Automatic datacenter state summarization
First Claim
1. A method comprising:
- receiving a context graph indicating a plurality of relationships among a plurality of nodes corresponding to components of a datacenter, each node comprising properties corresponding to a particular component of the datacenter;
identifying one or more subgraphs from the context graph, wherein each subgraph is a portion of the context graph that is determined to be a relevant region associated with the datacenter;
for each subgraph identified as a relevant region in the context graph, determining whether each subgraph identified as a relevant region corresponds to a relevance condition and lacks an annotation describing the relevance condition;
determining, for each subgraph identified as a relevant region, a plurality of context hashes based on selected properties, the context hashes generated by applying a hash function to each subgraph;
comparing the plurality of context hashes to a plurality of subgraph hashes derived from a library of subgraphs to determine a set of subgraph hashes that are similar to the plurality of context hashes derived for each relevant region;
identifying annotations for each subgraph identified as a relevant region by referencing corresponding subgraph hashes in the set of subgraph hashes;
combining the annotations in accordance with an importance into an annotation for each relevant region; and
annotating the context graph with the combined annotations, the combined annotations describing the datacenter as a whole with a textual description.
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Abstract
In a datacenter setting, annotations or descriptions of relevant parts or subgraphs corresponding to components in the datacenter are predicted. Given a set of training data (library of subgraphs seen in the past labeled with a textual description explaining why were they considered relevant enough to be placed in the historical database), the recurrent neural network (RNN) learns how to combine the different textual annotations coming from each relevant region into a single annotation that describes the whole system. Accordingly, given a set of input or test data (datacenter state modeled a context graph that is not annotated), the system determines which regions of the input graph are more relevant, and for each of these regions, the RNN predicts an annotation even in a previously unseen or different datacenter infrastructure.
21 Citations
20 Claims
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1. A method comprising:
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receiving a context graph indicating a plurality of relationships among a plurality of nodes corresponding to components of a datacenter, each node comprising properties corresponding to a particular component of the datacenter; identifying one or more subgraphs from the context graph, wherein each subgraph is a portion of the context graph that is determined to be a relevant region associated with the datacenter; for each subgraph identified as a relevant region in the context graph, determining whether each subgraph identified as a relevant region corresponds to a relevance condition and lacks an annotation describing the relevance condition; determining, for each subgraph identified as a relevant region, a plurality of context hashes based on selected properties, the context hashes generated by applying a hash function to each subgraph; comparing the plurality of context hashes to a plurality of subgraph hashes derived from a library of subgraphs to determine a set of subgraph hashes that are similar to the plurality of context hashes derived for each relevant region; identifying annotations for each subgraph identified as a relevant region by referencing corresponding subgraph hashes in the set of subgraph hashes; combining the annotations in accordance with an importance into an annotation for each relevant region; and annotating the context graph with the combined annotations, the combined annotations describing the datacenter as a whole with a textual description. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method comprising:
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receiving historical metrics from a plurality of historical nodes for relevance conditions seen by historical datacenters; aggregating the historical metrics into historical vector representations for the plurality of historical nodes in the historical datacenter, the historical vector representations including information derived from the historical metrics of neighbors of the historical nodes and having annotations describing relevance conditions seen by the historical datacenters; training a classifier based on the historical vector representations and annotations; and predicting an annotation for a datacenter represented by a context graph indicating a plurality of relationships among a plurality of nodes corresponding to components of the datacenter, the context graph having a subgraph identified as a relevant region corresponding to a relevance condition not having an annotation describing the relevance condition with a textual description, wherein the classifier identifies similar subgraphs to one or more context subgraphs derived from the context graph and annotations corresponding to the similar subgraphs. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A computerized system comprising:
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a processor; and a non-transitory computer storage medium storing computer-useable instructions that, when used by the processor, cause the system to; receive a context graph indicating a plurality of relationships among a plurality of nodes corresponding to components of a datacenter, each node comprising properties corresponding to a particular component of the datacenter; identifying one or more relevant regions from the context graph, wherein each relevant region is a portion of the context graph and is associated with the datacenter; for each relevant region of the context graph, determining whether each relevant region identified corresponds to a relevance condition and lacks an annotation describing the relevance condition; determine a plurality of context hashes for each relevant region based on selected properties of a node of the plurality of nodes associated with the relevant region; compare the plurality of context hashes to a plurality of subgraph hashes derived from a library of subgraphs to determine a set of subgraph hashes that are similar to the plurality of context hashes corresponding to each relevant region; identify annotations corresponding to the set of subgraph hashes for each relevant region; and annotate the node with one or more combined annotations, the combined annotations describing the relevance conditions of the datacenter at a particular state with a textual description.
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Specification