Evaluating Policy-Driven Adaptation on the Edge-to-Cloud Continuum
Machine Learning and Artificial Intelligence
TimeFriday, 19 November 20219:40am - 10am CST
DescriptionDeveloping data-driven applications requires orchestrating data-to-discovery pipelines across distributed data sources and computing units. Realizing such pipelines poses two major challenges: programming analytics that reacts at runtime to unforeseen events, and adaptation of the resources and computing paths between the edge and the cloud. While these concerns are interdependent, they must be separated during the design process of the application and the deployment operations of the infrastructure.
This work proposes a system stack for the adaptation of distributed analytics across the computing continuum. We evaluate the ability to continually balance the computation or data movement’s cost with the value of operations to the application objectives. Using a disaster response application, we observe that the system can select appropriate configurations while managing trade-offs. The evaluation shows that our model is able to adapt to variations in the data input size, bandwidth, and CPU capacities with minimal deadline violations (less than 10%).