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    • 2. 发明授权
    • Modeler for predicting storage metrics
    • 用于预测存储指标的建模器
    • US08620921B1
    • 2013-12-31
    • US13016892
    • 2011-01-28
    • Sai Rama Krishna SusarlaKaladhar VorugantiVipul Mathur
    • Sai Rama Krishna SusarlaKaladhar VorugantiVipul Mathur
    • G06F17/30
    • G06N99/005G06F11/3409G06F17/30294G06F17/30587
    • Described herein is a system and method for dynamically managing service-level objectives (SLOs) for workloads of a cluster storage system. Proposed states/solutions of the cluster may be produced and evaluated to select one that achieves the SLOs for each workload. A planner engine may produce a state tree comprising nodes, each node representing a proposed state/solution. New nodes may be added to the state tree based on new solution types that are permitted, or nodes may be removed based on a received time constraint for executing a proposed solution or a client certification of a solution. The planner engine may call an evaluation engine to evaluate proposed states, the evaluation engine using an evaluation function that considers SLO, cost, and optimization goal characteristics to produce a single evaluation value for each proposed state. The planner engine may call a modeler engine that is trained using machine learning techniques.
    • 这里描述了用于动态管理用于集群存储系统的工作负载的服务级目标(SLO)的系统和方法。 可以生成和评估集群的建议状态/解决方案,以选择为每个工作负载实现SLO的状态/解决方案。 计划器引擎可以产生包括节点的状态树,每个节点表示提出的状态/解。 可以基于允许的新解决方案类型将新节点添加到状态树,或者可以基于接收到的时间约束来移除节点,以执行解决方案或解决方案的客户端认证。 计划器引擎可以调用评估引擎来评估提出的状态,评估引擎使用考虑SLO,成本和优化目标特征的评估函数,以产生每个建议状态的单个评估值。 计划器引擎可以调用使用机器学习技术训练的建模者引擎。