Cost-Efficient and Latency-Aware Workflow Scheduling Policy for Container-based Systems

Container technology is being adopted to simplify workflow execution. In this paper, we investigate a workflow scheduling policy for container-based systems. A workflow, representing an application, consists of a set of tasks. Each task can be executed in a container within a virtual machine (VM), where the container packaging the function for the task should be loaded into the VM before task execution. To reduce the workflow execution time and the network bandwidth consumption, we propose a cost-efficient and latency-aware workflow scheduling algorithm that strategically loads the containers into VMs and executes the tasks on the VMs. The algorithm is based on "Stretch Out and Compact", which can stretch out the tasks along the resources by critical path analysis and then find the inefficient slots within the computing resources and eventually compact the tasks into those slots. We introduce a concept of "virtual task" into the algorithm, where container loading is regarded as a virtual task that should be executed before the real task. The introduction of the virtual task can be more effective in finding the inefficient slots for the compaction, thus resulting in a more efficient workflow scheduling policy. Simulation results show that compared to the algorithms that fully or selectively load the dockers, the proposed algorithm can achieve less execution time while saving network bandwidth consumption for loading dockers.