Dynamic resource distribution using periodicity-aware predictive modeling
First Claim
1. A method, comprising:
- maintaining, at a storage pool, measurements corresponding to prior usage of a distributed storage resource of a cluster by a plurality of nodes in the cluster, the storage pool comprising an aggregation of one or more storage devices directly attached to respective nodes in the cluster;
receiving, at a first node of the plurality of nodes in the cluster, the measurements from the storage pool;
determining, at the first node, a time period for a training window corresponding to a portion of the measurements, wherein the training window bounds a periodically recurring portion of the measurements;
training, at the first node, a predictive model based at least in part on the periodically recurring portion of the measurements to produce a trained predictive model;
determining a resource allocation operation based at least in part on a prediction from the trained predictive model.
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Abstract
Resource allocation techniques for distributed data storage. A set of distributed storage system historical resource usage measurements are collected and stored using distributed storage system measurement techniques. The resource usage metrics are associated with and/or derived from processing entities in the distributed storage computing system. An analysis module determines a training window time period corresponding to a portion of the collected distributed storage system historical resource usage measurements. The training window time period is determined so as to provide an earlier time boundary and a later time boundary that defines a periodically recurring portion of the distributed storage system historical resource usage measurements. A latest cycle of those periodically recurring measurements are then used to train a predictive model, which in turn is used to produce distributed storage system predicted resource usage characteristics. Resource allocation decisions are made based at least in part on predictions from the trained predictive model.
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Citations
20 Claims
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1. A method, comprising:
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maintaining, at a storage pool, measurements corresponding to prior usage of a distributed storage resource of a cluster by a plurality of nodes in the cluster, the storage pool comprising an aggregation of one or more storage devices directly attached to respective nodes in the cluster; receiving, at a first node of the plurality of nodes in the cluster, the measurements from the storage pool; determining, at the first node, a time period for a training window corresponding to a portion of the measurements, wherein the training window bounds a periodically recurring portion of the measurements; training, at the first node, a predictive model based at least in part on the periodically recurring portion of the measurements to produce a trained predictive model; determining a resource allocation operation based at least in part on a prediction from the trained predictive model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A non-transitory computer readable medium having stored thereon a sequence of instructions which, when executed by a processor performs a set of acts comprising:
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maintaining, at a storage pool, measurements corresponding to prior usage of a distributed storage resource of a cluster by a plurality of nodes in the cluster, the storage pool comprising an aggregation of one or more storage devices directly attached to respective nodes in the cluster; receiving, at a first node of the plurality of nodes in the cluster, the measurements from the storage pool; determining, at the first node, a time period for a training window corresponding to a portion of the measurements, wherein the training window bounds a periodically recurring portion of the measurements; training, at the first node, a predictive model based at least in part on the periodically recurring portion of the measurements to produce a trained predictive model; and determining a resource allocation operation based at least in part on a prediction from the trained predictive model. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A system, comprising:
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a storage medium having stored thereon a sequence of instructions; and a processor that executes the sequence of instructions to perform a set of acts comprising; maintaining, at a storage pool, measurements corresponding to prior usage of a distributed storage resource of a cluster by a plurality of nodes in the cluster, the storage pool comprising an aggregation of one or more storage devices directly attached to respective nodes in the cluster; receiving, at a first node of the plurality of nodes in the cluster, the measurements from the storage pool; determining, at the first node, a time period for a training window corresponding to a portion of the measurements, wherein the training window bounds a periodically recurring portion of the measurements; training, at the first node, a predictive model based at least in part on the periodically recurring portion of the measurements to produce a trained predictive model; and determining a resource allocation operation based at least in part on a prediction from the trained predictive model. - View Dependent Claims (20)
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Specification