JEDERL:一种异构计算平台任务调度优化算法

JEDERL:A task scheduling optimization algorithm for heterogeneous computing platforms

随着图形处理单元、现场可编程门阵列等计算单元的快速发展,由不同类型计算资源组成的异构计算平台具有计算资源丰富、架构灵活多样、并行处理能力强等优点,在云计算、数据中心、物联网等领域中得到广泛的应用。针对异构计算平台任务调度中存在的计算资源异构及缺乏任务全局信息的问题,首先根据任务及资源的属性进行任务执行的抽象建模;然后利用图神经网络对任务和计算资源进行可伸缩的状态信息编码,从3个层次聚合任务及资源特征,解决了任务数量不确定、缺乏全局信息的问题;接着以最小化任务的平均完成时间为目标,基于深度确定性策略梯度算法设计任务调度算法。实验结果表明,JEDERL算法与随机调度、先进先出调度、短任务优先调度、轮盘法调度以及现有的强化学习调度算法相比,任务平均完成时间分别减少了约27.8%、12.6%、28.6%、21.9%、5.3%,并且在异构计算平台中服务器个数和任务数变化时表现稳定。

With the rapid development of GPU,FPGA,and other computing units,the heterogeneous computing platform is widely used in cloud computing,the data center,the Internet of things,and other fields because of its rich computing resources,flexible architecture,and strong parallel processing capability.Aiming at the task scheduling problem of heterogeneous computing resources and lack of global task information for heterogeneous computing platforms,the task execution model is carried out according to the attributes of tasks and computing resources.Then,we use graph neural networks to encode the scalable state information on tasks and computing resources,and the characteristic of tasks and computing resources are aggregated on three levels,which solves the problems of the uncertain number of tasks and lack of global information.To minimize the average task completion time,we design a task scheduling algorithm based on the Deep Deterministic Policy Gradient(DDPG).Experimental results show that compared with Random scheduling,First in First Out scheduling,Shortest Job First scheduling,Roulette scheduling,and existing reinforcement learning scheduling algorithm,the average task completion time of our algorithm(JEDERL,Job Embedding Device Embedding Reinforcement Learning)is reduced by 27.8%,12.6%,28.6%,21.9%,and 5.3%,respectively and that the proposed algorithm stays stable when the number of cluster servers and tasks changes.