Cache Optimization Models and Algorithms
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Abstract (arXiv)
Storage resources and caching techniques permeate almost every area of communication networks today. In the near future, caching is set to play an important role in storage-assisted Internet architectures, information-centric networks, and wireless systems, reducing operating and capital expenditures and improving the services offered to users. In light of the remarkable data traffic growth and the increasing number of rich-media applications, the impact of caching is expected to become even more profound than it is today. Therefore, it is crucial to design these systems in an optimal fashion, ensuring the maximum possible performance and economic benefits from their deployment. To this end, this article presents a collection of detailed models and algorithms, which are synthesized to build a powerful analytical framework for caching optimization.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 0; 2024: 0; 2025: 1.
6 papers.
- 2021No-Regret Caching via Online Mirror Descentcited by 12
- 2022Optimistic No-regret Algorithms for Discrete Cachingcited by 10
- 2022Online Caching with Optimistic Learningcited by 7
- 2022Online Caching with no Regret: Optimistic Learning via Recommendationscited by 4
- 2022Universal Cachingcited by 2
- 2025Optimistic Learning for Communication Networkscited by 0
- 2025Optimistic Learning for Communication Networks4 Apr 2025
- 2022Optimistic No-regret Algorithms for Discrete Caching15 Aug 2022
- 2022Universal Caching10 May 2022
- 2022Online Caching with no Regret: Optimistic Learning via Recommendations20 Apr 2022
- 2022Online Caching with Optimistic Learning22 Feb 2022
- 2021No-Regret Caching via Online Mirror Descent29 Jan 2021