Container live migration serves as the cornerstone of maintaining containerized workloads in cloud and edge datacenters, particularly for stateful applications. However, the de facto memory pre-copy based migration faces severe performance issues for containers with dynamically changing memory dirty pages. Existing research often overlooks such dynamic nature of memory pages of various workloads and their unpredictable relationship with system-level features, causing unwise stop-and-copy iterations of container migrations. This can prolong container migrations by tens of seconds, severely degrading application performance. To address these challenges, we introduce U²CMigration, a user-unaware container live migration strategy for containerized workloads. It employs a lightweight and autonomous two-phase prediction by analyzing container memory pages across various workloads. We utilize the data shift prediction for stable memory pages (phase-1). For unstable memory pages (phase-2), we develop an attention-based prediction method that jointly considers the spatio-temporal characteristics of memory pages and system-level features. Guided by dirty page predictions, we further develop a container live migration strategy that judiciously decides the optimal stop-and-copy iteration with the minimum amount of memory dirty pages. We have implemented an open-source prototype of U²CMigration (https://doi.org/10.57760/sciencedb.32136) based on the CRIU (checkpoint/restore in userspace) project. Extensive prototype experiments demonstrate that U²CMigration reduces the container migration duration by 26.1%–47.9% and the downtime by 21.3%–32.6% compared with the state-of-the-art solutions.

Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.