DCAIApr 28, 2022

RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources

arXiv:2205.08350v1h-index: 31
Originality Incremental advance
AI Analysis

This work addresses cost reduction and SLA guarantees for cloud providers by better exploiting volatile resources, representing an incremental improvement over existing strategies.

The paper tackles the problem of underutilized cloud resources by proposing RISCLESS, a reinforcement learning strategy that uses a small proportion of stable resources alongside ephemeral ones to guarantee SLAs and reduce costs, resulting in a 15.9% average profit increase, 36.7% reduction in SLA violation time, and 19.5% increase in ephemeral resource usage compared to state-of-the-art methods.

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

Foundations

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