BACKGROUND

Embodiments of the invention relate to optimized deployment of analytic models in an edge topology. For example, embodiments of the invention relate to recommending optimal compressed configurations for analytic model compositions in a multi-tiered edge topology.

Edge computing may be described as a distributed computing environment that brings data analysis/processing and data storage closer to the location where it is needed to improve response times and save bandwidth. With the emergence of smart cameras and small, single-board computers with camera add-ons, some video analytics capabilities are moving to the edge. Such smart cameras and small, single-board computers with camera add-ons may be described as edge devices.

However, edge devices are usually not powerful enough to run a typical video analytics composition on a live stream on their own. Compression techniques may help run deep learning models more efficiently on edge devices, but may produce inferior results.

SUMMARY

In accordance with certain embodiments, a computer-implemented method is provided for optimized deployment of analytic models in an edge topology. The computer-implemented method comprises operations. A description of a multi-tiered edge topology with a plurality of nodes, a multimedia stream analytics composition that is executed on the plurality of nodes, and performance objectives are received, where the multimedia stream analytics composition includes tasks that use analytic models. The analytic models are optimized. The optimized analytic models are clustered to form clusters of optimized analytic models. A representative optimized analytic model is selected from each of the clusters of optimized analytic models. A configuration recommendation is determined that indicates deployment of the tasks and of each selected representative optimized analytic model on the plurality of nodes to meet the performance objectives. One or more workflows are generated to execute the tasks of the multimedia stream analytics composition on nodes of different tiers of the multi-tiered edge topology. The one or more workflows are executed on the plurality of nodes to generate output for the multimedia stream analytics composition.

In accordance with other embodiments, a computer program product is provided for optimized deployment of analytic models in an edge topology. The computer program product comprises a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform operations. A description of a multi-tiered edge topology with a plurality of nodes, a multimedia stream analytics composition that is executed on the plurality of nodes, and performance objectives are received, where the multimedia stream analytics composition includes tasks that use analytic models. The analytic models are optimized. The optimized analytic models are clustered to form clusters of optimized analytic models. A representative optimized analytic model is selected from each of the clusters of optimized analytic models. A configuration recommendation is determined that indicates deployment of the tasks and of each selected representative optimized analytic model on the plurality of nodes to meet the performance objectives. One or more workflows are generated to execute the tasks of the multimedia stream analytics composition on nodes of different tiers of the multi-tiered edge topology. The one or more workflows are executed on the plurality of nodes to generate output for the multimedia stream analytics composition.

In accordance with yet other embodiments, a computer system is provided for optimized deployment of analytic models in an edge topology. The computer system comprises one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; and program instructions, stored on at least one of the one or more computer-readable, tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform operations. A description of a multi-tiered edge topology with a plurality of nodes, a multimedia stream analytics composition that is executed on the plurality of nodes, and performance objectives are received, where the multimedia stream analytics composition includes tasks that use analytic models. The analytic models are optimized. The optimized analytic models are clustered to form clusters of optimized analytic models. A representative optimized analytic model is selected from each of the clusters of optimized analytic models. A configuration recommendation is determined that indicates deployment of the tasks and of each selected representative optimized analytic model on the plurality of nodes to meet the performance objectives. One or more workflows are generated to execute the tasks of the multimedia stream analytics composition on nodes of different tiers of the multi-tiered edge topology. The one or more workflows are executed on the plurality of nodes to generate output for the multimedia stream analytics composition.

Thus, embodiments advantageously provide a multi-tiered edge topology that allows running different parts of a multimedia stream analytics composition using different model compression settings and different frame rates in each tier of the multi-tiered edge topology. That is, embodiments select what parts of the multimedia stream analytics composition and what compression models should run where and when.

In accordance with additional embodiments, the performance objectives comprise any combination of accuracy, precision, recall, and throughput. This allows for different combinations of performance objectives to be used to customize preferences for the distribution of tasks of the multimedia stream analytics composition.

In accordance with yet additional embodiments, optimizing the analytic models comprises compressing the analytic models. This advantageously allows for compression of the analytic models for increased efficiency.

In accordance with more embodiments, the clustering is based on benchmark results, and where the benchmark results are generated by benchmark testing of the optimized analytic models using test data and the plurality of nodes in the multi-tiered edge topology. This advantageously provides a clustering technique that is based on test data and the plurality of nodes in the multi-tiered edge topology, which results in better distribution of tasks of the multimedia stream analytics composition.

In accordance with yet more embodiments, one or more of images and metadata are transmitted between the nodes of the plurality of nodes at different transmission speeds. Taking into account the different transmission speeds allows for better distribution of tasks of the multimedia stream analytics composition.

In accordance with further embodiments, for each node in the multi-tiered edge topology, an objective function is defined based on performance objectives for performance, accuracy, precision, and recall. Then, for each node, a cumulative objective function is determined based on a cumulative performance, a cumulative accuracy, a cumulative precision, and a cumulative recall. An overall objective function for the multi-tiered edge topology is created based on the cumulative objective function of each node. This advantageously generates an overall objective function for the multimedia stream analytics composition.

In accordance with yet further embodiments, the plurality of nodes includes a central server in a cloud infrastructure, where the central server stores one of images and metadata for the multimedia stream analytics composition in a data store. This advantageously allows for central storage of the images and/or metadata the multimedia stream analytics composition.

In other embodiments, a Software as a Service (SaaS) is configured to perform the operations of the method. This advantageously allows for the generation of the configuration recommendation and the one or more workflows to be performed via a service.

