边云协同计算中基于预测的资源部署与任务调度优化
Resourse Deployment with Prediction and Task Scheduling Optimization in Edge Cloud Collaborative Computing
内容：
The cloud computing model of data centralized processing（数据集中处理的云计算模式）
is facing new challenges for providing
diversified application services with rapid interaction and green efficiency.
数据集中处理的云计算模式在提供交互迅速和绿色高效的多样化应用服务方面面临着新的挑战.
In this paper, the cloud computing capability is extended to the edge devices,
and an edge cloud collaborative computing framework is proposed.
本文将云计算能力扩展到边缘设备，提出了一种边缘云协同计算框架；
A resource deployment algorithm based on task prediction (RDTP) is designed.
设计了一种基于任务预测的资源部署算法（RDTP）
The tasks are predicted by two-dimensional time series in cloud service center,
and the task resource deployment of edge server is optimized
by classification aggregation and delay threshold determination.
在云服务中心通过二维时间序列对任务进行预测，
结合分类聚合、延迟阈值判定等优化边缘服务器的任务资源部署；
A task scheduling algorithm based on Pareto improvement (TSPI) is proposed.
提出了一种基于帕累托优化的任务调度算法（TSPI），
At the edge servers, the Pareto progressive comparison is conducted in two stages
to obtain the tangent point or any intersection point of the two objective curves
of quality of user service and effect of system service to optimize task scheduling.
在边缘服务器上，分2个阶段进行帕累托渐进比较，以得到用户服务质量和系统服务效果2个目标曲线的相切点或任一相交点，从而优化任务调度.
The experimental results show that combining the resource deployment algorithm
based on task prediction and the task scheduling algorithm
based on Pareto improvement (RDTP-TSPI) increases the average user task hit rate.
实验结果表明：将基于任务预测的资源部署算法和基于帕累托优化的任务调度算法（RDTP-TSPI）相结合，可以提高平均用户任务命中率。
In addition, in the application scenarios of varying user task scales and
different Zipf distribution parameters α, the average service completion time of users,
the overall service effectiveness of system, and the total task delay rate of RDTP-TSPI
are better than the TSPI and BA (benchmark task scheduling algorithm based on FIFO).
此外，在不同用户任务规模和不同Zipf分布参数α的应用场景中，其用户平均服务完成时间、系统整体服务效应度和总任务延迟率，均优于基于帕累托优化的任务调度算法和基于FIFO(first input first output)的基准任务调度算法.
Key words: task scheduling, resource deployment, task prediction, collaborative computing, edge computing
任务调度，资源部署，任务预测，协同计算，边缘计算
