Converged routing for distributed computing systems

1. An apparatus, comprising:

a network interface to communicate over a network; and

a processor to:

receive, via the network interface, a plurality of data streams to be routed over the network, wherein the plurality of data streams each contain sensor data captured by one or more sensors;

determine, based on analyzing the plurality of data streams, that the plurality of data streams contains a subset of related data streams, wherein at least some of the sensor data contained in the subset of related data streams is contextually related;

select, based on analyzing the subset of related data streams, a convergence function for generating a converged data stream based on the subset of related data streams

perform the convergence function on the subset of related data streams to generate the converged data stream, wherein the converged data stream represents at least some of the sensor data contained in the subset of related data streams, and wherein the converged data stream is smaller in size than the subset of related data streams; and

route, via the network interface, the converged data stream to one or more corresponding destinations over the network.

2. The apparatus of claim 1 , wherein the processor to determine, based on analyzing the plurality of data streams, that the plurality of data streams contains the subset of related data streams is further to:

determine that the subset of related data streams are contextually related based on a type of content within the subset of related data streams.

3. The apparatus of claim 1 , wherein the processor to determine, based on analyzing the plurality of data streams, that the plurality of data streams contains the subset of related data streams is further to:

determine that at least some of the sensor data contained in the subset of related data streams was captured within a particular proximity in location and time.

4. The apparatus of claim 1 , wherein the processor to determine, based on analyzing the plurality of data streams, that the plurality of data streams contains the subset of related data streams is further to:

identify a plurality of content names associated with the plurality of data streams, wherein each content name of the plurality of content names indicates a type of content within a particular data stream of the plurality of data streams; and

determine that the subset of related data streams are contextually related based on the plurality of content names associated with the plurality of data streams.

5. The apparatus of claim 4 , wherein:

the network comprises an information-centric network; and

the plurality of content names correspond to entries of a pending interest table associated with the information-centric network.

6. The apparatus of claim 4 , wherein the plurality of content names correspond to a plurality of uniform resource identifiers associated with the plurality of data streams.

7. The apparatus of claim 1 , wherein the processor to select, based on analyzing the subset of related data streams, the convergence function for generating a converged data stream based on the subset of related data streams is further to:

select the convergence function from a plurality of convergence functions based on a type of content within the subset of related data streams.

8. The apparatus of claim 1 , wherein:

the convergence function comprises a compression function for compressing the subset of related data streams; and

the processor to perform the convergence function on the subset of related data streams to generate the converged data stream is further to compress the subset of related data streams using the compression function.

9. The apparatus of claim 1 , wherein:

the convergence function comprises a sensor fusion function for combining at least some of the sensor data contained in the subset of related data streams; and

the processor to perform the convergence function on the subset of related data streams to generate the converged data stream is further to combine at least some of the sensor data using the sensor fusion function.

10. The apparatus of claim 1 , wherein:

the convergence function comprises an analytic function for analyzing content within the subset of related data streams; and

the processor to perform the convergence function on the subset of related data streams to generate the converged data stream is further to:

analyze the content within the subset of related data streams using the analytic function, wherein the analytic function is to output a summary of the content; and

generate metadata corresponding to the summary of the content, wherein the converged data stream is to comprise the metadata.

11. The apparatus of claim 1 , wherein:

the plurality of sensors comprise a plurality of cameras; and

the subset of related data streams comprise visual data captured by the plurality of cameras.

12. The apparatus of claim 11 , wherein:

the convergence function comprises an object detection function for detecting one or more objects within the visual data captured by the plurality of cameras; and

the processor to perform the convergence function on the subset of related data streams to generate the converged data stream is further to:

detect the one or more objects within the visual data using the object detection function; and

generate metadata corresponding to the one or more objects that are detected within the visual data, wherein the converged data stream is to comprise the metadata.

13. The apparatus of claim 1 , wherein the processor to perform the convergence function on the subset of related data streams to generate the converged data stream is further to discard one or more redundant data streams from the subset of related data streams.

14. The apparatus of claim 1 , wherein the processor to perform the convergence function on the subset of related data streams to generate the converged data stream is further to convert the subset of related data streams into a lower resolution format.

15. The apparatus of claim 14 , wherein the lower resolution format is 1/n of a size of the subset of related data streams, wherein n represents a number greater than 1.