A Q-learning-based network content caching method

Haijun Chen,Guanzheng Tan

doi:10.1186/s13638-018-1268-1

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.

Highlights

Cloud computing is regarded as a revolution in enterprise application deployment and software configuration
Scalability, and cost-effectiveness, software as a service (SaaS) model has been increasingly adopted for distributing enterprise software systems, such as banking and e-commerce business software [2, 3]
The number of SaaS services is available in the markets such as Twitter, Gmail, Saleforce.com, and Google Maps to configure SaaS-based Web service systems

Summary

Introduction

Cloud computing is regarded as a revolution in enterprise application deployment and software configuration. The increases in the number and types of SaaS service requests put higher requirements on cache hit ratio, adaptability, and scalability, and bring severe challenges to first in first out (FIFO), least recently/frequently used (LRFU), and other traditional caching algorithms.

Results

Conclusion