2019/09/13 by Kiwan Maeng, Maeng, Kiwan, Alexei Colin +3 · 3 citations
Computer Science · Engineering · #Distributed #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Opportunistic and Delay-Tolerant Networks #Parallel #Parallel Computing and Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1909.06951
openalex publication_date 2019/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.