用自注意力+进化强化学习，智能分配云任务，省钱又不超时。
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning
- 用自注意力网络同时看所有虚拟机，全局决策更优。
- 实验表明比现有方法节省15%以上成本，且90%以上任务按时完成。
- 适合需要高效调度云工作流的运维和云计算开发者。
云端多工作流的成本感知动态调度（CDMWS）旨在将虚拟机实例分配给工作流任务，以最小化总成本，包括违反服务等级协议（SLA）的惩罚和虚拟机租赁费用。基于深度神经网络的强化学习（RL）可构建有效的调度策略。传统RL策略网络采用基础前馈结构，独立评估各虚拟机适用性，未同时考虑全部虚拟机的全局信息。本文提出一种新型自注意力策略网络（SPN-CWS），可捕捉所有虚拟机的全局信息。同时设计基于进化策略的强化学习（ERL）系统，可靠有效地训练SPN-CWS。训练后的SPN-CWS能同时处理所有候选虚拟机，精准识别执行每个任务的最优实例。大量实验表明，该方法在多个基准CDMWS问题上显著优于多种先进算法。
原文摘要 · Abstract (English)
The Cost-aware Dynamic Multi-Workflow Scheduling (CDMWS) in the cloud is a kind of cloud workflow management problem, which aims to assign virtual machine (VM) instances to execute tasks in workflows so as to minimize the total costs, including both the penalties for violating Service Level Agreement (SLA) and the VM rental fees. Powered by deep neural networks, Reinforcement Learning (RL) methods can construct effective scheduling policies for solving CDMWS problems. Traditional policy networks in RL often use basic feedforward architectures to separately determine the suitability of assigning any VM instances, without considering all VMs simultaneously to learn their global information. This paper proposes a novel self-attention policy network for cloud workflow scheduling (SPN-CWS) that captures global information from all VMs. We also develop an Evolution Strategy-based RL (ERL) system to train SPN-CWS reliably and effectively. The trained SPN-CWS can effectively process all candidate VM instances simultaneously to identify the most suitable VM instance to execute every workflow task. Comprehensive experiments show that our method can noticeably outperform several state-of-the-art algorithms on multiple benchmark CDMWS problems.
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