Alpaca: Intermittent Execution without Checkpoints (1909.06951v1)

Abstract: The emergence of energy harvesting devices creates the potential for batteryless sensing and computing devices. Such devices operate only intermittently, as energy is available, presenting a number of challenges for software developers. Programmers face a complex design space requiring reasoning about energy, memory consistency, and forward progress. This paper introduces Alpaca, a low-overhead programming model for intermittent computing on energy-harvesting devices. Alpaca programs are composed of a sequence of user-defined tasks. The Alpaca runtime preserves execution progress at the granularity of a task. The key insight in Alpaca is the privatization of data shared between tasks. Updates of shared values in a task are privatized and only committed to main memory on successful execution of the task, ensuring that data remain consistent despite power failures. Alpaca provides a familiar programming interface and a highly efficient runtime model. We also present an alternate version of Alpaca, Alpaca-undo, that uses undo-logging and rollback instead of privatization and commit. We implemented a prototype of both versions of Alpaca as an extension to C with an LLVM compiler pass. We evaluated Alpaca, and directly compared to three systems from prior work. Alpacaconsistently improves performance compared to the previous systems, by up to 23.8x, while also improving memory footprint in many cases, by up to 17.6x.

• The paper introduces Alpaca, a novel programming model for intermittent execution on energy-harvesting devices that avoids the need for traditional checkpoints.
• Alpaca achieves significant performance gains and memory reduction compared to prior systems by using a task-based model with data privatization or undo-logging.
• Unlike prior methods, Alpaca works efficiently with mixed volatile/non-volatile memory and offers a user-friendly interface for managing intermittent computation.

Insights into the Alpaca Programming Model for Intermittent Execution

The research paper presents Alpaca, a programming and execution model designed to enhance the efficiency of intermittent computing on energy-harvesting devices. The framework addresses significant challenges faced by these devices, which operate without continuous power supply, often experiencing frequent and unexpected power outages. Alpaca sets out to improve execution consistency and computational progress, specifically targeting the limitations inherent in prior systems that execute under similar constraints.

Alpaca’s programming model introduces a sequence of tasks that are structured to accommodate power interruptions gracefully. These tasks ensure that the execution progress is preserved at the granular level, employing the privatization of shared data to maintain consistency across power failures. The authors also offer a variant, Alpaca-undo, which replaces privatization and commit with undo-logging and rollback, demonstrating a flexible approach to consistency management.

Performance evaluation revealed impressive results when Alpaca was tested against previously established systems. Notably, Alpaca substantially reduces memory footprint by up to 17.6 times, while also achieving performance gains averaging between 4 to 5.2 times the efficiency, sometimes reaching up to 23.8 times improvement. Such enhancements underscore Alpaca’s proficiency in optimizing both run-time overheads and memory usage.

One of Alpaca’s principal advantages over its predecessors is its minimal assumptions on device hardware. Unlike prior frameworks that necessitate entirely non-volatile memory configurations, Alpaca can operate efficiently with devices comprised of a mix of volatile and non-volatile memory. Such versatility is key in adapting to various hardware architectures without demanding extensive modifications.

The Alpaca framework is further characterized by its user-friendly programming interface that enables developers to denote tasks and manage shared data explicitly. Each task in Alpaca functions akin to a transactional memory system, with data updates being committed only upon task completion. This mechanism ensures atomicity and significantly reduces checkpointing overhead, which had been a substantial limitation in earlier approaches.

Alpaca’s effectiveness is especially notable in energy-harvesting environments where the software must handle intermittent execution induced by power scarcity. By leveraging a task-based model and privatization, Alpaca clears a significant path toward achieving deterministic execution without the excessive burden of maintaining system-wide checkpoints.

From a theoretical perspective, Alpaca contributes to the field of energy-efficient computing by addressing a critical need for models that can handle frequent power disruptions effortlessly. The task decomposition method and idempotent execution guarantee secure progress even under the most constrained energy conditions, opening new possibilities for the development of robust batteryless computing devices.

Looking forward, the paper suggests the potential for further refinement in the area of automated task decomposition, which, if successfully implemented, could significantly optimize programming efficiency. Such developments would potentially extend Alpaca’s utility and applicability, facilitating more adaptive and intelligent energy management paradigms.

In conclusion, Alpaca provides a sophisticated approach to intermittent execution without reliance on checkpoints, offering a significant stride forward in the field of programming models tailored for energy-harvesting devices. As the demand for sustainably powered computing grows, Alpaca underscores a promising step in the evolution of resilient software systems.