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    • 6. 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。
    • 9. 发明授权
    • Systems and methods for tracking working-set estimates with a limited resource budget
    • 以有限的资源预算跟踪工作集估计的系统和方法
    • US08769202B1
    • 2014-07-01
    • US13198495
    • 2011-08-04
    • Gokul SoundararajanLakshmi Narayanan BairavasundaramVipul MathurKaladhar Voruganti
    • Gokul SoundararajanLakshmi Narayanan BairavasundaramVipul MathurKaladhar Voruganti
    • G06F12/00
    • G06F12/0802G06F12/0888G06F2212/6042
    • Embodiments of the systems and techniques described here can leverage several insights into the nature of workload access patterns and the working-set behavior to reduce the memory overheads. As a result, various embodiments make it feasible to maintain running estimates of a workload's cacheability in current storage systems with limited resources. For example, some embodiments provide for a method comprising estimating cacheability of a workload based on a first working-set size estimate generated from the workload over a first monitoring interval. Then, based on the cacheability of the workload, a workload cache size can be determined. A cache then can be dynamically allocated (e.g., change, possibly frequently, the cache allocation for the workload when the current allocation and the desired workload cache size differ), within a storage system for example, in accordance with the workload cache size.
    • 这里描述的系统和技术的实施例可以利用对工作负载访问模式和工作集行为的性质的几个见解,以减少内存开销。 因此,各种实施例使得可以在有限的资源的当前存储系统中维持工作负载的高速缓存的运行估计。 例如,一些实施例提供了一种方法,其包括基于在第一监视间隔上从工作负载产生的第一工作集大小估计来估计工作负载的可缓存性。 然后,基于工作负载的可缓存性,可以确定工作负载高速缓存大小。 然后可以根据工作负载高速缓存大小来动态地分配高速缓存(例如,当当前分配和期望的工作负载高速缓存大小不同时,可以频繁地改变工作负载的高速缓存分配),例如在存储系统内。