Improving makespan in dynamic task scheduling for cloud robotic systems with time window constraints

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. Time window constraints on tasks are a natural way to order tasks. The makespan is the maximum amount of time between when the first processing unit starts executing its first task and when all processing units have completed their last scheduled task. Load balancing allocation and scheduling ensures that the time between when the first processing unit completes its scheduled tasks and when all other processing units complete their scheduled tasks is as short as possible. We propose a method to ensure that the time window constraints are met. We propose the grid of all tasks balancing algorithm (GTBA) for distributing and scheduling tasks with minimum makespan. The GTBA method is a combinatorial method that describes a way to distribute tasks among processing units and schedule them when a set of tasks arrives in such a way that the makespan is minimized, the loads of all processing units are nearly balanced, and it is ensured that the time window constraints are met. We prove the correctness of the proposed algorithm and present simulations illustrating the obtained results.