Tutorial: MARS: A Framework for Runtime Monitoring, Modeling, and Management of Realtime Systems

From datacenters to embedded devices, modern realtime work-loads are demanding exceptional computational capacity from state-of-the-art systems, while satisfying energy constraints, real-time deadlines, mixed criticality workloads, and satisfactory quality-of-service (QoS). In response, researchers have proposed resource management policies to maximize system utilization and efficiency, e.g., power managers, dynamic frequency and voltage scaling governors, task mappers and schedulers, offloading orchestrators, etc. Policies can utilize techniques from various algorithmic domains, e.g., game theory, control theory, and machine learning. In this tutorial, we give an overview and demonstration of MARS (Middleware for Adaptive and Reflective Systems), a cross-layer and multi-platform framework developed by Dutt Research Group at UC Irvine that allows system designers to easily create resource managers by composing system models and resource management policies in a flexible and coordinated manner. MARS consists of a generic user-level sensing/actuation interface that allows for portable policy design, and a reflective system model used to coordinate multiple policies. We demonstrate MARS’ ability to deploy a low-overhead realtime resource manager through a dynamic voltage and frequency scaling (DVFS) policy example which can run on any Linux-based HMP computing platform. We also demonstrate MARS’ ability to trans-parently collect, store, and analyze realtime application behavior at scale through an architectural monitor for (1) a rack server executing inference services and (2) an embedded developer board executing autonomous driving tasks.