Online Distributed Analytics at the Edge with Multiple Service Grades

Abstract

In this paper, we study the problem of how to allocate bandwidth and computation resources to deliver data analytics services at the edge. The types of services we envision consist of a chain of tasks that must be carried out sequentially, and where the number of tasks executed in the chain determines the grade in which a service is delivered. An example of such type of service is video analytics where different deep-learning algorithms are combined to provide a more accurate description of a scene. The contributions of the paper are to formulate the static resource allocation problem as a linear program, to discuss the challenges of static formulations in dynamic settings, and to propose a control-type formulation that uses approximate system dynamics and time-varying cost functions. The work also highlights the need for policies that can operate the network and learn its characteristics simultaneously.