Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System

Location-based services for moving objects are close to our lives. For example, ride-sharing services, micro-mobility services, navigation and traffic management, delivery services, and autonomous driving are all based on moving objects. The efficient management of such moving objects is therefore getting more and more important. The main challenge is the handling of a large number of location-update queries with scan queries. To address this challenge, we propose a novel in-memory grid indexing system, Waffle, for moving objects. Waffle divides a geographical space into fixed-sized cells. For efficient query processing, Waffle forms chunks, each of which consists of neighboring cells. Such a Waffle index is defined by several configuration knobs. A knob configuration has a significant impact on the performance of Waffle, and an appropriate configuration may change as objects continuously move. Therefore, we propose an online configuration tuning system, WaffleMaker, that automatically determines not only knob values but also when to change knob values, as a part of Waffle. Using a configuration determined by WaffleMaker, Waffle rebuilds the current index without blocking user queries based on a concurrency control scheme. Through extensive experiments, we show that Waffle performed better than the existing methods, and WaffleMaker automatically tuned configuration knob values.