Derakshani_FINAL VERSION.pdf (941.91 kB)
Contextual learning for content caching with unknown time-varying popularity profiles via incremental clustering
journal contribution
posted on 2021-02-15, 11:36 authored by Xingchi Liu, Mahsa DerakhshaniMahsa Derakhshani, Sangarapillai LambotharanSangarapillai LambotharanWith the rapid development of social networks and
high-quality video sharing services, the demand for delivering
large quantity and high quality contents under stringent endto-end delay requirement is increasing. To meet this demand,
we study the content caching problem modelled as a Markov
decision process in the network edge server when the popularity
profiles are unknown and time-varying. In order to adapt to the
changing trends of content popularity, a context-aware popularity
learning algorithm is proposed. We prove that the learning
error of this scheme is sublinear in the number of requests.
In light of the learned popularities, a reinforcement learningbased caching scheme is designed on top of the state-actionreward-state-action algorithm with a function approximation.
A reactive caching algorithm is also proposed to reduce the
complexity. The time complexities of both the caching schemes
are studied to demonstrate their feasibility in real time systems
and a theoretical analysis is performed to prove that the cache hit
rate of the reactive caching algorithm asymptotically converges to
the optimal cache hit rate. Finally the simulations are presented
to demonstrate the superiority of the proposed algorithms.
Funding
Royal Academy of Engineering under the Leverhulme Trust Research Fellowship scheme (DerakhshaniLTRF1920\16\67)
Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
Engineering and Physical Sciences Research Council
Find out more...History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
IEEE Transactions on CommunicationsVolume
69Issue
5Pages
3011 - 3024Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2021-02-03Publication date
2021-02-12Copyright date
2021ISSN
0090-6778eISSN
1558-0857Publisher version
Language
- en