The development of Internet of Things leads to an increase in edge devices, and the traditional cloud is unable to meet the demands of the low latency of numerous devices in edge area. On the hand, the media delivery requires high-quality solution to meet ever-increasing user demands. The edge cloud paradigm is put forward to address the issues, which facilitates edge devices to acquire resources dynamically and rapidly from nearby places. However, in order to complete as many tasks as possible in a limited time to meet the needs of users, and to complete the consistency maintenance in as short a time as possible, a two-level scheduling optimization scheme in an edge cloud environment is proposed. The first-level scheduling is by using our proposed artificial fish swarm-based job scheduling method, most jobs will be scheduled to edge data centers. If the edge data center does not have enough resource to complete, the job will be scheduled to centralized cloud data center. Subsequently, the job is divided into same-sized tasks. Then, the second-level scheduling, considering balance load of nodes, the edge cloud task scheduling is proposed to decrease completion time, while the centralized cloud task scheduling is presented to reduce total cost. The experimental results show that our proposed scheme performs better in terms of minimizing latency and completion time, and cutting down total cost.

中文翻译：

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一种提高边缘云环境一致性维护的高效调度优化策略

物联网的发展导致边缘设备增多，传统云无法满足边缘区域众多设备的低时延需求。另一方面，媒体交付需要高质量的解决方案来满足不断增长的用户需求。边缘云范式被提出来解决这个问题，它有助于边缘设备从附近的地方动态快速地获取资源。然而，为了在有限的时间内完成尽可能多的任务以满足用户的需求，并在尽可能短的时间内完成一致性维护，提出了边缘云环境下的两级调度优化方案. 一级调度是使用我们提出的基于人工鱼群的作业调度方法，大部分作业将被调度到边缘数据中心。如果边缘数据中心没有足够的资源来完成，作业将被调度到集中式云数据中心。随后，作业被划分为相同大小的任务。然后，二级调度考虑节点负载均衡，提出边缘云任务调度以减少完成时间，同时提出集中式云任务调度以降低总成本。实验结果表明，我们提出的方案在最小化延迟和完成时间以及降低总成本方面表现更好。提出边缘云任务调度以减少完成时间，而提出集中式云任务调度以降低总成本。实验结果表明，我们提出的方案在最小化延迟和完成时间以及降低总成本方面表现更好。提出边缘云任务调度以减少完成时间，而提出集中式云任务调度以降低总成本。实验结果表明，我们提出的方案在最小化延迟和完成时间以及降低总成本方面表现更好。