should consider a safety margin for resources to react to unpredictable workloads. The challenge is to find the safety margin that provides the best trade-off between the amount of resources to reclaim and the risk of SLA violations. Most state-of-the-art solutions consider a fixed safety margin for all types of metrics (e.g., CPU, RAM). However, a unique fixed margin does not consider various workloads variations over time which may lead to SLA violations or/and poor utilization. In order to tackle these challenges, we propose ReLeaSER, a Reinforcement Learning strategy for optimizing the ephemeral resources' utilization in the cloud. ReLeaSER dynamically tunes the safety margin at the host-level for each resource metric. The strategy learns from past prediction errors (that caused SLA violations). Our solution reduces significantly the SLA violation penalties on average by 2.7x and up to 3.4x. It also improves considerably the CPs' potential savings by 27.6% on average and up to 43.6%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
