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Decision-Making Approaches for Performance QoS in Distributed Storage Systems: A Survey

机译:分布式存储系统中性能QoS的决策方法:一项调查

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Distributed storage systems designed to offer explicit performance quality-of-service (QoS) guarantees must regulate the allocation and use of resources to achieve a user-specified level of service. QoS-driven systems employ decision-making techniques to decide on appropriate actions to take during initial deployment or under variations in workload and/or system configuration. In this survey we cover both traditional approaches to decision-making for explicit performance QoS (control theory, multi-dimensional constrained optimization, policy-based techniques) as well as more recent approaches based on machine-learning, offering a broad perspective to the state-of-the-art in the field. As performance prediction is a central concept in decision-making, we also summarize research on performance prediction techniques used in this context.
机译:旨在提供明确的性能服务质量(QoS)保证的分布式存储系统必须规范资源的分配和使用,以实现用户指定的服务水平。 QoS驱动的系统采用决策技术来决定在初始部署期间或在工作负载和/或系统配置变化下要采取的适当措施。在本次调查中,我们涵盖了用于明确性能QoS的传统决策方法(控制理论,多维约束优化,基于策略的技术),以及基于机器学习的最新方法,从而为国家提供了广阔的前景最先进的领域。由于绩效预测是决策中的核心概念,因此我们也总结了在这种情况下使用的绩效预测技术的研究。

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