  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.
