Aveksha: a hardware-software approach for non-intrusive tracing and profiling of wireless embedded systems.

ABSTRACTIt is important to get an idea of the events occurring in an embedded wireless node when it is deployed in the field, away from the convenience of an interactive debugger. Such visibility can be useful for post-deployment testing, replay-based debugging, and for performance and energy profiling of various software components. Prior software-based solutions to address this problem have incurred high execution overhead and intrusiveness. The intrusiveness changes the intrinsic timing behavior of the application, thereby reducing the fidelity of the collected profile. Prior hardware-based solutions have involved the use of dedicated ASICs or other tightly coupled changes to the embedded node's processor, which significantly limits their applicability. In this paper, we present Aveksha, a hardware-software approach for achieving the above goals in a non-intrusive manner. Our approach is based on the key insight that most embedded processors have an on-chip debug module (which has traditionally been used for interactive debugging) that provides significant visibility into the internal state of the processor. We design a debug board that interfaces with the on-chip debug module of an embedded node's processor through the JTAG port and provides three modes of event logging and tracing: breakpoint, watchpoint, and program counter polling. Using expressive triggers that the on-chip debug module supports, Aveksha can watch for, and record, a variety of programmable events of interest. A key feature of Aveksha is that the target processor does not have to be stopped during event logging (in the last two of the three modes), subject to a limit on the rate at which logged events occur. Aveksha also performs power monitoring of the embedded wireless node and, importantly, enables power consumption data to be correlated to events of interest. Aveksha is an operating system-agnostic solution. We demonstrate its functionality and performance using three applications running on Telos motes; two in TinyOS and one in Contiki. We show that Aveksha can trace tasks and other generic events at the function and task-level granularity. We also describe how we used Aveksha to find a subtle bug in the TinyOS low power listening protocol.