DataVinci: Proactive Data Placement for Ad-Hoc Computing

Abstract

Mobile ad-hoc computing enables applications to offload computationally intensive tasks to end-user devices in proximity. Many state-of-the-art applications such as face recognition, machine learning, or computer vision require large amounts of input data that is shared among multiple tasks. In these use cases, offloading the workload to remote devices becomes more time-consuming and, consequently, less attractive due to the required data transfer. As a solution, a proactive distribution of the data files on potential computational resource providers eliminates the need for ad-hoc data transfers. The characteristics of ad-hoc computing environments necessitate non-trivial data and task placement strategies. In this paper, we propose DataVinci — a data and task scheduler for mobile ad-hoc computing environments. DataVinci determines the number of copies for each data file (replicas), places these replicas proactively on remote devices, and schedules tasks based on the previously created data distribution. It continuously adjusts the number of replicas and balances the trade-off between execution latencies and data transfer overhead. In a large-scale study, we show the effectiveness of DataVinci, which reduces the average task execution time by more than 60 percent compared to an approach without proactive data placement, while keeping the amount of transferred data constant.