A Q-learning-based network content caching method

Abstract Cloud computing provides users with a distributed computing environment offering on-demand services. As its technologies become gradually mature and its application becomes more universal, cloud computing greatly reduces users’ costs while increasing working efficiency of enterprises and individuals (Futur Gener Comput Syst 25:599–616, 2009). Software as a service (SaaS), as a kind of information servicing model based on cloud platforms, is rising with the developments of Internet technologies and the maturing of application software. The responsibility of a SaaS server is to timely and accurately satisfy users’ needs for information. An intelligent and efficient content caching solution or method plays a vital role in that. This paper proposes a reinforcement learning (RL)-based content caching method named time-based Q Cacher (TQC) which effectively solves the problem of low hit ratio of server caching and ultimately achieves an intelligent, flexible, and highly adaptable content caching model.