  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.
