In a datacenter setting, future states of nodes of a datacenter (each node representing a component of the datacenter in a context graph) are predicted. Initially, historical metrics collected from the datacenter nodes, as well as historical metrics of neighboring nodes. The metrics are aggregated into historical metric summary vector representations of the nodes which are utilized to train a future state predictor to predict future datacenter states. Once trained, metrics may be input into the future state predictor and the future state predictor may be utilized to predict a future state of one or more of the nodes of the datacenter.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving historical metrics from a plurality of historical nodes of a historical datacenter, the plurality of historical nodes corresponding to a historical context graph indicating a plurality of relationships among the plurality of historical nodes, each historical node corresponding to a component of the historical datacenter; aggregating the historical metrics into historical metric summary vector representations for the plurality of historical nodes in the historical datacenter, the historical metric summary vector representations including information derived from the historical metrics of neighbors of the plurality of historical nodes; and training a future state predictor with the historical metric summary vector representations to predict a future state of a node of an input datacenter. 2. The method of claim 1, wherein the historical metrics comprise one or more of a temperature of one or more components of the historical datacenter, a work load of one or more components of the historical datacenter, network usage, input/output (IO) operations, a functionality of a node, or processor capacity. 3. The method of claim 1, further comprising: concatenating a node state pattern vector to each historical metric summary vector to generate a plurality of enhanced historical vector representations; and training the future state predictor with the enhanced historical vector representations to predict the future state of the node of the input datacenter. 4. The media of claim 1, further comprising receiving metrics corresponding to a plurality of nodes of the input datacenter. 5. The media of claim 4, further comprising aggregating the metrics into a metric summary vector representation for a node of the plurality of nodes of the input datacenter, the vector representation corresponding to a time-window prior to a future state and including information derived from the metrics of neighbors of the node. 6. The method of claim 5, utilizing the future state predictor, predicting the future state of the node. 7. The method of claim 1, wherein the historical metrics and the metrics are received from different datacenters. 8. The method of claim 1, wherein the future state predictor includes a first neural network that processes each vector representation of prior and current states. 9. The method of claim 8, further comprising, upon processing the vector representation of a current state, transferring a resulting pattern to a second neural network that generates a sequence of predicted states, the resulting pattern identifying vector representations having similar conditions to the current state and corresponding to subgraphs that, when aggregated, are similar to a portion of the context graph. 10. The method of claim 6, further comprising providing the future state of the node to a decision support system. 11. The method of claim 10, further comprising receiving a recommendation from the decision support system, the recommendation comprising taking a particular action or taking no action. 12. The method of claim 6, further comprising providing the future state of the node to a classifier that predicts an anomaly or failure of the node. 13. A method comprising: receiving metrics from a plurality of nodes in a datacenter, the plurality of nodes corresponding to a context graph indicating a plurality of relationships among the plurality of nodes, each node corresponding to a component of the datacenter; aggregating the metrics into a metric summary vector representation for a node of the plurality of nodes in the datacenter, the vector representation corresponding to a time-window prior to a future state and including information derived from the metrics of neighbors of the node; and utilizing a future state predictor that has been trained to predict futures states of the node, predicting a future state of the node. 14. The method of claim 13, further comprising concatenating a node state pattern vector to the metric summary vector to generate an enhanced vector representation. 15. The method of claim 14, further comprising providing the future state of the node to a decision support system. 16. The system of claim 15, further comprising receiving a recommendation from the decision support system. 17. The system of claim 16, wherein the recommendation includes taking a particular action including purchasing a new component for the datacenter or powering off a component in the datacenter. 18. The system of claim 16, wherein the recommendation includes taking no action. 19. The method of claim 14, further comprising providing the future state of the node to a classifier that predicts an anomaly or failure. 20. A computerized system: a processor; and a non-transitory computer storage medium storing computer-useable instructions that, when used by the processor, cause the processor to: receive historical metrics from a plurality of historical nodes in a historical datacenter corresponding to a historical context graph indicating a plurality of relationships among the plurality of historical nodes corresponding to components of the historical datacenter; aggregate the historical metrics into historical metric summary vector representations for the plurality of historical nodes in the historical datacenter, the historical metric summary vector representations including information derived from the historical metrics of neighbors of the plurality of historical nodes; train a future state predictor with the historical metric summary vector representations to predict a future state of a node for an input datacenter; receive metrics from a plurality of nodes in the datacenter corresponding to a time-window prior to the future state; aggregate the metrics into a metric summary vector representation for the node of the plurality of nodes in the datacenter, the metric summary vector representation corresponding to the time-window prior to the future state and including information derived from the metrics of neighbors of the node; and utilizing the future state predictor, predict the future state of the node.