Improving Makespan in Dynamic Task Scheduling for Cloud Robotic Systems with Time Window Constraints

TL;DR

This paper introduces a new grid-based scheduling algorithm for cloud robotic systems that minimizes makespan while respecting task time window constraints, improving task completion efficiency.

Contribution

The paper presents a novel grid of all tasks balancing algorithm that ensures minimal makespan in dynamic task scheduling with time window constraints in robotic cloud systems.

Findings

Algorithm guarantees minimal makespan under constraints

Theoretical proof of algorithm correctness

Simulation results demonstrate efficiency improvements

Abstract

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.