Online Caching through Optimistic Online Mirror Descent

Bachelor Thesis (2022)
Authors

G.J. Admiraal (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Supervisors

George Iosifidis (TU Delft - Embedded Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright
© 2022 Gijs Admiraal
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Gijs Admiraal
Graduation Date
22-06-2022
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Abstract

The advent of wireless networks such as content distribution networks and edge computing networks calls for more effective online caching policies. Traditional policies lose performance since these new networks deal with highly non-stationary requests and frequent popularity shifts. Consequently, a new framework called Online Convex Optimization (OCO), which does not assume the request pattern, has recently been used to tackle the online caching problem. Besides, in many practical scenarios, a request prediction of unknown quality is available. This paper will leverage that and proposes a new online caching policy that uses these predictions. This policy will use the Optimistic Online Mirror Descent (OOMD) algorithm to solve the OCO problem. The policy will still obtain the same regret bound as its non-optimistic counterpart up to some constant even if the predictions are not accurate. The performance of the proposed policy is evaluated and compared with previous OCO-based policies with the use of trace-driven numerical tests.

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