A Q-learning-based network content caching method

• Published in: EURASIP journal on wireless communications and networking Vol. 2018; no. 1; pp. 1 - 10
• Main Authors: Chen, Haijun, Tan, Guanzheng
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 27.11.2018; Springer Nature B.V; SpringerOpen
• Subjects: Caching; Cloud computing; Communications Engineering; Computer networks; Computing costs; Distributed processing; Emerging Intelligent Algorithms for Edge-of-Things Computing; Engineering; Information Systems Applications (incl.Internet); Networks; Signal,Image and Speech Processing; Software services; Software-as-a-service; User satisfaction; SaaS Service; Caching Policy; Cache Contents; Cloud Computer; Cache Server
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-018-1268-1

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.