RASC: Enhancing Observability & Programmability in Smart Spaces

TL;DR

This paper introduces RASC, a new abstraction for IoT actions in smart spaces that enhances observability and programmability, enabling better failure detection, scheduling, and prediction without replacing existing RPC mechanisms.

Contribution

The paper presents RASC, a novel abstraction that improves IoT action observability and programmability, and demonstrates its effectiveness through integration and evaluation in a real-world framework.

Findings

RASC achieves latency SLOs for long-duration IoT actions.

Scheduling policies with RASC outperform existing methods by 10%-55%.

RASC enables accurate prediction of action completion times.

Abstract

While RPCs form the bedrock of systems stacks, we posit that IoT device collections in smart spaces like homes, warehouses, and office buildings--which are all "user-facing"--require a more expressive abstraction. Orthogonal to prior work, which improved the reliability of IoT communication, our work focuses on improving the observability and programmability of IoT actions. We present the RASC (Request-Acknowledge-Start-Complete) abstraction, which provides acknowledgments at critical points after an IoT device action is initiated. RASC is a better fit for IoT actions, which naturally vary in length spatially (across devices) and temporally (across time, for a given device). RASC also enables the design of several new features: predicting action completion times accurately, detecting failures of actions faster, allowing fine-grained dependencies in programming, and scheduling. RASC is intended to be implemented atop today's available RPC mechanisms, rather than as a replacement. We integrated RASC into a popular and open-source IoT framework called Home Assistant. Our trace-driven evaluation finds that RASC meets latency SLOs, especially for long actions that last O(mins), which are common in smart spaces. Our scheduling policies for home automations (e.g., routines) outperform state-of-the-art counterparts by 10%-55%.