The past few decades have seen software and hardware growing more heterogeneous and layered in abstractions. This trend produced many benefits for hiding complexity and increasing efficiency and modularity. But it also makes reasoning about performance and identifying its underlying factors more challenging because of the presence of performance variability. Moreover, performance variability can prevent synchronous applications from scaling and server applications from meeting service-level agreements.

Serverless computing platforms simplify development, deployment, and automated management of modular software functions. However, existing serverless platforms typically assume an over-provisioned cloud, making them a poor fit for Edge Computing environments where resources are scarce. In this paper we propose a redesigned serverless platform that comprehensively tackles the key challenges for serverless functions in a resource constrained Edge Cloud.

Name-based pub/sub allows for efficient and timely delivery of information to interested subscribers. A challenge is assigning the right name to each piece of content, so that it reaches the most relevant recipients. An example scenario is the dissemination of social media posts to first responders during disasters. We present FLARE, a framework using federated active learning assisted by naming. FLARE integrates machine learning and name-based pub/sub for accurate timely delivery of textual information. In this demo, we show FLARE’s operation.