As the quantity and complexity of information processed by software systems increase, large-scale software systems have an increasing requirement for high-performance distributed computing systems. With the acceleration of the Internet in Web 2.0, Cloud computing as a paradigm to provide dynamic, uncertain and elastic services has shown superiorities to meet the computing needs dynamically. Without an appropriate scheduling approach, extensive Cloud computing may cause high energy consumptions and high cost, in addition that high energy consumption will cause massive carbon dioxide emissions. Moreover, inappropriate scheduling will reduce the service life of physical devices as well as increase response time to users' request. Hence, efficient scheduling of resource or optimal allocation of request, that usually a NP-hard problem, is one of the prominent issues in emerging trends of Cloud computing. Focusing on improving quality of service (QoS), reducing cost and abating contamination, researchers have conducted extensive work on resource scheduling problems of Cloud computing over years. Nevertheless, growing complexity of Cloud computing, that the super-massive distributed system, is limiting the application of scheduling approaches. Machine learning, a utility method to tackle problems in complex scenes, is used to resolve the resource scheduling of Cloud computing as an innovative idea in recent years. Deep reinforcement learning (DRL), a combination of deep learning (DL) and reinforcement learning (RL), is one branch of the machine learning and has a considerable prospect in resource scheduling of Cloud computing. This paper surveys the methods of resource scheduling with focus on DRL-based scheduling approaches in Cloud computing, also reviews the application of DRL as well as discusses challenges and future directions of DRL in scheduling of Cloud computing.

翻译：随着软件系统处理信息的数量与复杂性不断增加，大规模软件系统对高性能分布式计算系统的需求日益增长。在Web 2.0时代互联网加速发展的背景下，云计算作为一种提供动态、不确定且弹性服务的范式，在动态满足计算需求方面展现出显著优势。若缺乏恰当的调度方法，大规模的云计算不仅可能导致高能耗与高成本，高能耗还会造成大量二氧化碳排放。此外，不合理的调度会缩短物理设备的使用寿命，同时增加用户请求的响应时间。因此，资源的高效调度或请求的最优分配——通常是一个NP难问题——已成为云计算新兴趋势中的关键议题之一。多年来，为提升服务质量（QoS）、降低成本并减少污染，研究者在云计算资源调度问题上开展了大量工作。然而，作为超大规模分布式系统的云计算日益增长的复杂性，正限制着传统调度方法的应用。机器学习作为处理复杂场景问题的有效工具，近年来作为一种创新思路被用于解决云计算资源调度问题。深度强化学习（DRL）是深度学习（DL）与强化学习（RL）的结合，属于机器学习的一个分支，在云计算资源调度中具有广阔的应用前景。本文系统综述了以DRL为核心的云计算资源调度方法，总结了DRL在该领域的应用现状，并探讨了DRL在云计算调度中面临的挑战与未来发展方向。