Machine-learning-based load balancing for cloud-based disaster recovery apparatuses, processes and systems

A load balancing asset virtualizing processor-readable, non-transient medium, the medium storing a component collection, the component collection structured with processor-executable instructions comprising:

• obtain, via at least one processor, an asset virtualization request datastructure, the asset virtualization request datastructure structured to include a first data field for identifying an asset and a second data field for specifying an expected workload timeframe corresponding to the expected workload timeframe associated with the asset virtualization request datastructure;
• retrieve, via the at least one processor, a set of asset workload classification labels for the asset, the set of asset workload classification labels determined using an asset workload classification datastructure, the asset workload classification datastructure trained using a machine learning method and asset telemetry training data;
• retrieve, via the at least one processor, a set of node workload classification labels for each node in a set of available compute nodes, set of node workload classification labels determined using a node workload classification datastructure, the node workload classification datastructure trained using a machine learning method and node telemetry training data from a repository, the node telemetry training data including labeled training records over a certain period of time;
• determine, via the at least one processor, a set of candidate compute nodes from the set of available compute nodes, in which the set of node workload classification labels for a candidate compute node is determined to be compatible with the set of asset workload classification labels for the asset, in which compatibility is determined using a set of capacity threshold rules;
• select, via the at least one processor, a candidate compute node from the set of candidate compute nodes;
• and
• instantiate, via the at least one processor, a virtual machine corresponding to the asset on the selected candidate compute node.

A load balancing asset virtualizing processor-implemented system, comprising:

• means to store a component collection;
• means to process processor-executable instructions from the component collection, the component collection structured with processor-executable instructions including:
• obtain, via at least one processor, an asset virtualization request datastructure, the asset virtualization request datastructure structured to include a first data field for identifying an asset and a second data field for specifying an expected workload timeframe corresponding to the expected workload timeframe associated with the asset virtualization request datastructure;
• retrieve, via the at least one processor, a set of asset workload classification labels for the asset, the set of asset workload classification labels determined using an asset workload classification datastructure, the asset workload classification datastructure trained using a machine learning method and asset telemetry training data;
• retrieve, via the at least one processor, a set of node workload classification labels for each node in a set of available compute nodes, set of node workload classification labels determined using a node workload classification datastructure, the node workload classification datastructure trained using a machine learning method and node telemetry training data from a repository, the node telemetry training data including labeled training records over a certain period of time;
• determine, via the at least one processor, a set of candidate compute nodes from the set of available compute nodes, in which the set of node workload classification labels for a candidate compute node is determined to be compatible with the set of asset workload classification labels for the asset, in which compatibility is determined using a set of capacity threshold rules;
• select, via the at least one processor, a candidate compute node from the set of candidate compute nodes;
• and
• instantiate, via the at least one processor, a virtual machine corresponding to the asset on the selected candidate compute node.

A load balancing asset virtualizing processor-implemented process, including processing processor-executable instructions via at least one processor from a component collection stored in at least one memory, the component collection structured with processor-executable instructions comprising:

• obtain, via the at least one processor, an asset virtualization request datastructure, the asset virtualization request datastructure structured to include a first data field for identifying an asset and a second data field for specifying an expected workload timeframe corresponding to the expected workload timeframe associated with the asset virtualization request datastructure;
• retrieve, via the at least one processor, a set of asset workload classification labels for the asset, the set of asset workload classification labels determined using an asset workload classification datastructure, the asset workload classification datastructure trained using a machine learning method and asset telemetry training data;
• retrieve, via the at least one processor, a set of node workload classification labels for each node in a set of available compute nodes, set of node workload classification labels determined using a node workload classification datastructure, the node workload classification datastructure trained using a machine learning method and node telemetry training data from a repository, the node telemetry training data including labeled training records over a certain period of time;
• determine, via the at least one processor, a set of candidate compute nodes from the set of available compute nodes, in which the set of node workload classification labels for a candidate compute node is determined to be compatible with the set of asset workload classification labels for the asset, in which compatibility is determined using a set of capacity threshold rules;
• select, via the at least one processor, a candidate compute node from the set of candidate compute nodes;
• and
• instantiate, via the at least one processor, a virtual machine corresponding to the asset on the selected candidate compute node.