Distributed assignment of video analytics tasks in cloud computing environments to reduce bandwidth utilization

A task assignment method for video analytics processing in a cloud computing environment, the method comprising:

• determining, by executing an instruction with a processor, a directed acyclic graph including nodes and edges to represent:
• (i) a plurality of video sources, (ii) a cloud computing platform, and (iii) a plurality of intermediate network devices in the cloud computing environment, the plurality of intermediate network devices to communicate data from the video sources to the cloud computing platform;
• specifying, by executing an instruction with the processor, task orderings of respective sequences of video analytics processing tasks to be executed in the cloud computing environment on respective video source data generated by respective ones of the video sources;
• and
• iteratively assigning, by executing an instruction with the processor and based on the directed acyclic graph and the task orderings, combinations of the video sources, the intermediate network devices and the cloud computing platform to execute the respective sequences of video analytics processing tasks on the corresponding video source data generated by the respective ones of the video sources, the combinations of the video sources, the intermediate network devices and the cloud computing platform being assigned to satisfy the task orderings along respective data paths from the video sources to the cloud computing platform as represented in the directed acyclic graph.

A non-transitory computer readable medium comprising computer readable instructions which, when executed, cause a processor to at least:

• determine a directed acyclic graph including nodes and edges to represent:
• (i) a plurality of video sources, (ii) a cloud computing platform, and (iii) a plurality of intermediate network devices in a cloud computing environment, the plurality of intermediate network devices to communicate data from the video sources to the cloud computing platform;
• specify task orderings of respective sequences of video analytics processing tasks to be executed in the cloud computing environment on respective video source data generated by respective ones of the video sources;
• and
• iteratively assign, based on the directed acyclic graph and the task orderings, combinations of the video sources, the intermediate network devices and the cloud computing platform to execute the respective sequences of video analytics processing tasks on the corresponding video source data generated by the respective ones of the video sources, the combinations of the video sources, the intermediate network devices and the cloud computing platform to be assigned to satisfy the task orderings along respective data paths from the video sources to the cloud computing platform as represented in the directed acyclic graph.