Improving Makespan in Dynamic Task Scheduling for Cloud Robotic Systems with Time Window Constraints
This work provides an incremental improvement for cloud robotic system operators by minimizing makespan under time window constraints.
This paper addresses dynamic task scheduling in cloud robotic systems with time window constraints, aiming to minimize makespan. The authors propose a grid-based task balancing algorithm to distribute and schedule tasks, theoretically proving its correctness and illustrating results through simulations.
A scheduling method in a robotic network cloud system with minimal makespan is beneficial as the system can complete all the tasks assigned to it in the fastest way. Robotic network cloud systems can be translated into graphs where nodes represent hardware with independent computing power and edges represent data transmissions between nodes. Time window constraints on tasks are a natural way to order tasks. The makespan is the maximum amount of time between when the first node to receive a task starts executing its first scheduled task and when all nodes have completed their last scheduled task. Load balancing allocation and scheduling ensures that the time between when the first node completes its scheduled tasks and when all other nodes complete their scheduled tasks is as short as possible. We propose a grid of all tasks to ensure that the time window constraints for tasks are met. We propose grid of all tasks balancing algorithm for distributing and scheduling tasks with minimum makespan. We theoretically prove the correctness of the proposed algorithm and present simulations illustrating the obtained results.