A Composable Monitoring System for Heterogeneous Embedded Platforms

Abstract

Advanced computations on embedded devices are nowadays a must in any application field. Often, to cope with such a need, embedded systems designers leverage on complex heterogeneous reconfigurable platforms that offer high performance, thanks to the possibility of specializing/customizing some computing elements on board, and are usually flexible enough to be optimized at runtime. In this context, monitoring the system has gained increasing interest. Ideally, monitoring systems should be non-intrusive, serve several purposes, and provide aggregated information about the behavior of the different system components. However, current literature is not close to such ideality: For example, existing monitoring systems lack in being applicable to modern heterogeneous platforms. This work presents a hardware monitoring system that is intended to be minimally invasive on system performance and resources, composable, and capable of providing to the user homogeneous observability and transparent access to the different components of a heterogeneous computing platform, so system metrics can be easily computed from the aggregation of the collected information. Building on a previous work, this article is primarily focused on the extension of an existing hardware monitoring system to cover also specialized coprocessing units, and the assessment is done on a Xilinx FPGA-based System on Programmable Chip. Different explorations are presented to explain the level of customizability of the proposed hardware monitoring system, the tradeoffs available to the user, and the benefits with respect to standard de facto monitoring support made available by the targeted FPGA vendor.

AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems

“…In an AIOps-enabled context, the toolkit should support 1) the monitoring of runtime data (such as logs, events and metrics [15]) and software data and their traceability (namely Observe), 2) the analysis of both historical and real-time data (namely Analyze), and 3) the automation of development and operation activities (namely Automate). Accountability [23] and explainability [22] capabilities should be managed as cross-cutting concerns throughout the DevOps engineering process and toolchain.…”