Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems -- graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and-container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes.

翻译：容器正被越来越多的互联网服务提供商用于在多接入边缘计算（MEC）系统中部署其应用。尽管基于容器的虚拟化技术显著提升了应用的可用性，但其可能面临高昂的通信开销与资源使用不均衡的问题。然而，迄今仍鲜有研究致力于克服这些困难。本文针对基于相互依赖的多任务组合构建的应用，设计了一种工作流驱动的数学模型，将问题形式化为包含两个子问题——图划分与多选择向量装箱——的多目标组合优化问题，并提出若干联合任务-容器化与容器放置方法，以降低通信开销并平衡多类型计算资源利用率。通过与现有顶尖的任务与容器调度方案进行对比，验证了所提算法在性能上的优越性。