# Temporal Locality in Today's Content Caching: Why it Matters and How to Model it

arXiv: https://arxiv.org/abs/1305.7114 · HTML: https://mlsanity.com/paper/1305.7114/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2013.

## Built on by (most cited first, 13 of 13)

- [No-Regret Caching via Online Mirror Descent](https://mlsanity.com/paper/2101.12588/) (2021)
- [Online Caching with Optimal Switching Regret](https://mlsanity.com/paper/2101.07043/) (2021)
- [Optimistic No-regret Algorithms for Discrete Caching](https://mlsanity.com/paper/2208.06414/) (2022)
- [Online Caching with Optimistic Learning](https://mlsanity.com/paper/2202.10590/) (2022)
- [Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning](https://mlsanity.com/paper/2101.05885/) (2021)
- [Online Caching with no Regret: Optimistic Learning via Recommendations](https://mlsanity.com/paper/2204.09345/) (2022)
- [A Plan Reuse Mechanism for LLM-Driven Agent](https://mlsanity.com/paper/2512.21309/) (2025)
- [No-Regret Caching with Noisy Request Estimates](https://mlsanity.com/paper/2309.02055/) (2023)
- [Efficient and Optimal No-Regret Caching under Partial Observation](https://mlsanity.com/paper/2503.02758/) (2025)
- [A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks](https://mlsanity.com/paper/2105.05564/) (2021)
- [MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments](https://mlsanity.com/paper/2205.10642/) (2022)
- [Caching Contents with Varying Popularity using Restless Bandits](https://mlsanity.com/paper/2212.03291/) (2022)
- [No-Regret Mixing of LRU and LFU with Optimal Switching Cost](https://mlsanity.com/paper/2609.07566/) (2026)

## Abstract (arXiv)

The dimensioning of caching systems represents a difficult task in the design of infrastructures for content distribution in the current Internet. This paper addresses the problem of defining a realistic arrival process for the content requests generated by users, due its critical importance for both analytical and simulative evaluations of the performance of caching systems. First, with the aid of YouTube traces collected inside operational residential networks, we identify the characteristics of real traffic that need to be considered or can be safely neglected in order to accurately predict the performance of a cache. Second, we propose a new parsimonious traffic model, named the Shot Noise Model (SNM), that enables users to natively capture the dynamics of content popularity, whilst still being sufficiently simple to be employed effectively for both analytical and scalable simulative studies of caching systems. Finally, our results show that the SNM presents a much better solution to account for the temporal locality observed in real traffic compared to existing approaches.