A Q-learning-based network content caching method

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ID: 39260
2018
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Abstract
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.
Reference Key
chen2018aeurasip Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chen, Haijun;Tan, Guanzheng;
Journal eurasip journal on wireless communications and networking
Year 2018
DOI
DOI not found
URL
http://link.springer.com/article/10.1186/s13638-018-1268-1
Keywords Keywords not found

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